Evaluating Multi-Sensor Placement and Neural Network Architectures for Physical Activity Level Classification

TL;DR

This study evaluates how sensor placement and neural network architectures affect physical activity level classification accuracy, finding that multi-sensor setups and CNN-LSTM models significantly improve performance for health monitoring.

Contribution

It systematically compares sensor placements and deep learning models, identifying optimal configurations for accurate physical activity classification.

Findings

Adding ankle sensors greatly improves activity classification accuracy.

CNN-LSTM models outperform other neural network architectures.

Multi-sensor setups are statistically more effective than single sensors.

Abstract

Accurate physical activity level (PAL) classification could be beneficial for osteoarthritis (OA) management. This study examines the impact of sensor placement and deep learning models on AL classification using the Metabolic Equivalent of Task values. The results show that the addition of anankle sensor (WA) significantly improves the classification of intensity activities compared to wrist-only configuration(53% to 86.2%). The CNN-LSTM model achieves the highest accuracy (95.09%). Statistical analysis confirms multi-sensor setups outperform single-sensor configurations (p < 0.05). The WA configuration offers a balance between usability and accuracy, making it a cost-effective solution for AL monitoring, particularly in OA management.