# Utilizing Data Quality Indices for Strategic Sensor Channel Selection to Enhance Performance of Hand Gesture Recognition Systems

## 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.

## Key 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 88.2% for fused sEMG-pFMG on the UOW dataset and 70.28% on Ninapro DB2). Notably, using three pairs of strategically selected sEMG-pFMG channels generated 88.2%, which is comparable to the 88.38% accuracy obtained with a full eight-channel sEMG system on the UOW dataset, highlighting the efficacy of our channel selection methodologies. These results highlight the value of data quality indices for sensor selection and provide a foundation for developing more efficient wearable HMI systems.