Article
Version 1
Preserved in Portico This version is not peer-reviewed
Human Upper Limb Movement Classification using Machine Learning on Sensor Data
Version 1
: Received: 5 June 2024 / Approved: 6 June 2024 / Online: 6 June 2024 (12:42:55 CEST)
How to cite: Gulrez, S.; Rehman, A.; Kabir, A. Human Upper Limb Movement Classification using Machine Learning on Sensor Data. Preprints 2024, 2024060388. https://doi.org/10.20944/preprints202406.0388.v1 Gulrez, S.; Rehman, A.; Kabir, A. Human Upper Limb Movement Classification using Machine Learning on Sensor Data. Preprints 2024, 2024060388. https://doi.org/10.20944/preprints202406.0388.v1
Abstract
In this paper, a seamless connection between sensor technology and machine learning (ML) has been presented which has wide applicability in rehabilitation and physical medicine. The intent is to identify an ML algorithm which has a better performance, easy to deploy, and has less training time and consumes less computational resources. In short, we propose an algorithm which maximizes the synergy between a sensor and ML by maximizing classification performance and easy to do implementation, which is robust in computational power and lower in resource consumption. The premise of the work is to show the important considerations for evaluating ML algorithms for Upper-limb activity recognition for Physical medicine and rehabilitation field.
Keywords
Machine learning; Upper limb; Movement
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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