Paper Info
Title
Recurrent network based automatic detection of chronic pain protective behavior using MoCap and sEMG data
Abstract
In chronic pain physical rehabilitation, physiotherapists adapt exercise sessions according to the movement behavior of patients. As rehabilitation moves beyond clinical sessions, technology is needed to similarly assess movement behaviors and provide such personalized support. In this paper, as a first step, we investigate automatic detection of protective behavior (movement behavior due to pain-related fear or pain) based on wearable motion capture and electromyography sensor data. We investigate two recurrent networks (RNN) referred to as stacked-LSTM and dual-stream LSTM, which we compare with related deep learning (DL) architectures. We further explore data augmentation techniques and additionally analyze the impact of segmentation window lengths on detection performance. The leading performance of 0.815 mean F1 score achieved by stacked-LSTM provides important grounding for the development of wearable technology to support chronic pain physical rehabilitation during daily activities.
Year
DOI
Venue
2019
10.1145/3341163.3347728
Proceedings of the 23rd International Symposium on Wearable Computers
Keywords
Field
DocType
affective behavior, physical rehabilitation, recurrent networks
Chronic pain,Computer vision,Affective behavior,Computer science,Artificial intelligence,Physical medicine and rehabilitation
Conference
ISBN
Citations 
PageRank 
978-4503-6870-4
4
0.43
References 
Authors
0
6
Name
Order
Citations
PageRank
Chongyang Wang153.14
Temitayo A. Olugbade2446.14
Akhil Mathur310115.10
Amanda Williams412611.24
Nicholas D. Lane54247248.15
Nadia Bianchi-Berthouze6123998.61