Chronic Pain Protective Behavior Detection with Deep Learning

Autor: Chongyang Wang, Nadia Bianchi-Berthouze, Amanda C. de C. Williams, Nicholas D. Lane, Akhil Mathur, Temitayo A. Olugbade
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
medicine.medical_specialty
Computer Science - Artificial Intelligence
medicine.medical_treatment
Computer Science - Human-Computer Interaction
Biomedical Engineering
Medicine (miscellaneous)
Wearable computer
Health Informatics
02 engineering and technology
Motion capture
Human-Computer Interaction (cs.HC)
Machine Learning (cs.LG)
03 medical and health sciences
0302 clinical medicine
Physical medicine and rehabilitation
Health Information Management
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
medicine
Affective computing
Rehabilitation
business.industry
Deep learning
Chronic pain
Body movement
medicine.disease
Computer Science Applications
Artificial Intelligence (cs.AI)
Artificial intelligence
Psychology
business
F1 score
030217 neurology & neurosurgery
Software
Information Systems
Zdroj: ACM Transactions on Computing for Healthcare
ISSN: 2637-8051
2691-1957
Popis: In 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 paper, 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 modelled per activity type, performance is 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.
24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcare
Databáze: OpenAIRE
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