The World at Your Fingertips
HSE University scientists have created a model for decoding finger movements for next-generation hand prostheses. This will allow users not only to grab objects, but also to gesticulate using the movements of each finger.
The results of the work, which investigates the possibility of accurately restoring finger movements using electromyographic signals from forearm muscles in people with disabilities, were presented at the joint scientific seminar ‘Digital Technologies for Medical Applications’. The event took place as part of the Digital Transformation: Technologies, Effects, Effectiveness strategic project under the Priority 2030 programme. The project was selected as part of the HSE University Research Project Competition for Young Scientists (up to 35 years old) on the topic of digital transformation and the development of digital technologies in 2022.
Igor Agamirzian
‘Today, almost any medical device is a specialised computer,’ said Igor Agamirzian, Director of HSE University’s School of Innovation and Entrepreneurship, as he opened the event.
‘The most important area now is the use of digital technologies to solve the problems faced by people with disabilities, and there is significant progress there,’ he stressed.
Anna Makarova, Research Assistant at HSE University’s Centre for Bioelectric Interfaces, said that present-day prostheses for people who have had hand amputations or were born with hand aplasia are far from ideal. Many modern bioelectric hand prostheses employ a rather primitive control system based on the use of only two large forearm muscles. The prosthesis mechanism converts the electrical signals of alternating muscle tension and relaxation into commands that are typed like binary code in programming. This does not happen intuitively—the user must learn to use the prosthesis, just as they would learn to program using ones and zeros.
Thus, the prosthesis can form one to ten grips, but the movements of each individual finger are not currently utilised on such devices. Studying the possibility of restoring precise movements based on muscle activity is a prerequisite for creating the next generation of prosthesis control systems.
Anna Makarova
‘Recent work has shown that with the use of various methods of machine learning, the movements of individual fingers in healthy people can be restored even through superficial muscle signals. We decided to find out if this is possible in people with amputations,’ the speaker explained.
The HSE University scientists created a model for decoding finger movements and selected the optimal hyperparameters that give the best decoding accuracy on data obtained in an experiment involving healthy subjects. To record EMG activity, they used a wireless 8-channel bracelet and a virtual reality headset. The experimental environment included a virtual environment with the capability to capture and save the coordinates of individual fingers. The subjects were instructed to perform symmetrical movements with both hands, which made it possible to obtain data on the movements performed in the case of amputees undergoing the experiment.
The model was found to be 50% accurate for subjects with a congenital absence of a hand and 71% for subjects who underwent an amputation in adulthood. These are promising results and a good prospect for further research and model improvements, said Anna Makarova.
Anna Makarova
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