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Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
- Source :
- Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021), Scientific Reports
- Publication Year :
- 2021
- Publisher :
- Nature Portfolio, 2021.
-
Abstract
- Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.
- Subjects :
- Quality of life
Computer science
Interface (computing)
Science
0206 medical engineering
Skeletal muscle
Artificial Limbs
02 engineering and technology
Prosthesis Design
Convolutional neural network
Article
Machine Learning
03 medical and health sciences
0302 clinical medicine
Amputees
Real-time Control System
Computational models
Humans
Simulation
Computational model
Multidisciplinary
Gestures
Hand Strength
Electromyography
Prostheses and Implants
Hand
020601 biomedical engineering
Data processing
Gesture recognition
Unique user
Medicine
Neural Networks, Computer
Transfer of learning
030217 neurology & neurosurgery
Algorithms
Gesture
Muscle Contraction
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 11
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....17173318dde14906a26928c680dfdc84