On the Applications of EMG Sensors and Signals
This reprint captures the latest advances in Electromyography (EMG) sensor development, EMG sensor applications, and EMG signal conditioning using theoretical and experimental approaches. This report is timely, as EMG sensors and signals have applications in many domains. This reprint provides a sna...
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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042 | |a dc | ||
072 | 7 | |a TB |2 bicssc | |
072 | 7 | |a TBX |2 bicssc | |
100 | 1 | |a Kamavuako, Ernest N. |4 edt | |
700 | 1 | |a Kamavuako, Ernest N. |4 oth | |
245 | 1 | 0 | |a On the Applications of EMG Sensors and Signals |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2023 | ||
300 | |a 1 electronic resource (366 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This reprint captures the latest advances in Electromyography (EMG) sensor development, EMG sensor applications, and EMG signal conditioning using theoretical and experimental approaches. This report is timely, as EMG sensors and signals have applications in many domains. This reprint provides a snapshot of several exciting EMG sensor and signal applications: swallowing, motion detection and prostheses control, muscle synergies, robotic exoskeleton, driver's behaviour, signal conditioning, and muscle assessment. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Technology: general issues |2 bicssc | |
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a human activity recognition | ||
653 | |a surface electromyography | ||
653 | |a inertial measurement units | ||
653 | |a feature selection | ||
653 | |a wearable sensors | ||
653 | |a surface electromyogram | ||
653 | |a prosthesis control | ||
653 | |a wearable | ||
653 | |a low-cost | ||
653 | |a human fatigue monitoring | ||
653 | |a neuromuscular fatigue | ||
653 | |a surface electromyography time-frequency signal analysis | ||
653 | |a time-series modeling | ||
653 | |a autoregressive moving average model with exogenous inputs | ||
653 | |a isometric contraction | ||
653 | |a elbow extension | ||
653 | |a stroke rehabilitation | ||
653 | |a surface electromyography (sEMG) | ||
653 | |a pattern recognition (PR) | ||
653 | |a ankle joint movements | ||
653 | |a home-based physical therapy | ||
653 | |a lower limb functional recovery | ||
653 | |a inspiratory threshold loading | ||
653 | |a neuromechanical coupling | ||
653 | |a parasternal intercostal muscles | ||
653 | |a respiratory pressure | ||
653 | |a surface mechanomyography | ||
653 | |a Targeted Muscle Reinnervation (TMR) | ||
653 | |a upper limb amputee | ||
653 | |a prosthesis | ||
653 | |a prosthetic control | ||
653 | |a multi-DoF control | ||
653 | |a pattern recognition | ||
653 | |a myofascial pain syndrome | ||
653 | |a trigger points | ||
653 | |a electromyography | ||
653 | |a deep dry needling | ||
653 | |a ischemic pressure technique | ||
653 | |a ADL | ||
653 | |a EMG | ||
653 | |a forearm muscles | ||
653 | |a muscles role | ||
653 | |a synergies | ||
653 | |a muscle coordination | ||
653 | |a muscle synergy | ||
653 | |a MCR-ALS | ||
653 | |a sparseness | ||
653 | |a motor function | ||
653 | |a stroke | ||
653 | |a electromyogram | ||
653 | |a exploratory data analysis | ||
653 | |a wave train electrical activity analysis method | ||
653 | |a wave trains | ||
653 | |a wavelets | ||
653 | |a signal processing | ||
653 | |a AUC diagrams | ||
653 | |a ROC analysis | ||
653 | |a Parkinson's disease | ||
653 | |a myoelectric prostheses | ||
653 | |a embroidered EMG electrodes | ||
653 | |a pilot study | ||
653 | |a online and offline performance | ||
653 | |a conventional gel electrodes | ||
653 | |a artificial neural network | ||
653 | |a exoskeleton | ||
653 | |a assistive technology | ||
653 | |a robotics | ||
653 | |a hand gestures | ||
653 | |a muscle synergies | ||
653 | |a movements | ||
653 | |a postures | ||
653 | |a the central nervous system | ||
653 | |a motor control | ||
653 | |a the neural control | ||
653 | |a electrode shift | ||
653 | |a hand posture | ||
653 | |a feature vector | ||
653 | |a human-computer interaction | ||
653 | |a armband sensor | ||
653 | |a driver-automation shared control | ||
653 | |a haptic guidance steering | ||
653 | |a adaptive automation design | ||
653 | |a driver distraction | ||
653 | |a human machine interface | ||
653 | |a robotcontrol | ||
653 | |a EMG-control schemes | ||
653 | |a swallowing events | ||
653 | |a geriatrics | ||
653 | |a hydration | ||
653 | |a fluid intake | ||
653 | |a classification model | ||
653 | |a biomechanical simulation | ||
653 | |a Granger causality | ||
653 | |a functional coordination | ||
653 | |a dysphagia | ||
653 | |a swallowing muscle coupling | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/7041 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/98979 |7 0 |z DOAB: description of the publication |