Automatic Detection of Protective Behavior in Chronic Pain Physical Rehabilitation: A Recurrent Neural Network Approach

Chongyang Wang, Temitayo Olugbade, Akhil Mathur, ACDC Williams, Nic Lane, N Bianchi-Berthouze
in Journal article


In chronic pain physical rehabilitation, physiotherapists adapt movement to
current performance of patients especially based on the expression of
protective behavior, gradually exposing them to feared but harmless and
essential everyday movements. As physical rehabilitation moves outside the
clinic, physical rehabilitation technology needs to automatically detect such
behaviors so as to provide similar personalized support. In this paper, we
investigate the use of a Long Short-Term Memory (LSTM) network, which we call
Protect-LSTM, to detect events of protective behavior, based on motion capture
and electromyography data of healthy people and people with chronic low back
pain engaged in five everyday movements. Differently from previous work on the
same dataset, we aim to continuously detect protective behavior within a
movement rather than overall estimate the presence of such behavior. The
Protect-LSTM reaches best average F1 score of 0.815 with leave-one-subject-out
(LOSO) validation, using low level features, better than other algorithms.
Performances increase for some movements when modelled separately (mean F1
scores: bending=0.77, standing on one leg=0.81, sit-to-stand=0.72,
stand-to-sit=0.83, reaching forward=0.67). These results reach excellent level
of agreement with the average ratings of physiotherapists. As such, the results
show clear potential for in-home technology supported affect-based personalized
physical rehabilitation.