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Published in:
2019 | OriginalPaper | Chapter
Towards Versatile Fast Training for Wearable Interfaces in Prosthetics
Authors : Simone Benatti, Fabio Montagna, Victor Kartsch, Abbas Rahimi, Luca Benini
Published in: Converging Clinical and Engineering Research on Neurorehabilitation III
Publisher: Springer International Publishing
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Abstract
Developing embedded systems tailored for resource-constrained platforms enables the design of robust frameworks for controlling artificial arms in prosthetic applications. This work presents preliminary results of the implementation of a novel platform for EMG-based gesture recognition application based on Hyper dimensional Computing (HDC), a novel brain-inspired classifier. HDC reaches classification accuracy comparable with traditional statistical learning algorithms, while its training phase is one order of magnitude faster, resulting suitable for the implementation on low-power and low-cost digital platforms. The proposed setup acquires EMG data from 8 sensors, performs training in 1.2 s on the embedded microcontroller and classifies 5 gestures with 88% accuracy, a latency of 10ms and energy consumption of just 0.65 mJ per classification.