Paper Info
Title
Chronic Pain Protective Behavior Detection with Deep Learning
Abstract
AbstractIn chronic pain rehabilitation, physiotherapists adapt physical activity to patients’ performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this article, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modeled per activity type, performance achieves a mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts’ rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.
Year
DOI
Venue
2021
10.1145/3449068
ACM Transactions on Computing for Healthcare
DocType
Volume
Issue
Journal
2
3
ISSN
Citations 
PageRank 
2691-1957
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Chongyang Wang153.14
Temitayo A. Olugbade200.34
Akhil Mathur310115.10
Amanda Williams412611.24
Nicholas D. Lane54247248.15
Nadia Bianchi-Berthouze600.68