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Learning Bodily and Temporal Attention in Protective Movement Behavior Detection

Wang, C; Peng, M; Olugbade, TA; Lane, ND; Williams, ACDC; Bianchi-Berthouze, N; (2019) Learning Bodily and Temporal Attention in Protective Movement Behavior Detection. In: Proceedings of 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). (pp. pp. 324-330). IEEE: Cambridge, UK. Green open access

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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.

Type: Proceedings paper
Title: Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
Event: 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ACIIW.2019.8925084
Publisher version: https://doi.org/ 10.1109/ACIIW.2019.8925084
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
URI: https://discovery.ucl.ac.uk/id/eprint/10078208
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