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Publication Detail
Bi-Modal Detection of Painful Reaching for Chronic Pain Rehabilitation Systems
  • Publication Type:
  • Authors:
    Olugbade TA, Aung MSH, Marquardt N, Bianchi-Berthouze N, Williams ACDEC
  • Pagination:
    455, 458
  • Status:
  • Name of conference:
    ICMI '14: 2014 International Conference on Multimodal Interaction
  • Conference place:
    Istanbul, Turkey
  • Conference start date:
  • Conference finish date:
  • Keywords:
    emotion, machine learning, motion capture, electromyography, body movement, pain rehabilitation technology, physical rehabilitation
Physical activity is essential in chronic pain rehabilitation. However, anxiety due to pain or a perceived exacerbation of pain causes people to guard against beneficial exercise. Interactive rehabiliation technology sensitive to such behaviour could provide feedback to overcome such psychological barriers. To this end, we developed a Support Vector Machine framework with the feature level fusion of body motion and muscle activity descriptors to discriminate three levels of pain (none, low and high). All subjects underwent a forward reaching exercise which is typically feared among people with chronic back pain. The levels of pain were categorized from control subjects (no pain) and thresholded self reported levels from people with chronic pain. Salient features were identified using a backward feature selection process. Using feature sets from each modality separately led to pain classification F1 score of 0.63 and 0.69 for movement and muscle activity respectively. However using a combined bimodal feature set this increased to F1 = 0.8.
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