A Probabilistic Model of Human Activity Recognition with Loose Clothing †

Abstract

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by (Formula presented.) more than rigid-attached sensors when the window size is (Formula presented.) (Formula presented.). Simulated and real human motion capture experiments with several participants confirm the model’s predictions, demonstrating that this counterintuitive effect is accurately captured.