TripCEAiR: A multi-loss minimization approach for surface EMG based airwriting recognition

Tripathi, A and Prathosh, AP and Muthukrishnan, SP and Kumar, L (2023) TripCEAiR: A multi-loss minimization approach for surface EMG based airwriting recognition. In: Biomedical Signal Processing and Control, 85 .

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Abstract

Airwriting Recognition refers to the problem of identification of letters written in space with movement of the finger. It can be seen as a special case of dynamic gesture recognition wherein the set of gestures are letters in a particular language. Surface Electromyography (sEMG) is a non-invasive approach used to capture electrical signals generated as a result of contraction and relaxation of the muscles. sEMG has been widely adopted for gesture recognition applications. Unlike static gestures, dynamic gestures are user-friendly and can be used as a method for input with applications in Human Computer Interaction. There has been limited work in recognition of dynamic gestures such as airwriting, using sEMG signals and forms the core of the current work. In this work, a multi-loss minimization framework for sEMG based airwriting recognition is proposed. The proposed framework aims at learning a feature embedding vector that minimizes the triplet loss, while simultaneously learning the parameters of a classifier head to recognize corresponding alphabets. The proposed method is validated on a dataset recorded in the lab comprising of sEMG signals from 50 participants writing English uppercase alphabets. The effect of different variations of triplet loss, triplet mining strategies and feature embedding dimension is also presented. The best-achieved accuracy was 81.26 and 65.62 in user-dependent and independent scenarios respectively by using semihard positive and hard negative triplet mining. The code for our implementation will be made available at https://github.com/ayushayt/TripCEAiR © 2023 Elsevier Ltd

Item Type:	Journal Article
Publication:	Biomedical Signal Processing and Control
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd.
Keywords:	Character recognition; Embeddings; Gesture recognition; Human computer interaction, Airwriting; Electrical signal; Feature embedding; Gestures recognition; Losses minimizations; Muscle computer interface; Surface electromyography; Surface electromyography signals; Surface EMG; Triplet loss, Muscle
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	14 Jun 2023 13:24
Last Modified:	14 Jun 2023 13:24
URI:	https://eprints.iisc.ac.in/id/eprint/81934

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