[UC Berkeley] An armband to control prosthetic hands
Dec 22, 2020
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UC Berkeley researchers have created a new device that combines wearable biosensors with artificial intelligence software to help recognize what hand gesture a person intends to make based on electrical signal patterns in the forearm. The device paves the way for better prosthetic control and seamless interaction with electronic devices.
Abstract: Wearable devices that monitor muscle activity based on surface electromyography could be of use in the development of hand gesture recognition applications. Such devices typically use machine-learning models, either locally or externally, for gesture classification. However, most devices with local processing cannot offer training and updating of the machine-learning model during use, resulting in suboptimal performance under practical conditions. Here we report a wearable surface electromyography biosensing system that is based on a screen-printed, conformal electrode array and has in-sensor adaptive learning capabilities. Our system implements a neuro-inspired hyperdimensional computing algorithm locally for real-time gesture classification, as well as model training and updating under variable conditions such as different arm positions and sensor replacement. The system can classify 13 hand gestures with 97.12% accuracy for two participants when training with a single trial per gesture. A high accuracy (92.87%) is preserved on expanding to 21 gestures, and accuracy is recovered by 9.5% by implementing model updates in response to varying conditions, without additional computation on an external device.
Story excerpts:
“Prosthetics are one important application of this technology, but besides that, it also offers a very intuitive way of communicating with computers.” said Ali Moin, who helped design the device as a doctoral student in UC Berkeley’s Department of Electrical Engineering and Computer Sciences. “Reading hand gestures is one way of improving human-computer interaction. And, while there are other ways of doing that, by, for instance, using cameras and computer vision, this is a good solution that also maintains an individual’s privacy.”
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Moin is co-first author of a new paper describing the device, which appeared online Dec. 21, 2020 in the journal Nature Electronics.
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Andy Zhou is co-first author of this paper. Other authors include Abbas Rahimi, Alisha Menon, George Alexandrov, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan and Fred Burghardt of UC Berkeley; Simone Benatti of the University of Bologna; and Luca Benini of ETH Zürich and the University of Bologna.
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“When Amazon or Apple creates their algorithms, they run a bunch of software in the cloud that creates the model, and then the model gets downloaded onto your device,” said Jan Rabaey, the Donald O. Pedersen Distinguished Professor of Electrical Engineering at UC Berkeley and senior author of the paper. “The problem is that then you’re stuck with that particular model. In our approach, we implemented a process where the learning is done on the device itself. And it is extremely quick: You only have to do it one time, and it starts doing the job. But if you do it more times, it can get better. So, it is continuously learning, which is how humans do it.”
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While the device is not ready to be a commercial product yet, Rabaey said that it could likely get there with a few tweaks.
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This work was supported, in part, by the CONIX Research Center, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by the U.S. Department of Defense’s Defense Advanced Research Projects Agency (DARPA). The work is also based, in part, on research sponsored by the Air Force Research Laboratory under agreement number FA8650-15-2-5401, as conducted through the Flexible Hybrid Electronics Manufacturing Innovation Institute, NextFlex. Additional support was received from sponsors of the Berkeley Wireless Research Center; the National Science Foundation Graduate Research Fellowship, under grant number 1106400; the ETH Zurich Postdoctoral Fellowship program and the Marie Sklodowska-Curie Actions for People COFUND program.
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