This paper describes the impedance characteristics of the human arm during
passive movement. The arm was moved in the desired trajectory. The
motion was actuated by a 1-degree-of-freedom robot system. Trajectories
used in the experiment were minimum jerk (the rate of change of
acceleration) trajectories, which were found during a human and human
cooperative task and optimum for muscle movement. As the muscle is
mechanically analogous to a spring-damper system, a second-order equation
was considered as the model for arm dynamics. In the model, inertia,
stiffness, and damping factor were considered. The impedance parameters
were estimated from the position and torque data obtained from the
experiment and based on the “Estimation of Parametric Model”. It was
found that the inertia is almost constant over the operational time. The
damping factor and stiffness were high at the starting position and became
near zero after 0.4 seconds.
Prediction the Biodynamic Response of Seated Human Body to Vibration Using A...MOSTAFAABDEEN1970
The biodynamic response behaviors of seated human body subject to wholebody
vibration have been widely investigated. The biodynamic response
characteristics of seated human subjects have been extensively reported in
terms of apparent mass and driving-point mechanical impedance while seat-tohead
vibration transmissibility has been widely used to characterize response
behavior of the seated subjects exposed to vibration. These functions (apparent
mass, driving-point mechanical impedance) describe “to-the-body” force–motion
relationship at the human–seat interface, while the transmissibility function
describes “through-the-body” vibration transmission properties. The current study
proposed a 4-DOF analytic biomechanical model of the human body in a sitting
posture without backrest in vertical vibration direction to investigate the
biodynamic responses of different masses and stiffness. Following the analytical
approach, numerical technique developed in the present paper to facilitate and
rapid the analysis. The numerical analysis used here applies one of the artificial
intelligence technique to simulate and predict the response behaviors of seated
human body for different masses and stiffness without the need to go through the
analytic solution every time. The Artificial Neural Network (ANN) technique is
introduced in the current study to predict the response behaviors for different
masses and stiffness rather than those used in the analytic solution. The results
of the numerical study showed that the ANN method with less effort was very
efficiently capable of simulating and predicting the response behaviors of seated
human body subject to whole-body vibration.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Non linear 3 d finite element analysis of the femur boneeSAT Journals
Abstract In this paper a 3D stress analysis on the human femur is carried out with a view of understanding the stress and strain distributions coming into picture during normal day to day activities of a normal human being. This work was based on the third generation standard femur CAD model being provided by Rizzoli Orthopedic Institute. By locating salient geometric features on the CAD model with the VHP (Visible Human Project) femur model, material properties at four crucial locations were calculated and assigned to the current model and carried out a nonlinear analysis using a general purpose finite element software ABAQUS. Simulation of Marten’s study revealed that the highest stress formed in the absence of the cancellous tissue is almost double the value of stress formed with cancellous tissue. A comparative study was made with the Lotz’s model by taking into consideration two different sections near the head and neck of the femur. An exhaustive number of finite element analyses were carried out on the femur model, to simulate the actual scenario. Index Terms: Fracture, Cortex, Cancellous, femur bone, finite element
Dynamic Model of Hip and Ankle Joints Loading during Working with a Motorized...J. Agricultural Machinery
The main objective of this paper is to develop a seven-link dynamic model of the operator’s body while working with a motorized backpack sprayer. This model includes the coordinates of the sprayer relative to the body, the rotational inertia of the sprayer, the muscle moments acting on the joints, and a kinematic coupling that keeps the body balanced between the two legs. The constraint functions were determined and the non-linear differential equations of motion were derived using Lagrangian equations. The results show that undesirable fluctuations in the ankle force are noticeable at the beginning and end of a swing phase. Therefore, injuries to the ankle joint are more likely due to vibrations. The effects of engine speed and sprayer mass on the hip and ankle joint forces were then investigated. It is found that the engine speed and sprayer mass have significant effects on the hip and ankle forces and can be used as effective control parameters. The results of the analysis also show that increasing the engine speed increases the frequency of the hip joint force. However, no significant effects on the frequency of the ankle joint force are observed. The results of this study may provide researchers with insight into estimating the allowable working hours with the motorized backpack sprayers, prosthesis design, and load calculations of hip implants in the future.
Prediction the Biodynamic Response of Seated Human Body to Vibration Using A...MOSTAFAABDEEN1970
The biodynamic response behaviors of seated human body subject to wholebody
vibration have been widely investigated. The biodynamic response
characteristics of seated human subjects have been extensively reported in
terms of apparent mass and driving-point mechanical impedance while seat-tohead
vibration transmissibility has been widely used to characterize response
behavior of the seated subjects exposed to vibration. These functions (apparent
mass, driving-point mechanical impedance) describe “to-the-body” force–motion
relationship at the human–seat interface, while the transmissibility function
describes “through-the-body” vibration transmission properties. The current study
proposed a 4-DOF analytic biomechanical model of the human body in a sitting
posture without backrest in vertical vibration direction to investigate the
biodynamic responses of different masses and stiffness. Following the analytical
approach, numerical technique developed in the present paper to facilitate and
rapid the analysis. The numerical analysis used here applies one of the artificial
intelligence technique to simulate and predict the response behaviors of seated
human body for different masses and stiffness without the need to go through the
analytic solution every time. The Artificial Neural Network (ANN) technique is
introduced in the current study to predict the response behaviors for different
masses and stiffness rather than those used in the analytic solution. The results
of the numerical study showed that the ANN method with less effort was very
efficiently capable of simulating and predicting the response behaviors of seated
human body subject to whole-body vibration.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Non linear 3 d finite element analysis of the femur boneeSAT Journals
Abstract In this paper a 3D stress analysis on the human femur is carried out with a view of understanding the stress and strain distributions coming into picture during normal day to day activities of a normal human being. This work was based on the third generation standard femur CAD model being provided by Rizzoli Orthopedic Institute. By locating salient geometric features on the CAD model with the VHP (Visible Human Project) femur model, material properties at four crucial locations were calculated and assigned to the current model and carried out a nonlinear analysis using a general purpose finite element software ABAQUS. Simulation of Marten’s study revealed that the highest stress formed in the absence of the cancellous tissue is almost double the value of stress formed with cancellous tissue. A comparative study was made with the Lotz’s model by taking into consideration two different sections near the head and neck of the femur. An exhaustive number of finite element analyses were carried out on the femur model, to simulate the actual scenario. Index Terms: Fracture, Cortex, Cancellous, femur bone, finite element
Dynamic Model of Hip and Ankle Joints Loading during Working with a Motorized...J. Agricultural Machinery
The main objective of this paper is to develop a seven-link dynamic model of the operator’s body while working with a motorized backpack sprayer. This model includes the coordinates of the sprayer relative to the body, the rotational inertia of the sprayer, the muscle moments acting on the joints, and a kinematic coupling that keeps the body balanced between the two legs. The constraint functions were determined and the non-linear differential equations of motion were derived using Lagrangian equations. The results show that undesirable fluctuations in the ankle force are noticeable at the beginning and end of a swing phase. Therefore, injuries to the ankle joint are more likely due to vibrations. The effects of engine speed and sprayer mass on the hip and ankle joint forces were then investigated. It is found that the engine speed and sprayer mass have significant effects on the hip and ankle forces and can be used as effective control parameters. The results of the analysis also show that increasing the engine speed increases the frequency of the hip joint force. However, no significant effects on the frequency of the ankle joint force are observed. The results of this study may provide researchers with insight into estimating the allowable working hours with the motorized backpack sprayers, prosthesis design, and load calculations of hip implants in the future.
UPPER EXTREMITY ROBOTICS EXOSKELETON: APPLICATION, STRUCTURE AND ACTUATIONijbesjournal
Robotic exoskeleton is getting important to human in many aspects such as power assist, muscle training, regain motor function and rehabilitation. The research and development towards these functions are expected to be combined and integrated with the human intelligent and machine power, eventually becoming another generation of robot which will enhance the machine intelligent and human power. This paper reviews the upper extremity exoskeleton with different functions, actuators and degree of freedom (DOF). Among the functions, rehabilitation and power assist have been highlighted while pneumatic actuator, pneumatic muscle, motor and hydraulic actuator are presented under the categories of actuator. In addition, the structure of exoskeleton is separated by its DOF in terms of shoulder, elbow, wrist and hand
UPPER EXTREMITY ROBOTICS EXOSKELETON: APPLICATION, STRUCTURE AND ACTUATIONijbesjournal
Robotic exoskeleton is getting important to human in many aspects such as power assist, muscle training, regain motor function and rehabilitation. The research and development towards these functions are expected to be combined and integrated with the human intelligent and machine power, eventually becoming another generation of robot which will enhance the machine intelligent and human power. This paper reviews the upper extremity exoskeleton with different functions, actuators and degree of freedom (DOF). Among the functions, rehabilitation and power assist have been highlighted while pneumatic actuator, pneumatic muscle, motor and hydraulic actuator are presented under the categories of actuator. In addition, the structure of exoskeleton is separated by its DOF in terms of shoulder, elbow, wrist and hand.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A Study on 3D Finite Element Analysis of Anterior Cruciate Ligament Behavior ...ijsrd.com
The present study deals with the force and stress distribution within the anteromedial (AM) and posterolateral (PL) bundles of the anterior cruciate ligament (ACL) in response to an anterior tibial load with the knee at full extension was calculated using a validated three dimensional finite element model (FEM) of a human ACL. The interaction between the AM and PL bundles, as well as the contact and friction caused by the ACL wrapping around the bone during knee motion, were included in the model. The AM and PL bundles of the ACL were simulated as incompressible homogeneous and isotropic hyperelastic materials. The validated FEM was then used to calculate the force and stress distribution within the ACL under an anterior tibial load at full extension. The AM and PL bundles shared the force, and the stress distribution was non-uniform within both bundles with the highest stress localized near the femoral insertion site. The contact and friction caused by the ACL wrapping around the bone during knee motion played the role of transferring the force from the ACL to the bone, and had a direct effect on the force and stress distribution of the ACL. This validated model will enable the analysis of force and stress distribution in the ACL in response to more complex loading conditions and has the potential to help design improved surgical procedures following ACL injuries.
range of motion for each joint is important for performing the activity by humans. it is also important to understand the flexibility and limitations of the human body for designing anything. be it space or furniture. Thus its knowledge is very important for a designer.
A new look at the ball disteli diagram and its relevance to knee proprioceptionWangdo Kim
Reconstruction of a torn anterior cruciate ligament (ACL) cannot be successful without a properly placed tibial tunnel. Graft impingement occurs when the graft becomes trapped in the notch between the rounded ends of the femur (intercondylar notch) with the knee in extension. A surgical technique for customizing the placement of the tibial tunnel, preventing roof impingement, is presented.
We consider the knee as a perceptual system, the units of anatomy in which are not the units of function. We are particularly interested in measuring of our knee proprioception, and of our ability to perceive change in our position through locating the instantaneous axes of the knee (IAK) during locomotion. This geometrical “patterns” of the IAK has been shown to be essential in the rehabilitation of both ACL reconstruction and the disorders in gait-related behaviors.
We present conditions of nonimpingement graft based on the principle that the tibial tunnel can be reproducibly placed in a manner that the force on each ACL graft is therefore in involution with original system.
Mechanics of the human hamstring muscles during sprintingFernando Farias
As peak musculotendon
force and strain for BF
LH
, ST, and SM occurred around the same time during terminal swing, it is suggested that this period in the
stride cycle may be when the biarticular hamstrings are at greatest injury risk. On this basis, hamstring injury prevention or rehabilitation
programs should preferentially target strengthening exercises that involve eccentric contractions performed with high loads at longer
musculotendon lengths.
Prediction the Biodynamic Response of the Seated Human Body using Artificial ...CSCJournals
The biodynamic response behaviors of seated human body subject to whole-body vibration have been widely investigated. The biodynamic response characteristics of seated human subjects have been extensively reported in terms of apparent mass and driving-point mechanical impedance while seat-to-head vibration transmissibility has been widely used to characterize response behavior of the seated subjects exposed to vibration. These functions (apparent mass, driving-point mechanical impedance) describe “to-the-body” force–motion relationship at the human–seat interface, while the transmissibility function describes “through-the-body” vibration transmission properties. The current study proposed a 4-DOF analytic biomechanical model of the human body in a sitting posture without backrest in vertical vibration direction to investigate the biodynamic responses of different masses and stiffness. Following the analytical approach, numerical technique developed in the present paper to facilitate and rapid the analysis. The numerical analysis used here applies one of the artificial intelligence technique to simulate and predict the response behaviors of seated human body for different masses and stiffness without the need to go through the analytic solution every time. The Artificial Neural Network (ANN) technique is introduced in the current study to predict the response behaviors for different masses and stiffness rather than those used in the analytic solution. The results of the numerical study showed that the ANN method with less effort was very efficiently capable of simulating and predicting the response behaviors of seated human body subject to whole-body vibration.
MINIMIZATION OF METABOLIC COST OF MUSCLES BASED ON HUMAN EXOSKELETON MODELING...ijbesjournal
In this work, movement of the exoskeleton wearer and the metabolic energy changes with the assisted
devices using OpenSim platform has been attempted. Two musculoskeletal models, one with torsional ankle
spring and the other with bi-articular path spring are subjected to forward dynamic simulation.The
changes in the metabolic rate of the lower extremity muscles before and after the addition of the assistive
devices were tested. The results about the effect of these external devices on individual muscles of the lower
muscle group were analysed which provided effective results.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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UPPER EXTREMITY ROBOTICS EXOSKELETON: APPLICATION, STRUCTURE AND ACTUATIONijbesjournal
Robotic exoskeleton is getting important to human in many aspects such as power assist, muscle training, regain motor function and rehabilitation. The research and development towards these functions are expected to be combined and integrated with the human intelligent and machine power, eventually becoming another generation of robot which will enhance the machine intelligent and human power. This paper reviews the upper extremity exoskeleton with different functions, actuators and degree of freedom (DOF). Among the functions, rehabilitation and power assist have been highlighted while pneumatic actuator, pneumatic muscle, motor and hydraulic actuator are presented under the categories of actuator. In addition, the structure of exoskeleton is separated by its DOF in terms of shoulder, elbow, wrist and hand
UPPER EXTREMITY ROBOTICS EXOSKELETON: APPLICATION, STRUCTURE AND ACTUATIONijbesjournal
Robotic exoskeleton is getting important to human in many aspects such as power assist, muscle training, regain motor function and rehabilitation. The research and development towards these functions are expected to be combined and integrated with the human intelligent and machine power, eventually becoming another generation of robot which will enhance the machine intelligent and human power. This paper reviews the upper extremity exoskeleton with different functions, actuators and degree of freedom (DOF). Among the functions, rehabilitation and power assist have been highlighted while pneumatic actuator, pneumatic muscle, motor and hydraulic actuator are presented under the categories of actuator. In addition, the structure of exoskeleton is separated by its DOF in terms of shoulder, elbow, wrist and hand.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A Study on 3D Finite Element Analysis of Anterior Cruciate Ligament Behavior ...ijsrd.com
The present study deals with the force and stress distribution within the anteromedial (AM) and posterolateral (PL) bundles of the anterior cruciate ligament (ACL) in response to an anterior tibial load with the knee at full extension was calculated using a validated three dimensional finite element model (FEM) of a human ACL. The interaction between the AM and PL bundles, as well as the contact and friction caused by the ACL wrapping around the bone during knee motion, were included in the model. The AM and PL bundles of the ACL were simulated as incompressible homogeneous and isotropic hyperelastic materials. The validated FEM was then used to calculate the force and stress distribution within the ACL under an anterior tibial load at full extension. The AM and PL bundles shared the force, and the stress distribution was non-uniform within both bundles with the highest stress localized near the femoral insertion site. The contact and friction caused by the ACL wrapping around the bone during knee motion played the role of transferring the force from the ACL to the bone, and had a direct effect on the force and stress distribution of the ACL. This validated model will enable the analysis of force and stress distribution in the ACL in response to more complex loading conditions and has the potential to help design improved surgical procedures following ACL injuries.
range of motion for each joint is important for performing the activity by humans. it is also important to understand the flexibility and limitations of the human body for designing anything. be it space or furniture. Thus its knowledge is very important for a designer.
A new look at the ball disteli diagram and its relevance to knee proprioceptionWangdo Kim
Reconstruction of a torn anterior cruciate ligament (ACL) cannot be successful without a properly placed tibial tunnel. Graft impingement occurs when the graft becomes trapped in the notch between the rounded ends of the femur (intercondylar notch) with the knee in extension. A surgical technique for customizing the placement of the tibial tunnel, preventing roof impingement, is presented.
We consider the knee as a perceptual system, the units of anatomy in which are not the units of function. We are particularly interested in measuring of our knee proprioception, and of our ability to perceive change in our position through locating the instantaneous axes of the knee (IAK) during locomotion. This geometrical “patterns” of the IAK has been shown to be essential in the rehabilitation of both ACL reconstruction and the disorders in gait-related behaviors.
We present conditions of nonimpingement graft based on the principle that the tibial tunnel can be reproducibly placed in a manner that the force on each ACL graft is therefore in involution with original system.
Mechanics of the human hamstring muscles during sprintingFernando Farias
As peak musculotendon
force and strain for BF
LH
, ST, and SM occurred around the same time during terminal swing, it is suggested that this period in the
stride cycle may be when the biarticular hamstrings are at greatest injury risk. On this basis, hamstring injury prevention or rehabilitation
programs should preferentially target strengthening exercises that involve eccentric contractions performed with high loads at longer
musculotendon lengths.
Prediction the Biodynamic Response of the Seated Human Body using Artificial ...CSCJournals
The biodynamic response behaviors of seated human body subject to whole-body vibration have been widely investigated. The biodynamic response characteristics of seated human subjects have been extensively reported in terms of apparent mass and driving-point mechanical impedance while seat-to-head vibration transmissibility has been widely used to characterize response behavior of the seated subjects exposed to vibration. These functions (apparent mass, driving-point mechanical impedance) describe “to-the-body” force–motion relationship at the human–seat interface, while the transmissibility function describes “through-the-body” vibration transmission properties. The current study proposed a 4-DOF analytic biomechanical model of the human body in a sitting posture without backrest in vertical vibration direction to investigate the biodynamic responses of different masses and stiffness. Following the analytical approach, numerical technique developed in the present paper to facilitate and rapid the analysis. The numerical analysis used here applies one of the artificial intelligence technique to simulate and predict the response behaviors of seated human body for different masses and stiffness without the need to go through the analytic solution every time. The Artificial Neural Network (ANN) technique is introduced in the current study to predict the response behaviors for different masses and stiffness rather than those used in the analytic solution. The results of the numerical study showed that the ANN method with less effort was very efficiently capable of simulating and predicting the response behaviors of seated human body subject to whole-body vibration.
MINIMIZATION OF METABOLIC COST OF MUSCLES BASED ON HUMAN EXOSKELETON MODELING...ijbesjournal
In this work, movement of the exoskeleton wearer and the metabolic energy changes with the assisted
devices using OpenSim platform has been attempted. Two musculoskeletal models, one with torsional ankle
spring and the other with bi-articular path spring are subjected to forward dynamic simulation.The
changes in the metabolic rate of the lower extremity muscles before and after the addition of the assistive
devices were tested. The results about the effect of these external devices on individual muscles of the lower
muscle group were analysed which provided effective results.
Similar to Impedance characteristic of the human arm during passive movements (20)
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
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Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
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Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
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Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
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Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
20240605 QFM017 Machine Intelligence Reading List May 2024
Impedance characteristic of the human arm during passive movements
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 1, March 2023, pp. 34~40
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i1.pp34-40 34
Journal homepage: http://ijai.iaescore.com
Impedance characteristic of the human arm during passive
movements
Md. Mozasser Rahman1
, Ryojun Ikeura2
1
Department of Mechanical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Muar,
Johor, Malaysia
2
Department of Mechanical Engineering, Faculty of Engineering, Mie University, Kamihama-1515, Tsu, Mie, Japan
Article Info ABSTRACT
Article history:
Received Sep 1, 2021
Revised Jul 1, 2022
Accepted Jul 30, 2022
This paper describes the impedance characteristics of the human arm during
passive movement. The arm was moved in the desired trajectory. The
motion was actuated by a 1-degree-of-freedom robot system. Trajectories
used in the experiment were minimum jerk (the rate of change of
acceleration) trajectories, which were found during a human and human
cooperative task and optimum for muscle movement. As the muscle is
mechanically analogous to a spring-damper system, a second-order equation
was considered as the model for arm dynamics. In the model, inertia,
stiffness, and damping factor were considered. The impedance parameters
were estimated from the position and torque data obtained from the
experiment and based on the “Estimation of Parametric Model”. It was
found that the inertia is almost constant over the operational time. The
damping factor and stiffness were high at the starting position and became
near zero after 0.4 seconds.
Keywords:
Human arm
Human-robot cooperation
Impedance characteristics
Minimum jerk trajectory
Passive movements
Single degree-of-freedom
This is an open access article under the CC BY-SA license.
Corresponding Author:
Md. Mozasser Rahman
Department of Mechanical Engineering Technology, Faculty of Engineering Technology
Universiti Tun Hussein Onn Malaysia
Km 1, Panchor Street, 84600 Pagoh, Muar, Johor Darul Ta’zim, Malaysia
Email: mozasser@uthm.edu.my
1. INTRODUCTION
The human being is the best creature in this universe. In comparison to other creatures, the size and
shape of the human body are most favorable in all respect and every movement of the human body is smooth
and perfect. In the past, scientists have tried to build a mechanism that imitates parts of the human body to
perform the task for mankind. The development of robots during the latter half of the twentieth century is its
burning example.
Among the moving parts of the human body, the upper limbs are used most frequently. The main
function of the upper limb is grasping and manipulating. This is also used as a walking aid to support the
body during gait. The upper limb consists of three main parts, the upper arm, forearm, and hand. It is
composed of three chain mechanisms, the shoulder girdle, the elbow, and the wrist, whose association allows
a wide range of combined motion. Due to the complexity of the hand mechanism, the wrist was not studied,
and the hand was taken as another rigid segment in the extension of the forearm. The movements of the
human arm can be divided into three major types: i) active movement, an external force is exerted by the
hand; ii) reaching movement, without exerting any external force; and iii) passive movement, a hand is
moved by external force [1].
The arm is a multi-joint redundant manipulator. It is found that the normalized speed and velocity
profiles for single and multiple joint trajectories are identical [2]. Shoulder velocity profiles remain
2. Int J Artif Intell ISSN: 2252-8938
Impedance characteristic of the human arm during passive movements (Md. Mozasser Rahman)
35
unchanged, but the acceleration phase of elbow trajectories is adjusted so that peak velocity and movement
time match that of the shoulder joint. They argue that hand-path is the primary movement criterion, and that
elbow movement is subordinate to elbow movement in a hierarchical scheme to reduce the available degrees
of freedom.
Cruse and Brüwer studied planar reaching movements while recording shoulder, elbow, and wrist
angles to determine how subjects solved the redundant degrees of freedom problem [3]. They propose that
each limb has an almost comfortable position and that by associating a cost to deviations from this position,
the posture adopted to reach a point in space minimizes this cost [4], [5]. Movements are executed as a
compromise between simultaneous, smooth interpolation of joint angles, minimizing discomfort, and straight
hand paths. Rossetti et al. studied variability in pointing movements and found that errors increased at
extreme joint positions [6]. They also concluded that configurations are chosen to minimize the sum of
discomfort at the participating joints.
The impedance characteristics of the arm must be affected by kinematic properties of the human
arm, motor control signals from the central nervous system (CNS), individual properties of each muscle, and
proprioceptive feedback via the muscle spindle and Golgi tendon organ. For multi-joint hand movements, the
hand stiffness and viscosity can be predicted with sufficient accuracy under the assumptions that the length of
the muscle moment arm and the muscle viscoelasticity can be approximated by polynomial models of the
joint angles [7]. Flash and Mussa-Ivaldi examined to what extent the kinematic properties of the human arm
can explain its spatial variations and found that the anatomical factors are not sufficient to account for the
observations [8].
Several studies have been made for single-joint and two-joint arm movements, where one human
moved or/and regulated a task. Dowben has shown that the viscoelastic properties of skeletal muscles, which
are the major source of human hand viscoelasticity, largely change depending on their activation level [9].
It has been also shown that the change of viscoelastic coefficients depends on the activation level of muscle
[10], task instruction of the subjects [11], joint angles [12], and speed of the arm movement and loading [13].
Tsuji et al. pointed out that muscle contraction for a grip force increases stiffness and viscosity of the hand
[14]. Also, Gomi et al. estimated hand stiffness during two-joint arm movements and argued that dynamic
stiffness differs from static one because of the neuromuscular activity during movements [15], [16]. Tsuji
analyzed the spatial characteristics of the human hand impedance with considering of effect of arm posture
and muscle activity [7]. Gomi and Osu again showed that the stiffness and viscoelasticity of human multi-
joint arm change under different contraction conditions during posture maintenance tasks and during force
regulation tasks [17].
All the studies described are related to active and reaching movements. But it has been pointed out
that passive movement is important for the cooperative task [18] and a variable structure of impedance
characteristics is regulated by a motor command from the CNS. No attempts have been made to find out the
characteristics of the human arm in passive movement and the time-variant nature of the impedance
characteristic of the human arm.
An investigation has already been made into the impedance characteristics of the human arm's
passive movements (the arm is moved by an external force) in the forward and backward direction while the
forearm was in the horizontal position. Both the upper arm and forearm were in the same vertical plane. The
elbow and the shoulder joint were assumed to have a constant center of rotation. The forearm was treated as a
rigid body. In that investigation, mass, stiffness, and damping factor for the variable impedance model had
been considered. It was found that the stiffness and the damping factor varied with the operational time [18].
In the present investigation, one degree-of-freedom rotational passive movements of the forearm
around the elbow were considered. Both the upper arm and forearm were in the same horizontal plane.
As only two muscles, biceps brachii and triceps brachii, are used in this rotational operation, the mechanics
of the muscles and bones are simple and it is easier to analyze the characteristics of the musculoskeletal
system [19]. To learn more about human motor adaptation, works have investigated the adaptation to stable
[20]–[22] and unstable [23]–[25] interactions produced by a haptic interface.
In a cooperation task performed by two humans, one human control the position of the carried
object and the other human follows the motion of that object. The former can designate as a leader and the
latter as a follower. The characteristics of the follower can be applied to the control method of a cooperative
robot. Moreover, if the target trajectory controlled by the leader is known, then the characteristics of the
follower can be investigated easily as a simple spring-mass-damper system [1]. The arm of the human is
moved along the target position trajectory and the force exerted by the arm is measured. From the data of the
target position trajectory and force, the impedance characteristics of the human arm can be estimated.
The time trajectory of position and velocity found during the experiment of cooperation between
two humans is similar to the minimum jerk motion proposed by Flash and Hogan [26] and Ikeura and
Mizutani [27]. They found that the human arm moves to minimize (1) and (2).
3. ISSN: 2252-8938
Int J Artif Intell, Vol. 12, No. 1, March 2023: 34-40
36
𝐽 = ∫ (
𝑑3𝜃
𝑑𝑡3)
2
𝑑𝑡
𝑡𝑓
0
(1)
where tf is the time duration of motion. The trajectory was derived by minimizing the function J as:
𝜃(𝑡) = 𝜃(0) + 𝑎{10(𝑡/𝑑)3
− 15(𝑡/𝑑)4
+ 6(𝑡/𝑑)5
} (2)
where a is movement amplitude, 𝜃(0) is the position at time t0 and d is the duration. The position and
velocity for movement of 600
are shown in Figure 1.
Figure 1. Time trajectory of position and velocity
The minimum jerk trajectory represents the free arm motion. Ikeura et al. found the minimum jerk
trajectory in the cooperative motion of two humans [28]. This means that the tracking of the motion of the
follower's arm is along the minimum jerk trajectory. In the cooperation between a human and a robot, the
robot should follow the motion of the human so that the human can move his/her arm along the minimum
jerk trajectory.
2. METHOD
2.1. Experimental set-up
Figure 2 illustrates an experimental system, in which a servo motor was used as the actuator. The
servo motor was fixed into a frame vertically upward. One end of a splint (508 cm thin aluminum plate) was
attached to the shaft of the motor. The arm was rotated along with the splint. A sensor located in between the
arm and the splint was used to measure the torque needed to move the arm.
The output of the torque sensor was sent to the personal computer (PC) through the digital signal
processing (DSP) board. An encoder was used to measure the angular position and the data was passed to the
computer through the counter board. All boards were implemented on an industry standard architecture (ISA)
bus of the PC.
2.2. Experimental procedure
The subjects are three right-handed male post-graduate university students (30-35 years old) with no
previous history of neuropathies or trauma to the upper limbs. The subjects were given sufficient information
about the experiment and then taken their consent to participate. In the experiment we defined a leader and a
follower, the leader controls the position of the object and the follower tracks the motion of the object. Here,
the robot was considered the leader and moved the splinter in the clockwise/anticlockwise directions. The
leader controls the position, so the role of the robot was the same as a human leader at that time. The reason
for choosing the robot as the leader was to move the arm at defined operating conditions.
As shown in Figure 2, a subject who followed the movement of the linear motor was seated beside
the setup. The shoulder of the subject was restrained to the chair back and the elbow of the right arm was
supported in the horizontal plane by a belt attached to the ceiling. He placed his arm on the splint so that the
wrist was fitted into the torque sensor attached to the splint. Then the torque sensor was adjusted so that the
elbow was positioned just above the center of rotation of the splint. A gap was maintained between the arm
and splint as shown in Figure 3.
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Impedance characteristic of the human arm during passive movements (Md. Mozasser Rahman)
37
Figure 2. Experimental set-up Figure 3. Servo-motor and splint
Figure 4 shows the control system of the experimental setup. Position trajectories used in the
experiments were minimum jerk trajectories. The velocity trajectory was the first-order differentiation of the
position trajectory. Movement amplitudes were 400
-700
and the duration of the movements was from 0.6 to
1.2 seconds, with an increment of 0.2 seconds. The sampling time for the position control of the servo motor
was 5 ms. The selection of position trajectory was done randomly so that the subject could not imagine the
direction of rotation.
2.3. Data analysis
As the muscle is mechanically analogous to a spring-damper system, as shown in Figure 5, a simple
second-order equation was used as the model for the arm dynamics. In the model, mass, damping factor, and
stiffness were considered.
𝐼𝑚𝜃̈ + 𝑐𝑚𝜃̇ + 𝑘𝑚𝜃 = 𝜏 (3)
where Im, cm, and km are the impedance parameters for inertia, damping factor, and stiffness and 𝜏 is the
torque to rotate the arm.
Figure 4. Block diagram of control system Figure 5. Impedance model of the human arm
For the estimation of the impedance parameters, the system identification toolbox of MATLAB (The Math
Works, Inc.) was used [29]. For calculations, auto regressive exogenous (ARX) model was used. To make
similarity with the ARX model, position 𝜃(𝑡) as an input and torque 𝜏(𝑡) as output was considered. If T is
the sampling time then, 𝜃̇(𝑡) =
𝜃(𝑡)−𝜃(𝑡−1)
𝑇
and 𝜃̈(𝑡) =
𝜃̇ (𝑡)−𝜃̇ (𝑡−1)
𝑇
. By using these values in (3), obtained is
(4).
Computer
Minimum Jerk Trajectory
Encoder
Servo Motor
OP Amp
D/A
Kp
+
-
=
Biceps Brachii
Triceps Brachii
k
c
k
c
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Int J Artif Intell, Vol. 12, No. 1, March 2023: 34-40
38
𝜏(𝑡) = 𝑎1𝜃(𝑡) + 𝑎2𝜃(𝑡 − 1) + 𝑎3𝜃(𝑡 − 2) (4)
where, 𝑎1 =
𝐼+𝑐𝑇+𝑘𝑇2
𝑇2 , 𝑎2 =
−(2𝐼+𝑐𝑇)
𝑇2 and 𝑎3 =
𝐼
𝑇2. In (4) is a form of the ARX model. Coefficients a1, a2, and
a3 were estimated by using the different variants of the recursive least-squares method. Then, the impedance
parameters I, c and k were calculated.
3. RESULT
Figure 6 shows a typical time trajectory of position and torque measured during the experiments.
This data was used for calculating the impedance parameters. Fifty-four replications were observed for the
calculation of impedance parameters at different angles and speeds of movement. The angle of movement
varied from 40 degrees to 70 degrees and the duration of movement varied from 0.6 seconds to 1.2 seconds
with an interval of 0.2 seconds. Calculated impedance parameters of two operations are shown in Figure 7.
Figure 7(a) represents the impedance parameter for the movement of 40 degrees in 0.6 seconds. A sample of
impedance parameters for the movement of 70 degrees in 1 second is shown in Figure 7(b).
Figure 6. Time trajectories of the position and the torque
(a) (b)
Figure 7. Impedance parameters (a) a=400
and d=0.6 seconds and (b) a=700
and 1.0 second
4. DISCUSSION
In the present paper, the impedance of the human arm including inertia, stiffness, and damping
factor was estimated for a single joint while it was moved by a robot. Figure 7 shows that the inertia is almost
constant and the damping factor is high at the starting position and is near zero at 0.4 seconds. Stiffness has
6. Int J Artif Intell ISSN: 2252-8938
Impedance characteristic of the human arm during passive movements (Md. Mozasser Rahman)
39
also a similar characteristic to the damping factor. Similar results were found for the multi-joint arm
movements with a higher viscous effect [1]. For a faster movement (Figure 7(a), a=400
and d=0.6 second) the
parameters come to zero earlier (about 4% of total operation time). But in the case of other movements, the
parameters come to zero at 0.4 seconds. Even for a very slow movement (a=400
and d=1.2 seconds)
parameters come to zero at 0.4 seconds. Therefore, it is proved that the impedance characteristics of a human
arm in passive movements do not depend upon the speed of movement or movement amplitude.
5. CONCLUSIONS
Impedance characteristics of the human arm during passive movement were analyzed. It is found
that the impedance characteristics of a human arm for passive movements, maintain a model, which does not
depend upon the speed and the movement amplitude, the inertia was constant, and the stiffness and damping
factor varied from high to low within 0.4 seconds. Several subjects were used, and similar results were found.
ACKNOWLEDGEMENTS
Communication of this research is made possible through monetary assistance by Universiti Tun
Hussein Onn Malaysia and the UTHM Publisher’s Office via Publication Fund E15216.
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BIOGRAPHIES OF AUTHORS
Md. Mozasser Rahman currently is an Associate Professor in the Department of
Mechanical Engineering Technology, Universiti Tun Hussein Onn Malaysia (UTHM). Dr.
Mozasser received a B. Sc. Eng. degree from Bangladesh Institute of Technology (BIT)
Khulna in Mechanical Engineering in 1988. After graduation, he worked for the same institute
as a lecturer. He got practical knowledge and experience in industrial maintenance and
automation. He was later conferred an M. Eng. degree and Ph. D. from Mie University, Japan,
in 2000 and 2003 respectively. Dr. Mozasser has expertise in robotics and industrial
automation. His research area covers human-robot cooperation, movement characteristics of
the human arm, and artificial human organsHe serves as a consultant for Industrial
Automation, and Robotic Systems to universities and industries. He received 1 academic
award from JSME (Japan Society of Mechanical Engineers) and 2 innovation awards from
MTEX (Malaysian Technology Exhibition). One of his inventions is pending for patent. He
published more than 50 articles and book chapters. He is a Member of the Institution of
Mechanical Engineers, UK, and a Chartered Engineer registered with the Engineering
Council, UK. He can be contacted at email: mozasser@uthm.edu.my.
Ryojun Ikeura currently is a Professor in the Department of Mechanical
Engineering, Faculty of Engineering, Mie University, Japan. He received his B.E., M.E., and
Ph.D. degrees in mechanical engineering from Tohoku University, Sendai, Japan, in 1986,
1988, and 1991 respectively. He has published more than 250 journal articles, conference
papers, and book chapters. His research area covers human-robot cooperation, movement
characteristics of the human arm and artificial human organs. He can be contacted at email:
ikeura@ss.mach.mie-u.ac.jp.