METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Sleep Apnea Identification using HRV Features of ECG Signals IJECEIAES
Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subjectspecific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.
Real Time Implementation and Investigation of Wireless Device of Electrical S...ijtsrd
"Electrical nerve stimulation ENS is the delivery of electricity across the intact surface of the skin to activate underlying nerves generally with the objective of pain relief. Wearable Intensive Nerve Stimulation WINS is an emerging form of ENS in which the device is wearable, automated, and designed for intensive use. This enables regular use throughout the day and night, whenever the patient experiences pain, which is essential for the management of chronic pain. Hence we design and develop a wireless controlled smart tiny wearable medical device that is capable of passing electricity through underlying nerves of human beings for symptomatic relief and management of chronic pain. This project can be applicable for coma persons. When there is a slight improvement in their acceleration, this device will stimulate the peripheral nerves accordingly. Mrs. R. Ponni | S. Manisha | A. Monisha | G. Nandhini | R. Priyatharcini "Real Time Implementation and Investigation of Wireless Device of Electrical Stimulation for Peripheral Nevers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21672.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/21672/real-time-implementation-and-investigation-of-wireless-device-of-electrical-stimulation-for-peripheral-nevers/mrs-r-ponni
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Sleep Apnea Identification using HRV Features of ECG Signals IJECEIAES
Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subjectspecific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.
Real Time Implementation and Investigation of Wireless Device of Electrical S...ijtsrd
"Electrical nerve stimulation ENS is the delivery of electricity across the intact surface of the skin to activate underlying nerves generally with the objective of pain relief. Wearable Intensive Nerve Stimulation WINS is an emerging form of ENS in which the device is wearable, automated, and designed for intensive use. This enables regular use throughout the day and night, whenever the patient experiences pain, which is essential for the management of chronic pain. Hence we design and develop a wireless controlled smart tiny wearable medical device that is capable of passing electricity through underlying nerves of human beings for symptomatic relief and management of chronic pain. This project can be applicable for coma persons. When there is a slight improvement in their acceleration, this device will stimulate the peripheral nerves accordingly. Mrs. R. Ponni | S. Manisha | A. Monisha | G. Nandhini | R. Priyatharcini "Real Time Implementation and Investigation of Wireless Device of Electrical Stimulation for Peripheral Nevers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21672.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/21672/real-time-implementation-and-investigation-of-wireless-device-of-electrical-stimulation-for-peripheral-nevers/mrs-r-ponni
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
Review of Software to Analyse the Physical Conditions of the Athletes using sEMGijtsrd
Electromyography measures muscle responses of a nerve's simulation of the muscle. EMG is generally measured or recorded through surface, needle or wired electrodes. The surface electromyography is a commonly used technique for measuring the muscle exhilaration. The purpose of this project is to evaluate the use of sEMG in the practical context and to translate the given context to the appropriate analysis. The sEMG are used on the athletes while they are running and respective results are being noted. By using this technique our project wishes to implement an android iOS application to calculate the corresponding values which are being noted by the particular device which we have been made. The signals which are being given by the device is converted into the appropriate percentage values or graphs which can be determined into giving a complete overview about the person whom he is checking and can suggest the diets and exercises to make that person fit to the expectations. This software is mainly look forward for the development of the future athletes which can win the prizes. This platform provides immense forms of diets which are based on the values or results which have been depicted. Performance analysis in sports is considered to be an integral component of understanding the requirements of the optimal performance. Several measurement techniques have been used to inspect the performance of the best athletes today. it is mostly commonly done in laboratory where physiology and bio mechanics can be analyzed. in this system first, the coaches conduct a study about the agility, strength and nutrition of the excellent players of the country. Then the coaches of the respective clubs or the schools check each and every student's physical condition and compare with the stored data in order to train them. The project has got direct advantage to the aspiring future athletes of the country and also to the health conscious society by providing them a device to calculate on their body metrics and work around to improve on it. Tenwin James K | Varun Vincent | Lino Louis | Vishnuraj T | Aneesh Chandran "Review of Software to Analyse the Physical Conditions of the Athletes using sEMG" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31546.pdf Paper Url :https://www.ijtsrd.com/computer-science/embedded-system/31546/review-of-software-to-analyse-the-physical-conditions-of-the-athletes-using-semg/tenwin-james-k
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Biomedical Signal Processing / Biomedical Signals/ Bio-signals/ Bio-signals C...Mehak Azeem
These amazing and highly informative slides presented to the IEEE Signal Processing Society of IEEE MESCE Student Branch. These slides aim to provide basic knowledge about biosignals, their classification, examples and their working.
For more information, please contact:
[mehakazeem@ieee.org]
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
The mind-to-movement system that allows a quadriplegic man to control a computer using only his thoughts is a scientific milestone. It was reached, in large part, through the brain gate system. This system has become a boon to the paralyzed. The Brain Gate System is based on Cyber kinetics platform technology to sense, transmit, analyze and apply the language of neurons. The principle of operation behind the Brain Gate System is that with intact brain function, brain signals are generated even though they are not sent to the arms, hands and legs.The signals are interpreted and translated into cursor movements, offering the user an alternate Brain Gate pathway to control a computer with thought,just as individuals who have the ability to move their hands use a mouse. The 'Brain Gate' contains tiny spikes that will extend down about one millimetre into the brain after being implanted beneath the skull,monitoring the activity from a small group of neurons.It will now be possible for a patient with spinal cord injury to produce brain signals that relay the intention of moving the paralyzed limbs,as signals to an implanted sensor,which is then output as electronic impulses. These impulses enable the user to operate mechanical devices with the help of a computer cursor. Matthew Nagle,a 25-year-old Massachusetts man with a severe spinal cord injury,has been paralyzed from the neck down since 2001.After taking part in a clinical trial of this system,he has opened e-mail,switched TV channels,turned on lights
Review of Software to Analyse the Physical Conditions of the Athletes using sEMGijtsrd
Electromyography measures muscle responses of a nerve's simulation of the muscle. EMG is generally measured or recorded through surface, needle or wired electrodes. The surface electromyography is a commonly used technique for measuring the muscle exhilaration. The purpose of this project is to evaluate the use of sEMG in the practical context and to translate the given context to the appropriate analysis. The sEMG are used on the athletes while they are running and respective results are being noted. By using this technique our project wishes to implement an android iOS application to calculate the corresponding values which are being noted by the particular device which we have been made. The signals which are being given by the device is converted into the appropriate percentage values or graphs which can be determined into giving a complete overview about the person whom he is checking and can suggest the diets and exercises to make that person fit to the expectations. This software is mainly look forward for the development of the future athletes which can win the prizes. This platform provides immense forms of diets which are based on the values or results which have been depicted. Performance analysis in sports is considered to be an integral component of understanding the requirements of the optimal performance. Several measurement techniques have been used to inspect the performance of the best athletes today. it is mostly commonly done in laboratory where physiology and bio mechanics can be analyzed. in this system first, the coaches conduct a study about the agility, strength and nutrition of the excellent players of the country. Then the coaches of the respective clubs or the schools check each and every student's physical condition and compare with the stored data in order to train them. The project has got direct advantage to the aspiring future athletes of the country and also to the health conscious society by providing them a device to calculate on their body metrics and work around to improve on it. Tenwin James K | Varun Vincent | Lino Louis | Vishnuraj T | Aneesh Chandran "Review of Software to Analyse the Physical Conditions of the Athletes using sEMG" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31546.pdf Paper Url :https://www.ijtsrd.com/computer-science/embedded-system/31546/review-of-software-to-analyse-the-physical-conditions-of-the-athletes-using-semg/tenwin-james-k
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Biomedical Signal Processing / Biomedical Signals/ Bio-signals/ Bio-signals C...Mehak Azeem
These amazing and highly informative slides presented to the IEEE Signal Processing Society of IEEE MESCE Student Branch. These slides aim to provide basic knowledge about biosignals, their classification, examples and their working.
For more information, please contact:
[mehakazeem@ieee.org]
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
The mind-to-movement system that allows a quadriplegic man to control a computer using only his thoughts is a scientific milestone. It was reached, in large part, through the brain gate system. This system has become a boon to the paralyzed. The Brain Gate System is based on Cyber kinetics platform technology to sense, transmit, analyze and apply the language of neurons. The principle of operation behind the Brain Gate System is that with intact brain function, brain signals are generated even though they are not sent to the arms, hands and legs.The signals are interpreted and translated into cursor movements, offering the user an alternate Brain Gate pathway to control a computer with thought,just as individuals who have the ability to move their hands use a mouse. The 'Brain Gate' contains tiny spikes that will extend down about one millimetre into the brain after being implanted beneath the skull,monitoring the activity from a small group of neurons.It will now be possible for a patient with spinal cord injury to produce brain signals that relay the intention of moving the paralyzed limbs,as signals to an implanted sensor,which is then output as electronic impulses. These impulses enable the user to operate mechanical devices with the help of a computer cursor. Matthew Nagle,a 25-year-old Massachusetts man with a severe spinal cord injury,has been paralyzed from the neck down since 2001.After taking part in a clinical trial of this system,he has opened e-mail,switched TV channels,turned on lights
Tracking times in temporal patterns embodied in intra-cortical data for cont...IJECEIAES
Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks.
The Neuroprosthetics is an emerging field in the Health Care & Engineering Sector.
In this Technology a Specialized Chip is implanted in the Brain & by using Electronic & Mechanical Components the Brain Waves in converted into respective Mechanical Movements.
Neuroprosthetics is specifically used for patients suffering from Paralysis, Amoyotropic Lateral Sclerosis & Multiple Sclerosis.
This Field is in its Initial Stage in terms of Research specifically in India.This field requires a lot of research specially for India & Developing Countries.
Neuroprosthetics will be an Transforming World for Health Sector in the future.
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
Report consist of the literature survey of Spinal Cord Injuries. Design and implantation of the electrode with MEMS technology to implantable pulse generator with a rechargeable battery to cure the pain.
The final design document for my senior design project involving the fabrication of a shape memory alloy nerve cuff for the reinnervation of bladder function for individuals with spinal cord injuries and/or bladder deficiencies.
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 invasive modalities of neurocognitive science used for brain mappingeSAT Journals
Abstract The brain plays a pivotal role in the study neurocognitive science. It is the seat of intelligence and is a complex organ which is being widely studied. Although mapping different areas of the brain has been carried a lot still remains for the study of cognition. Various non invasive modalities used for brain mapping are classified according to the measurement techniques used. Electromagnetic Technique uses two modalities for studying electromagnetic and electrical activity of the brain EEG (Electroencephalography) MEG (Magnetoencephalogrphy).Whereas hemodynamic technique uses recording of hemodynamic activity of the brain, these modalities are MRI (Magnetic Resonance Imaging).fMRI (functional Magnetic Resonance Imaging) PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography) NRIS (Near Infrared Spectroscopy ). Hemodynamic techniques like MRI, fMRI, PET and SPECT provide excellent spatial resolution while electromagnetic modalities provide excellent temporal resolution .This paper explains various noninvasive modalities, their working principle and a comparative study of all the modalities. Index Terms: modalities, spatial and temporal resolution, Radionuclide, Artifacts
DEVELOPMENT OF MOTION CONTROL ALGORITHM FOR LOWER LIMB EXOSKELETONE WITH SIMU...DAXESHPATEL81
‘Exoskeleton’ can be interpreted into two different terms Exo-
meaning the external and ‘skeleton’- meaning rigid structure for any living organisms.
Exoskeletons are used to perform task which are usually beyond the boundaries of
human in their comfort environment. The exoskeletons can either be autonomous or
operator depended on the task to be fulfilled. Usually an ideal exoskeleton should be
able to perform any tasks which are done by human but with greater capacity and
durability. As our topic being one of the most fancy and fiction like equipment it has
been able to draw much wider attention than any other in the field of Robotics. But
such a topic composes two very different fields of Applied Sciences – Robotics and
Biology. In order to fully utilize these fields in development of Exoskeleton one must
possess a keen knowledge of all aspects of these both separate fields. So a new
Branch of Applied sciences has been introduced called ‘Bionics’
DEVELOPMENT OF MOTION CONTROL ALGORITHM FOR LOWER LIMB EXOSKELETONE WITH SIMU...DAXESHPATEL81
‘Exoskeleton’ can be interpreted into two different terms Exo-
meaning the external and ‘skeleton’- meaning rigid structure for any living organisms.
Exoskeletons are used to perform task which are usually beyond the boundaries of
human in their comfort environment. The exoskeletons can either be autonomous or
operator depended on the task to be fulfilled. Usually an ideal exoskeleton should be
able to perform any tasks which are done by human but with greater capacity and
durability. As our topic being one of the most fancy and fiction like equipment it has
been able to draw much wider attention than any other in the field of Robotics. But
such a topic composes two very different fields of Applied Sciences – Robotics and
Biology. In order to fully utilize these fields in development of Exoskeleton one must
possess a keen knowledge of all aspects of these both separate fields. So a new
Branch of Applied sciences has been introduced called ‘Bionics’
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
Dr. Frankenstein’s Dream Made Possible: Implanted Electronic Devices The Innovation Group
The developments in micro-nano-electronics, biology and neuro-sciences make it possible to imagine a new world where vital signs can be monitored continuously, artificial organs can be implanted in human bodies and interfaces between the human brain and the environment can extend the capabilities of men thus making the dream of Dr. Frankenstein become true. This paper surveys some of the most innovative implantable devices and offers some perspectives on the ethical issues that come with the introduction of this technology.
www.theinnovationgroup.it
Abstract
Besides traditional approaches for producing and storing hydrogen from cheap, surplus electricity or heat to use in energy conversion processes,
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![Nazita Taghavi1
, Greg R Luecke1
and Nicholas D Jeffery2
*
1
Department of Mechanical Engineering and Virtual Reality Applications Center, Iowa State University of Science and Technology, USA
2
Department of Veterinary Medicine & Biological Science, Texas A&M University, USA
*Corresponding author: Lei Wang, Changsha University of Science and Technology, No. 960 Wanjiali Road, 410114, Changsha, Hunan, China, Tel:
13874997316; Email:
Submission: June 14, 2018; Published: August 17, 2018
A Review on Electromechanical Methods and
Devices for Neural Rehabilitation
Introduction
The nervous system of most quadrupeds consists of two main
systems: The Central Nervous System including brain and spinal
cord and the Peripheral Nervous System which is a large nervous
system running through the body, controlling all voluntary and au-
tomatic body movements [1,2]. Disease like peripheral neuropathy
[3], damage in spinal cord [4] and severe stroke to head can cause
numbness, weakness, trouble with grasping items, problems with
walking and balancing or severe debilitating disabilities. Some of
these diseases and symptoms can be treated by medicines, physical
therapies and surgery; however, in many cases, patients face perma-
nent disabilities specially with those diseases related to brain and
spinal cord injuries since these organs include a complex system of
nerves and regeneration of those nerve cells after injury is impos-
sible. To help patients with these sort of disabilities, engineers have
proposed several electromechanical systems and methods such as
designing and building neural prostheses to help patients restore
some lost organs functions. In this article, we review recent devel-
opments of neural prostheses and their structures.
Neural Prostheses
Neural prostheses are a series of assistive devices which are de-
signed and used for therapeutic electrical stimulation, reduce pain
and even substitution of sensory or motor functions lost through
damages caused by injury or a disease to the neural system to re-
store or rehabilitate normal bodily functions [5-9]. Many of these
devices use a technique called functional electrical stimulation
(FES) to stimulate peripheral nerves electrically using skin or im-
planted electrodes [10-15]. Although skin electrodes are safer and
easier for patient to apply on body, a large current is needed for
stimulation of nerves. Implanted electrodes can be planted at prox-
imity of target nerve to use smaller current for stimulation. These
electrodes are more complex than ordinary surface electrodes and
must be clinically safe and durable [16].
Although design, construction and use of neural prostheses
with implantable electrodes and stimulators are complex, in re-
cent years, using them in patients’ bodies for various purposes
have been grown. To produce these stimulators, technologies like
construction of pacemakers may be used so that these devices
can be remain in body for years and agitate target tissues nerves
using electrical stimulations [16]. Example of these kinds of neu-
ral prostheses are dorsal column stimulator [17] and deep-brain
stimulators [18-20] for release pain and reduce spasticity, phrenic
nerve stimulator for respiration [21,22], sacral root stimulators for
bladder control [23,24] and peroneal nerve stimulator for counter-
acting hemiplegic foot drop [25,26].
Neural Prostheses Structures
Over recent decades, progresses have been achieved for reha-
bilitations and physical movements in paresis or paralyzed patients
using FES technique [27]. Various optimized cycling mechanisms,
stimulation strategy and stimulation patterns have been studied to
rehabilitate lower-limbs of paralytics using FES to pedal stationary
cycle [28-38]. Bellman et al. [39,40] designed an experimental set-
up using a stationary cycle with an optical encoder and a sensor at-
tached to the crank to measure cycling cadence and crank position,
a current controlled stimulator to produce pulses to activate target
muscles, a data acquisition hardware and software to analyze input
data from sensors and calculate output command to be applied to
Mini Review
Evolutions in Mechanical
EngineeringC CRIMSON PUBLISHERS
Wings to the Research
1/4Copyright © All rights are reserved by Nazita Taghavi.
Volume 1 - Issue - 2
Abstract
To help patients suffering disabilities caused by damage to neural system, engineers have developed new methods and devices. A recently emerging
technique is called Functional Electrical Stimulation which is the application of electrical charge for stimulating damaged organ nerves and recover
some muscular functions. In this paper, we aim to review the most recent developments in neural prostheses and their structures which have been
designed and built to apply this technique to patients’ nervous system.](https://image.slidesharecdn.com/eme-181116064241/85/Crimson-Publishers-A-Review-on-Electromechanical-Methods-and-Devices-for-Neural-Rehabilitation-1-320.jpg)
![Evolutions Mech Eng Copyright © Nazita Taghavi
2/4
How to cite this article: Nazita T, Greg R L, Nicholas D J. A Review on Electromechanical Methods and Devices for Neural Rehabilitation. 1(2).
EME.000508.2018.
Volume - 1 Issue - 2
muscles and finally skin electrodes to deliver current to muscles.
They proposed a switched system theory and a nonlinear model
of a stationary FES-cyclingsystem to improve cycling cadence and
performance.
Data acquisition system
Neural prostheses inducing FES usually have a data acquisition
system to receive data from sensors and analytically determine the
input command to muscles. This system is part of device stimulator
and usually uses a controlling strategy to actively control the stim-
ulation and induce proper charge to muscles to track the desired
trajectory. For stationary FES systems, when the space limit is not
crucial, usual data acquisition systems like computers or industri-
al or commercial stimulators like Compact RIO and Grass S8800
[41,42] can be used. For portable neural prostheses, the stimulator
must be light and small and in many cases, wearable. Recently, with
the advent of small and low-cost microcontroller platforms like Ar-
duino microcontroller, wearable and carriable FES-based neural
prostheses also have been designed and built [43,44]. Melo et al.
[43] used Arduino MCU as their analytical modulus to develop a
gait neuro-prosthesis. In their system, data received from Inertial
measurement units and force sensitive resistors is analyzed and
muscle stimulation command fordrop foot correction is sent to an
actuation modulus of the system.
Sensors
Sensors functionality in neural prostheses is sometimes de-
tecting and tracking patient gait events and send the data for fur-
ther analyses to device data acquisition system. For example, force
sensing resistors are used under the shoe or sole to detect the time
when foot heel touches the ground [45-47] or gyroscopes and ac-
celerometers to measure velocities and accelerations of different
patient’s body parts to detect feet gait events such as initial contact,
foot-flat start, toe-off and heel-off [48-57]. Maqbool et al. [58] used
an inertial measurement unit (IMU) attached to the shank of am-
putees for detecting patient’s gait event at real time. The IMU could
measure the angular velocity and linear acceleration of the shank
and these data was used by data acquisition system to detect gait
with accuracy of 99.78% using a special algorithm.
Neural Prostheses Applications in Animals
Neural and spinal cord damages and diseases are also common
in animals and can cause effects and symptoms like in humans.
Dachshund, for instant, is a kind of dog which highly susceptible to
spinal cord injuries because of its special physical body. This dog
is famous for its short legs in comparison with its long body which
makes dog more prone to breaks and damages to back of dog where
the spinal cord located. If damages to spinal cord occurs, dog may
lose its sense and movement abilities in its back leg. Sometimes dog
loses its walking ability completely, however, in many cases, dog
automatically moves its legs and even walk because of reflexes in
muscles when dog toes touch the ground. Even in such cases, dog
frequently falls since it has no sense in back muscles and ligaments
and cannot control its body or balance its hip.
Rehabilitation treatments of these injuries in dogs is a chal-
lenge for Veterinarians. Taghavi et al. [59,60], invented a “balancing
device” to correct the gait of these patient dogs. This device includ-
ed an Arduino Uno microcontroller for gathering data from an IMU
and functions as a stimulator to apply voltage to target muscles.
The IMU attached on the hip of dog sends data about the hip balanc-
ing status by measuring angular velocities and accelerations. The
amount of voltage and the duration of stimulation is based on dif-
ferent algorithm and strategies that are programmed and uploaded
to microcontroller. These strategies were developed based on dog
anatomy and gait analyses.
Conclusion
In this paper, we reviewed newly developed neural prostheses
which deliver functional electrical stimulation to damaged nerves.
We showed these devices include data acquisition systems and
sensors to obtain sensing data, analyze stimulation command and
provide enough charge which is delivered to target nerves using
electrodes. Several different processors and microprocessors as
well as electrical sensors can have applications in designing and
building of such devices. We explained examples that shows these
devices can help both humans and animals patients.
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IEEE Trans Biomed Eng 37(11): 1118-1120.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)