Learning Bodily and Temporal Attention in Protective Movement Behavior Detection

TL;DR

This paper introduces BodyAttentionNet, a deep learning model that uses bodily and temporal attention mechanisms to improve detection of protective movement behaviors in people with chronic pain, aiding targeted interventions.

Contribution

The paper presents a novel attention-based deep learning architecture, BANet, that effectively captures informative cues for protective behavior detection with fewer parameters.

Findings

BANet outperforms existing methods in accuracy.

BANet requires fewer parameters than comparable models.

Statistically significant improvements in protective behavior detection.

Abstract

For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative temporal and body configurational cues characterizing specific movements and the strategies used to perform them. We propose an end-to-end deep learning architecture named BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts that are more informative to the detection of protective behavior. The approach addresses the variety of ways people execute a movement (including healthy people) independently of the type of movement analyzed. Through extensive comparison experiments with other state-of-the-art machine learning techniques used with motion capture data, we show statistically significant improvements achieved by using these attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state of the art for comparable if not higher performances.