A novel efficient human computer interface using an electrooculogrameSAT Publishing House
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
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
A novel efficient human computer interface using an electrooculogrameSAT Publishing House
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
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Trends in VLSI circuit in 2020 - International Journal of VLSI design & Commu...VLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
Modelling and Control of a Robotic Arm Using Artificial Neural NetworkIOSR Journals
Abstract: Often it can be seen that men with a lost arm face severe difficulties doing daily chores. Artificial
Intelligence could be effectively used to provide some respite to those people. Neural networks and their
applications have been an active research topic since recent past in the rehabilitation robotics/machine
learning community, as it can be used to predict posture/gesture which is guided by signals from the human
brain. In this paper, a method is proposed to estimate force from Surface Electromyography (s-EMG) signals
generated by specific hand movements and then design and control a Robotic arm using Artificial Neural
Network (ANN) to replicate human arm. Here the force prediction is a Regression process. A hand model has
been successfully moved using servo motor that has been programmed based on the results obtained from
sample data. The results shown in this paper illustrate how the Robotic arm performs.
Index Terms: Surface EMG, Artificial Neural Network, Robotic arm, Regression.
WHY ELYSIUM?
“Ultimate Destination to Boost Your Career Opportunities”
Elysium Technologies is a member of IEEE, ACM, Springer, Science Direct and Wiley and authorized member of Microsoft and ICTACT. We have collaboration with 17 International universities and 7 highly renowned universities in India. We have access for 212 International journals. Currently, 675 research scholars are pursuing their research work with us. Over the years, we have offered projects around 75000 IEEE titles.
• IEEE-based real-time projects
• Projects handled by the highly qualified and experienced experts with more than decades of experience
• Free Software Installations
• Free Video guide, Abstract, base paper and presentations
• Continuous support until Project Completion
• Feasible and convenient appointments for technical discussion
• Online chat sessions
• Live interactive sessions with the technical teams
We assure our best services always.
FACILITIES
Our curriculum is enhanced with highly innovative resources, unique professional approach, including a variety of instructional strategies and collaborative activities to promote professional advancement of the clients. Using a powerful communication interface, Our ETPL enables the students and research scholars to engage in the participatory sessions with the technical team. Based on the current technologies, Elysium has the expertise to design the wired and wireless frameworks and training delivery platform to meet the educational needs of the students and scholars.
24/7 Support
Our online support systems allow the students to give some feedbacks, comments, suggestions, testimonials and uploading of resumes through online. Our best-in-class online ticket support seamlessly routes the inquiries created through email, web-forms and phone calls into a simple, easy-to-use, multi-user, web-based ticket support platform. We have Live Chat support for providing instant and incessant support for the clients.
WHY FINAL YEAR PROJECTS ASSUME MUCH IMPORTANCE?
Irrespective of your discipline in Engineering or Science, the corporate recruiters appraise the worth and competency for the post mainly on the basis of the knowledge gained in the final year project, as they deem it to be a precursor to the real time work environment. Projects are a great opportunity to demonstrate your creative abilities and independence. It stretches your ability to reach the limits beyond the expectations.
Visit us: http://elysiumtechnologies.com/
Mobile No: 9944793398,9677724437
Chat with us: http://support365.elysiumgroups.com/livechat/chat.php
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)Victor Asanza
Procesamiento de Señales Biomédicas de Electroencefalografía y Electromiografía
Agenda:
✅ Introducción
⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
⇨ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October).
⇨ EMG Signal Processing with Clustering Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.
✅ Resultados Obtenidos
⇨ C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
The development of a wireless LCP-based intracranial pressure sensor for trau...IJECEIAES
Raised intracranial pressure (ICP) in traumatic brain injury (TBI) patients can lead to death. ICP measurement is required to monitor the condition of a patient and to inform TBI treatment. This work presents a new wireless liquid crystal polymer (LCP) based ICP sensor. The sensor is designed with the purpose of measuring ICP and wirelessly transmitting the signal to an external monitoring unit. The sensor is minimally invasive and biocompatible due to the mechanical design and the use of LCP. A prototype sensor and associated wireless module are fabricated and tested to demonstrate the functionality and performance of the wireless LCP-based ICP sensor. Experimental results show that the wireless LCP-based ICP sensor can operate in the pressure range of 0 - 60.12 mmHg. Based on repeated measurements, the sensitivity of the sensor is found to be 25.62 µVmmHg-1, with a standard deviation of ± 1.16 µVmmHg-1. This work represents a significant step towards achieving a wireless, implantable, minimally invasive ICP monitoring strategy for TBI patients.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
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.
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Trends in VLSI circuit in 2020 - International Journal of VLSI design & Commu...VLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
Modelling and Control of a Robotic Arm Using Artificial Neural NetworkIOSR Journals
Abstract: Often it can be seen that men with a lost arm face severe difficulties doing daily chores. Artificial
Intelligence could be effectively used to provide some respite to those people. Neural networks and their
applications have been an active research topic since recent past in the rehabilitation robotics/machine
learning community, as it can be used to predict posture/gesture which is guided by signals from the human
brain. In this paper, a method is proposed to estimate force from Surface Electromyography (s-EMG) signals
generated by specific hand movements and then design and control a Robotic arm using Artificial Neural
Network (ANN) to replicate human arm. Here the force prediction is a Regression process. A hand model has
been successfully moved using servo motor that has been programmed based on the results obtained from
sample data. The results shown in this paper illustrate how the Robotic arm performs.
Index Terms: Surface EMG, Artificial Neural Network, Robotic arm, Regression.
WHY ELYSIUM?
“Ultimate Destination to Boost Your Career Opportunities”
Elysium Technologies is a member of IEEE, ACM, Springer, Science Direct and Wiley and authorized member of Microsoft and ICTACT. We have collaboration with 17 International universities and 7 highly renowned universities in India. We have access for 212 International journals. Currently, 675 research scholars are pursuing their research work with us. Over the years, we have offered projects around 75000 IEEE titles.
• IEEE-based real-time projects
• Projects handled by the highly qualified and experienced experts with more than decades of experience
• Free Software Installations
• Free Video guide, Abstract, base paper and presentations
• Continuous support until Project Completion
• Feasible and convenient appointments for technical discussion
• Online chat sessions
• Live interactive sessions with the technical teams
We assure our best services always.
FACILITIES
Our curriculum is enhanced with highly innovative resources, unique professional approach, including a variety of instructional strategies and collaborative activities to promote professional advancement of the clients. Using a powerful communication interface, Our ETPL enables the students and research scholars to engage in the participatory sessions with the technical team. Based on the current technologies, Elysium has the expertise to design the wired and wireless frameworks and training delivery platform to meet the educational needs of the students and scholars.
24/7 Support
Our online support systems allow the students to give some feedbacks, comments, suggestions, testimonials and uploading of resumes through online. Our best-in-class online ticket support seamlessly routes the inquiries created through email, web-forms and phone calls into a simple, easy-to-use, multi-user, web-based ticket support platform. We have Live Chat support for providing instant and incessant support for the clients.
WHY FINAL YEAR PROJECTS ASSUME MUCH IMPORTANCE?
Irrespective of your discipline in Engineering or Science, the corporate recruiters appraise the worth and competency for the post mainly on the basis of the knowledge gained in the final year project, as they deem it to be a precursor to the real time work environment. Projects are a great opportunity to demonstrate your creative abilities and independence. It stretches your ability to reach the limits beyond the expectations.
Visit us: http://elysiumtechnologies.com/
Mobile No: 9944793398,9677724437
Chat with us: http://support365.elysiumgroups.com/livechat/chat.php
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)Victor Asanza
Procesamiento de Señales Biomédicas de Electroencefalografía y Electromiografía
Agenda:
✅ Introducción
⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
⇨ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October).
⇨ EMG Signal Processing with Clustering Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.
✅ Resultados Obtenidos
⇨ C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
The development of a wireless LCP-based intracranial pressure sensor for trau...IJECEIAES
Raised intracranial pressure (ICP) in traumatic brain injury (TBI) patients can lead to death. ICP measurement is required to monitor the condition of a patient and to inform TBI treatment. This work presents a new wireless liquid crystal polymer (LCP) based ICP sensor. The sensor is designed with the purpose of measuring ICP and wirelessly transmitting the signal to an external monitoring unit. The sensor is minimally invasive and biocompatible due to the mechanical design and the use of LCP. A prototype sensor and associated wireless module are fabricated and tested to demonstrate the functionality and performance of the wireless LCP-based ICP sensor. Experimental results show that the wireless LCP-based ICP sensor can operate in the pressure range of 0 - 60.12 mmHg. Based on repeated measurements, the sensitivity of the sensor is found to be 25.62 µVmmHg-1, with a standard deviation of ± 1.16 µVmmHg-1. This work represents a significant step towards achieving a wireless, implantable, minimally invasive ICP monitoring strategy for TBI patients.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
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.
Presentation slides discussing the theory and empirical results of a text-independent speaker verification system I developed based upon classification of MFCCs. Both mininimum-distance classification and least-likelihood ratio classification using Gaussian Mixture Models were discussed.
Text-Independent Speaker Verification ReportCody Ray
Provides an introduction to the task of speaker recognition, and describes a not-so-novel speaker recognition system based upon a minimum-distance classification scheme. We describe both the theory and practical details for a reference implementation. Furthermore, we discuss an advanced technique for classification based upon Gaussian Mixture Models (GMM). Finally, we discuss the results of a set of experiments performed using our reference implementation.
This project seeks to design innovative tools to measure in vivo biomechanical parameters of joint prostheses, orthopaedic implants, bones and ligaments. These tools, partly implanted, partly external, will record and analyze relevant information in order to improve medical treatments. An implant module includes sensors in order to measure the forces, temperature sensors to measure the interface frictions, magneto-resistance sensors to measure the 3D orientation of the knee joint as well as accelerometers to measure stem micro-motion and impacts. An external module, fixed on the patient.s body segments, includes electronic components to power and to communicate with the implant, as well as a set of sensors for measurements that can be realized externally.
This equipment is designed to help the surgeon with the alignment or positioning phase during surgery. After surgery, by providing excessive wear and micro-motion information about the prosthesis, it will allow to detect any early migration and potentially avoid later failure. During rehabilitation, it will provide useful outcomes to evaluate in vivo joint function. The tools provided can also be implanted during any joint surgery in order to give the physician the information needed to diagnose future disease such as ligament insufficiency, osteoarthritis or prevent further accident. The proposed nanosystems are set to improve the efficiency of healthcare, which is both a benefit to the patient and to society. Although the scientific and technical developments proposed in this project can be applied to all orthopaedic implants, the technological platform which is being built as a demonstrator is limited to the case of knee prosthesis. In addition, by reaching the minimum size achievable thanks to clever packaging techniques and also by reducing, or even removing, the cumbersome battery, it paves the way for a new generation of autonomous implantable medical devices.
Non-Contact Health Monitoring System Using Image and Signal ProcessingAtul Kumar Sharma
Presently digital medical devices promise to transform the future of medicine because of their ability to produce exquisitely detailed individual physiological data. As ordinary people start to have access and control over their own physiological data so that they can play a more active role in the management of their health. Currently many techniques are available for counting our heartbeat but it all needs bundles of sensors and wires. For heartbeat measurement using Electrocardiograph(ECG) method, we have to attach a bundle of leads in our chest and have to use adhesive gel. It is very difficult to patients and it can cause irritation to the skin. Another type is pulse oximeters and sensors, in this method sensors are attached to the finger tips or earlobes. This is also difficult for user.
In case of "Non-contact health monitoring system using image and signal processing" which gives contact free measurement about our physiological information using basic image processing devices. Users have the experience of real time health monitoring by just looking into "medical mirror". It recognizes our heartbeat without any external or internal sensor and displays it in real time. This invention helps people to access their own physiological data.
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
A Wireless ECG Plaster for Real-Time Cardiac Health Monitoring in Body Senso...ecgpapers
In this paper we present a wireless ECG plaster
that can be used for real-time monitoring of ECG in cardiac
patients. The proposed device is light weight (25 grams),
wearable and can wirelessly transmit the patient’s ECG signal to
mobile phone or PC using ZigBee. The device has a battery life of
around 26 hours while in continuous operation, owing to the
proposed ultra-low power ECG acquisition front end chip. The
prototype has been verified in clinical trials.
DrCell – A Software Tool for the Analysis of Cell Signals Recorded with Extra...CSCJournals
Microelectrode arrays (MEAs) have been applied for in vivo and in vitro recording and stimulation of electrogenic cells, namely neurons and cardiac myocytes, for almost four decades. Extracellular recordings using the MEA technique inflict minimum adverse effects on cells and enable long term applications such as implants in brain or heart tissue.
Hence, MEAs pose a powerful tool for studying the processes of learning and memory, investigating the pharmacological impacts of drugs and the fundamentals of the basic electrical interface between novel electrode materials and biological tissue. Yet in order to study the areas mentioned above, powerful signal processing and data analysis tools are necessary.
In this paper a novel toolbox for the offline analysis of cell signals is presented that allows a variety of parameters to be detected and analyzed. We developed an intuitive graphical user interface (GUI) that enables users to perform high quality data analysis. The presented MATLAB® based toolbox gives the opportunity to examine a multitude of parameters, such as spike and neural burst timestamps, network bursts, as well as heart beat frequency and signal propagation for cardiomyocytes, signal-to-noise ratio and many more. Additionally a spike-sorting tool is included, offering a powerful tool for cases of multiple cell recordings on a single microelectrode.
For stimulation purposes, artifacts caused by the stimulation signal can be removed from the recording, allowing the detection of field potentials as early as 5 ms after the stimulation.
Day by day the scope & use of the electronics concepts in bio-medical field is increasing step by step. In this paper the review of newly developed concepts is done for the monitoring of the ECG signal. This paper also reviews a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node. Further the study of IoT frame work for ECG monitoring has been carried out. Ms. Dhanashri Yamagekar | Dr. Pradip Bhaskar"Real time ECG Monitoring: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7065.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7065/real-time-ecg-monitoring-a-review/ms-dhanashri-yamagekar
Class D Power Amplifier for Medical Applicationieijjournal
The objective of this research was to design a 2.4 GHz class AB Power Amplifier (PA), with 0.18um Semiconductor Manufacturing International Corporation (SMIC) CMOS technology by using Cadence software, for health care applications. The ultimate goal for such application is to minimize the trade-offs between performance and cost, and between performance and low power consumption design. This paper introduces the design of a 2.4GHz class D power amplifier which consists of two stage amplifiers. This power amplifier can transmit 15dBm output power to a 50Ω load. The power added efficiency was 50% and the total power consumption was 90.4 mW. The performance of the power amplifier meets the specification requirements of the desired.
CLASS D POWER AMPLIFIER FOR MEDICAL APPLICATIONieijjournal1
The objective of this research was to design a 2.4 GHz class AB Power Amplifier (PA), with 0.18um
Semiconductor Manufacturing International Corporation (SMIC) CMOS technology by using Cadence
software, for health care applications. The ultimate goal for such application is to minimize the trade-offs
between performance and cost, and between performance and low power consumption design. This paper
introduces the design of a 2.4GHz class D power amplifier which consists of two stage amplifiers. This
power amplifier can transmit 15dBm output power to a 50Ω load. The power added efficiency was 50%
and the total power consumption was 90.4 mW. The performance of the power amplifier meets the
specification requirements of the desired.
1. EECS 452 Digital Signal Processing Design
Laboratory
Portable Electromyography (EMG) Tracking Device Interfacing Android
Device with Bluetooth for physical Therapy and Sports Medicine
Fred Buhler, Jason Hernandez, Angie Zhang, Alastair Shi
Department of Electrical Engineering and Computer Science
University of Michigan, Ann Arbor
April 24th, 2014
2. Table of Contents
I. Introduction
1) EMG Signal and its Nature
2) Motivation
3) Goals
4) End Product Evaluation
II. Description of the Project
1)Signal Architecture
2) Custom PCB design
3) Battery Supply
4) Signal Acquisition
5) Supporting Hardware
i)Bluetooth
ii) FPGA
6) DSP Algorithm Development
i) Fatigue Identifier Algorithm
Initial Method: Mean Frequency
A New Route: Power Spectrum
ii) Force Output Algorithm
7) Implementing Logic on FPGA
Fatigue Identifier Module
Force Output Module
Bluetooth Module
8) Programming Language
VHDL Blocks
C & C++
Java & HTML
MATLAB
9) Parts List
III. Milestones
First Milestone
Second Milestone
Third Milestone
IV. COE Design Expo
V. Member Contributions
VI.References and Citations
3. I. Introduction
EMG Signal and its Nature
Electromyography (EMG) is an experimental technique concerned with the
development, recording and analysis of myoelectric signals, which are formed by
physiological variations in the state of the muscle fibre membranes.
● Complicated signal
● Controlled by the nervous system
● Dependent on the anatomical and physiological properties of muscles
● Acquire noise while traveling through different tissues
Motivation
The EMG signals are studied for medical science research, rehabilitation, ergonomics,
sports science and more. EMG is used clinically for the diagnosis of neurological and
neuromuscular problems. It is used diagnostically by gait laboratories and by clinicians
trained in the use of biofeedback or ergonomic assessment.
We summarized a small subset of the benefits of working with EMG signals:
● EMG allows to physicians directly “look” into the muscle
● It allows measurements of muscular performance
● Helps in decision making both before/after surgery
● Allows analysis to improve sports activities
Goals
Before we started the project, we performed marker analysis and research to see if the
project is worth investing. After visiting laboratory and clinical setting, we learned the
following:
● The equipment in a clinical or a laboratory setting lacks portability and mobility
● Physical therapists have to bring the patients who have mobile constraints to the
exam room in order to perform EMG analysis.
● Leading competitor in the market can only send the data to a wireless local area
network. Data can only be displayed on a computer.
After understanding the current market, we were inspired and set out the following
goals for this semester including:
● Implement design on a printed circuit board (PCB) rather than a bread board
● Use FPGA as a center for data processing
● Develop working DSP algorithms to monitor the presence of fatigue and force
output of the muscle
● Send information to Android phone or tablet via Bluetooth
● Emotion detection (stretch goal)
4. End Product Evaluation
Before the design expo, we were able to meet most of the goals that we expected to
accomplish. As shown on the poster, our prototype is placed in the center. The custom
PCB is the harbor for EMG probes, power supply, analog filters, FPGA and Bluetooth.
At the COE design expo, the device was well received and it worked reasonably well at
the design expo. Our device has an algorithm that calibrates individual’s peak value of
the EMG signal and this feature ensures that the device works among different people.
Figure 1. The poster for “Portable EMG” shown at the College Engineering Design Expo
II. Description of project
EECS 452 is an embedded DSP class but we wanted to go to the extra mile. Besides the
key feature of our prototype is mobility, which enables 24/7 mobile monitoring of the
EMG activities for clinical applications. Angie Zhang and Jason Hernandez have
connections with the research lab in the medical school and the UM hospital. We were
able to collect the EMG data from medical grade equipment and compare to the signal
obtained from our own prototype. Given more time and resources, we would like to
further verify the accuracy of our device and seek medical professionals for advice.
5. System Architecture
Figure 2. Top Level Overview of the Portable EMG Tracking Device
Figure 3. Jason using the prototype to display his muscle activities while lifting a 10 lb dumbbell.
An EMG signal enters the system via a three-plug standard medical connector or a
standard 3.5mm audio jack connector terminal block on the PCB. The signal is amplified
and filtered through an open source analog front end (slightly modified from an Olimex
schematic). The signal is then digitized and sent to a Spartan 6 FPGA where the signal is
processed and sent to a PC over USB or a mobile device running Android over Bluetooth.
6. Figure 4. Implemented System Level Diagram
Custom PCB design
Figure 5. Custom PCB as the “communication center” of the data.
7. Instead of breadboards, we decided to use printed circuit board, which ensures more
durability and stability during the “wear and tear” of the testing process and the COE
design expo. The PCB is also highly customizable and can be reconfigured to work
specifically for one individual once commercialized. The board design can also be easily
changed into one that measures the ECG, which could lead to more applications and
future prospects for our project.
Battery Supply
Board is designed to operate on a wide range of input sources from 4 to 12 volts,
including batteries for mobile use or power supplies for use in a laboratory environment.
Signal Acquisition
Board accepts a wide range of commercially available EMG sensors with two inputs:
● 3.5mm audio jack
● Standard medical equipment plug.
After experimenting both probes, we found that the standard medical equipment plug
provides much better signals so we used that one for testing and demonstration.
Supporting Hardware
Bluetooth
The RN-42 Class 2 Bluetooth module supports BCSP, DUN, LAN, GAP SDP, RFCOMM,
and L2CAP protocols. Most importantly, it connects seamlessly to Bluetooth capable
mobile devices.
FPGA
OPal Kelly’s small form-factor XEM 6001 with a Spartan6 FPGA drives the board, is
fully reconfigurable, and enables powerful Digital Signal Processing with its DSP 48A1
slices.
Figure 6. OPal Kelly xem 6001
8. DSP Algorithm Development
Figure 7. Exhibits a simplified block diagram representing the DSP aspects found within the project.
The first 3 modules prepare the data for our information extraction modules that are present later in
the datapath.
1) Fatigue Identifier Algorithm
Initial Method: Mean Frequency
Before beginning work on our DSP algorithm, our team read through many EMG
publications in order to identify the most appropriate means for obtaining the
information we wanted. An earlier option of ours was to detect something known as the
median frequency within the power spectrum. This was considered to be a valuable
parameter because of the way it behaved during a muscle's state of fatigue.
Figure 8. Mean frequency spectrum and its correlation with muscle fatigue. [1]
9. A New Route: Power Spectrum
As we began implementing an algorithm which monitored this ability, out team
continued to research alternatives to identifying levels of muscle fatigue. We eventually
found out that not only did the median frequency shift a certain way during fatigue, but
there was a distinct change in the total power as well (Figure 8). Amplitudes at
frequencies greater than 30 Hz remained consistent as a muscle grew tired while
amplitudes for frequencies below 30 Hz increased dramatically. This phenomenon
explains both why the median point on the spectrum falls to a lower frequency, and why
there is an overall power increase in the spectrum [2].
After confirming that our newly obtained knowledge was persistent with our own data,
we settled on a means for observing the power spectrum. We chose to implement a
moving average filter as the core means for preparing our data for the nonconventional
processes that would occur later in our datapath. Initial Matlab tests with MA filters not
only proved its effectiveness on our datasets, but also demonstrated its applicability for a
variety of information extractions.
When visually observing the output of the MA filter, the biological properties which we
sought were clearly evident from one dataset to the next. Not only did the output
clearly resemble the force being exerted by the muscle of interest, but there was a
distinct definition of when the onset of fatigue occurred (Figure 9).
Figure 9. The input (rectified data) and output to the MA filter on a dataset that describes a
repetitive lifting of a 10 lb weight. Note the distinct vertical shift around the 25,000th
sample,
representing the onset of fatigue in the measured muscle
10. Once we were confident with the aspects' consistent presence within the MA filter
outputs, we began implementing it into hardware languages (VHDL & Verilog) as well as
start creating algorithms that could "decipher" the MA outputs. Using methods taught
in class and practiced in lab, we generated an elliptical low pass filter to isolate the
relevant information in the EMG signal. This type of lowpass was chosen due to its
ability to exemplify a sharp drop-off in the frequency spectrum while utilizing a tolerable
number of coefficients. The input to the MA filter was required to be rectified, so an
additional filter was included which mirrored everything below our DC bias to its
respectable quantity. We eventually agreed upon a 2048-point MA filter in order to
balance its ability in smoothing our data without overly distorting any relevant
information.
The MA filter would then be sent to a calibration module which adjusted the data
accordingly to later be interpreted by the force output and fatigue modules. The
calibration module was necessary in order for our algorithms to be effective when used
by a variety of people with different physical attributes. The calibrator worked by
identifying when a user was at rest and then they first exhibited activity. It also
provided succeeding modules with various parameters to be used in measuring
information that was significant to us. The calibration worked very closely with the
fatigue identifier, as it provided the module with certain values which were used to
construct multiple thresholds. The fatigue identifier detected whether or not a threshold
was successfully crossed (which included disregarding insignificant activations) by
comparing calibration values to the MA filter output over the course of 4 second window,
and would notify the calibrator of its current state in order to generate additional
parameters (such as new thresholds for determining an increased or decreased level of
fatigue). Subsequent fatigue thresholds were determined by finding the mean value of
the MA output over the 4 second window and raising that value by 25%. The output of
the fatigue identifier was a two bit value, capable of representing 3 levels of fatigue in
addition to the non-fatigued state.
2) Force Output Algorithm
The ability to detect force output was another main goal of this project. This module
worked by taking in data directly from the MA filter as well as certain coefficients from
the calibration module (Figure 10). The coefficients were used to create a curve that
estimated the current force as a numerical integer (in lbs) based off the user's previous
activity. Once this curve was generated, the module would use the MA output to
determine where the current input lies on the generated curve. Furthermore, the
algorithm worked in conjunction with the fatigue identifier in order to more accurately
calculate the force. As previously mentioned, there is a shift in amplitude when muscle
11. fatigue is present, and this shift would have to be accounted for in order prevent an
overestimation of the force [3].
Implementing Logic on FPGA
After fully develop the algorithms, we needed to implement them on the FPGA. The
elements of the DSP algorithm block diagram were implemented as individual modules
in HDL. The primary tool used to generate these modules was a built-in “Core
Generator” module known as “IP Core”. Through this tool various filters were built. The
“Core Generator” requires various configuration parameters such as filter coefficients,
data input type, sampling rate, and other things specific to core such as when data is
ready to leave and enter the core. This tool was used in the design of the blocks “Low
Pass Filter”, and “MA Filter” in conjunction to fdatool. The MA filter has all coefficients
set to “1”. There are various other handwritten VHDL modules to interface all of the
core generator modules, along with to handle the rectification/offset block, calibration,
and fatigue/force output.
Fatigue Identifier Module
The fatigue module detected fatigue based on a theory regarding an increase in values of
the EMG amplitude. Through the Calibration module we would acquire the average
value of the EMG, we would then calculate 25% more than that value indicating fatigue.
Once this value was acquired we would raise a flag showing fatigue signaling for the
Bluetooth communication module to tell the phone app to show fatigue.
Force Output Module
Figure 10. Shows the results of the force output module (above) after receiving input data from the
MA filter (below). The calibration coefficients are circled in red, the current fatigue it in yellow, and
the force output is in blue. Note the correlation between the peaks, troughs, and force output value.
This data was acquired while lifting and then lowering a 10 lb weight.
12. Bluetooth Module
The last module was for communication to the bluetooth module and how to utilize the
Bluetooth module. A baud-rate generator created a clock for serial communication, and a
frame generator module created a 5-element ASCII frame that contained the DSP state
and normalized data for plotting.
Programming Languages
Many programming languages were used in the process of the project. Here is a brief
summary of where and how each of them is used.
1) VHDL Blocks: Fatigue Detection Module, SPI driver, Opal Kelly Front Panel
Wrapper, Bluetooth baud generator and serial COM driver, Binary to serial frame
transformation, digital bias/rectification, as well as Remote configuration module & data
buffering.
2) C & C++: Built executable to accept configuration data (called from command line or
Matlab) as well as handled PC-side buffering to accept raw data from FPGA.
3) Java & HTML: Used for creating Mobile Application for Android devices
4) MATLAB: Both DSP algorithm testing, and as a FPGA/PC interfacing tool for
initiating data transfers and real-time behavioral configuration of FPGA.
Final Parts List
Items
Purchased
Item Num. Quantity Price per unit Total
Neuroline Sensor 715 05 2 packs $12.20 $24.40
Neuroline Sensor 715 08 2 packs $12.60 $25.20
2-slot Adhesive
Skin Interface
(SC-F01
(DE-02))
100 pieces $35 $35
FPGA Board XEM6001 or
DE70
1 Available in lab 0
Table 1. Detailed list of items purchased for this project.
Note: Total cost of the prototyping process was reasonably low and we did not go over
budget allowed for this project.
13. III. Milestones
1) First milestone:
Our original goal was to have the ability to collect raw EMG data on the PCB, digitize the
data, as well as have the raw data sent to a computer for DPS algorithm testing. We met
this milestone on-time, and by this milestone we were already running DSP algorithms
on our raw data in Matlab leading to our second milestone.
2) Second milestone:
Our original goal was to have a working DSP algorithm implemented in the FPGA fabric.
By the second milestone meeting – we already had a final working product (we could
collect, process, and display live data), however the DSP we had implemented was
simple time/magnitude threshold detection and indication of the EMG signal. While this
was in itself a successful milestone, the DSP still needed some work before milestone 3.
3) Third milestone:
By the design expo we successfully implemented a more complex and through DSP
algorithm that self-calibrated to the user and accurately detected fatigue.
IV. Design Expo
At the design expo and the final presentation, our prototype was passed around since it’s
highly portable. We had the test subject wearing the sticky probes, which collects the
EMG signal and send it to our device. Since the device sends data via Bluetooth to the
Android device, the user can see their live muscle activities while lifting the dumbbell of
different weights.
On a mobile device, the results were displayed as shown in Figure 11. One of the key
features in the user interface we applied is to indicate the onset of the fatigue state of the
muscle group by flashing the screen orange and the severe state of fatigue by flashing
the screen red.
We had great reception at the design expo. People of different age groups tried out our
device. The calibration module in DSP algorithm picks up unique characteristics of
individual’s muscle information so the device worked reasonably well among different
test subjects.
14. Figure 11. EMG Signal Display under relaxed condition (left) and after strenuous activity, muscle
fatigue is indicated by a flashing screen (right) on Fred’s Android phone.
Figure 12. Kids in 5th grade wearing electrodes and testing our prototype at the design expo.
15. V. Team Member Contribution
Fred Buhler (Senior in Electrical Engineering)
1) Designed PCB
2) Wrote FPGA-PC interface
3) Wrote several hardware communication modules to interface FPGA with external
components
4) Wrote Android Application
5) Assisted in writing test code as well as writing code generation scripts in Matlab
Jason Hernandez (Senior in Electrical Engineering and computer Engineering)
1) Initial and subsequent research on fatigue presence and force output
2) Tested preliminary algorithms in Matlab before hardware implementation
3) Transcribed fatigue identifier and force output into verilog from Matlab test code
4) Wrote and executed testbenches to simulate algorithms under various conditions
5) Designed early version of calibration module and median frequency calculator
Alastair Shi (Senior in Electrical Engineering)
1) EMG Signal Research on fatigue presence and force output
2) EMG Signal Processing Implementation (HDL) - all filters and fatigue module
3) Wrote and executed testbenches to simulate algorithms under various conditions
4) Guinea pig - lab test subject
Angie Zhang (Senior in Electrical Engineering)
1) Proposed the original idea and researched potential commercial value of the project
2) Worked closely with team members on the concept and development calibration
module, fatigue identifier and force output in both MATLAB and FPGA;
3) Learned to use ISIM, VHDL and helped convert Verilog code into VHDL
4) Collected data and tested the algorithms;
5) Poster design and final report
VI. References and citations
[1]
http://www.pt.ntu.edu.tw/hmchai/Biomechanics/BMmeasure/MuscleStrengthMeasure
.files/EMGwithFatigue.png
[2]
http://link.springer.com/article/10.1007%2FBF00421697
[3]
http://rsta.royalsocietypublishing.org/content/368/1920/2765.short