The Community for Technology Leaders
(2015)
to
ISSN:
ISBN:
pp: 243-249
Temitayo A. Olugbade , UCL Interaction Centre University, College London London, United Kingdom
Nadia Bianchi-Berthouze , UCL Interaction Centre University, College London London, United Kingdom
Nicolai Marquardt , UCL Interaction Centre University, College London London, United Kingdom
Amanda C. Williams , Dept. Clinical Psychology University, College London London, United Kingdom
ABSTRACT
People with chronic musculoskeletal pain would benefit from technology that provides run-time personalized feedback and help adjust their physical exercise plan. However, increased pain during physical exercise, or anxiety about anticipated pain increase, may lead to setback and intensified sensitivity to pain. Our study investigates the possibility of detecting pain levels from the quality of body movement during two functional physical exercises. By analyzing recordings of kinematics and muscle activity, our feature optimization algorithms and machine learning techniques can automatically discriminate between people with low level pain and high level pain and control participants while exercising. Best results were obtained from feature set optimization algorithms: 94% and 80% for the full trunk flexion and sit-to-stand movements respectively using Support Vector Machines. As depression can affect pain experience, we included participants' depression scores on a standard questionnaire and this improved discrimination between the control participants and the people with pain when Random Forests were used.
INDEX TERMS
CITATION
Temitayo A. Olugbade, Nadia Bianchi-Berthouze, Nicolai Marquardt, Amanda C. Williams, "Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain", , vol. 00, no. , pp. 243-249, 2015, doi:10.1109/ACII.2015.7344578
REFERENCES
(Ver 3.3 (11022016))