Bi-Modal Detection of Painful Reaching for Chronic Pain Rehabilitation Systems

Abstract

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 high pain classification F1 scores 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.

Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity

“…guarding). Among the most recent works are the studies of [39] and [40] [41]. Using, motion capture sensors mounted on screws inserted in the spine during surgery, [39] automatically classified 11 points of pain intensities of people with chronic pain with maximum error of 0.25 points.…”

“…Using, motion capture sensors mounted on screws inserted in the spine during surgery, [39] automatically classified 11 points of pain intensities of people with chronic pain with maximum error of 0.25 points. Using less invasive wearable motion capture and muscle sensors, [40][41] classified two levels of pain within the chronic pain group in addition to a healthy control group with a mean accuracy of 0.87 over three sets of physical exercise types. Though these works do not investigate MRSE, they provide sufficient evidence of the informative power of body movement features for assessing subjective experiences.…”

“…We used a multi-layer SVM similar to [40][41] such that a first SVM, SVM1, was used to discriminate between high MRSE and lower levels of MRSE and a second SVM, SVM2, was used to further differentiate the latter as either low or medium MRSE. To deal with class imbalance with the SVM, we set the regularization parameter C to be rescaled such that it is higher for the minority class, forcing the hyperplane away from this class [52].…”

Go-with-the-Flow: Tracking, Analysis and Sonification of Movement and Breathing to Build Confidence in Activity Despite Chronic Pain

“…At the same time, our results showed that external representation by sound can enhance patients' understanding of their own movements and breathing patterns (if embodied), and help with providing personalized explanations and advice, facilitating pacing and goal-setting. The supervisory support by the device could be further enhanced by using functionalities to automatically detect increased pain or more subtle cues of fear of pain from body cues (Aung et al, in press;Olugbade, Aung, Marquardt, De C. Williams, & Bianchi-Berthouze, 2014 and from facial expressions (Hammal & Cohn, 2012;Kaltwang, Rudovic, & Pantic, 2012;Meng & Bianchi-Berthouze, 2014;Romera-Paredes et al, 2013) and suggest or guide recalibration. Indeed, in a recent follow-up study we carried out on sensing wearable devices, people with CP confirmed the role of technology as a support to learning supervision skills and even to share such the supervisory role in real-life situation where the task at hand requires much attention (Felipe, Singh, Bradley, Williams, & Bianchi-Berthouze, 2015).…”

Learning Temporal and Bodily Attention in Protective Movement Behavior Detection

“…Recorded activities included: One-leg-stand, Stand-to-sit, Sit-to-stand, Reach-forward and Bend -typical everyday activities that are generally challenging for those with CLBP [7] [9]. Previous studies based on Mocap and sEMG data of the EmoPain dataset mainly employed vanilla neural networks [15] and feature engineering approaches [6] [13] [14], where the dynamic biomechanics of movements are only used to guide feature design. Unlike acute rehabilitation where a goldstandard movement trajectory (and its deviation) informs intervention, in chronic pain, fear of injury, fear of pain, and anxiety lead the person to engage body parts in ways that are not biomechanically necessary or efficient but may increase sense of control and reduce fear.…”