Utilizing Data Quality Indices for Strategic Sensor Channel Selection to Enhance Performance of Hand Gesture Recognition Systems
Shen Zhang, Hao Zhou, Rayane Tchantchane, Gursel Alici

TL;DR
This paper introduces a method to select the best sensor channels for hand gesture recognition using data quality indices, improving performance in wearable systems.
Contribution
The novelty lies in using data quality indices to strategically select sensor channels for enhanced gesture recognition accuracy.
Findings
ML-based channel selection achieved 76.36% accuracy for sEMG and 71.59% for pFMG on the UOW dataset.
Strategically selected sEMG-pFMG channels achieved 88.2% accuracy, comparable to a full eight-channel sEMG system.
The proposed methods outperformed random selection across multiple datasets.
Abstract
This study proposes a data quality-driven channel selection methodology to improve hand gesture recognition performance in multi-channel wearable Human–Machine Interface (HMI) systems. The methodology centers around calculating (i) five data quality indices for both surface electromyography (sEMG) and pressure-based force myography (pFMG) signals and (ii) establishing a relationship between these data quality indices and the accuracy of gesture recognition for applications typified by prosthetic hand control. Machine learning (ML)-based and correlation-based methods were used to select three optimal channel/pair configurations from an eight-channel/pair system. Evaluations on the UOW and Ninapro DB2 datasets showed that the proposed methods consistently outperformed random channel selection, with the ML-based approach achieving the best results (76.36% for sEMG, 71.59% for pFMG, and…
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Taxonomy
TopicsMuscle activation and electromyography studies · Advanced Sensor and Energy Harvesting Materials · Hand Gesture Recognition Systems
