Chen, KeWang, HonggangCatlin, AndrewSatyanarayana, AshwinVinjamuri, RamanaKadiyala, Sai Praveen2024-12-112024-12-112024-11-11Chen, Ke, Honggang Wang, Andrew Catlin, Ashwin Satyanarayana, Ramana Vinjamuri, and Sai Praveen Kadiyala. “A Framework for Empirical Fourier Decomposition Based Gesture Classification for Stroke Rehabilitation.” IEEE Internet of Things Journal, 2024, 1–1. https://doi.org/10.1109/JIOT.2024.3491674.https://doi.org/10.1109/JIOT.2024.3491674http://hdl.handle.net/11603/37095The demand for surface electromyography (sEMG) based exoskeletons is rapidly increasing due to their non-invasive nature and ease of use. With increase in use of Internet-of-Things (IoT) based devices in daily life, there is a greater acceptance of exoskeleton based rehab. As a result, there is a need for highly accurate and generalizable gesture classification mechanisms based on sEMG data. In this work, we present a framework which pre-processes raw sEMG signals with Empirical Fourier Decomposition (EFD) based approach followed by dimension reduction. This resulted in improved performance of the hand gesture classification. EFD decomposition’s efficacy of handling mode mixing problem on non-stationary signals, resulted in less number of decomposed components. In the next step, a thorough analysis of decomposed components as well as inter-channel analysis is performed to identify the key components and channels that contribute towards the improved gesture classification accuracy. As a third step, we conducted ablation studies on time-domain features to observe the variations in accuracy on different models. Finally, we present a case study of comparison of automated feature extraction based gesture classification vs. manual feature extraction based methods. Experimental results show that manual feature based gesture classification method thoroughly outperformed automated feature extraction based methods, thus emphasizing a need for rigorous fine tuning of automated models.10 pagesen-US© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ElectrodesPrincipal component analysisstroke rehabilitationEmpirical Fourier DecompositionManualsElectromyographyFeature extractionsurface electromyographySignal resolutionStroke (medical condition)Time-domain analysisMusclesAccuracygesture classificationText