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.. - 21:(2019), 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.
2019
CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION III
978-3-030-01844-3
978-3-030-01845-0
Springer International Publishing
Towards versatile fast training for wearable interfaces in prosthetics / Benatti, S.; Montagna, F.; Kartsch, V.; Rahimi, A.; Benini, L.. - 21:(2019), pp. 157-161. [10.1007/978-3-030-01845-0_31]
Benatti, S.; Montagna, F.; Kartsch, V.; Rahimi, A.; Benini, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1264947
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