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.
Towards versatile fast training for wearable interfaces in prosthetics / Benatti, S., Montagna, F., Kartsch, V., Rahimi, A., Benini, L. (BIOSYSTEMS & BIOROBOTICS). - In: CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION IIIGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : SPRINGER INTERNATIONAL PUBLISHING AG, 2019. - ISBN 978-3-030-01844-3. - pp. 157-161 [10.1007/978-3-030-01845-0_31]
Towards versatile fast training for wearable interfaces in prosthetics
Benatti S.;
2019
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.
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