Chronic-Pain Protective Behavior Detection with Deep Learning

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcar

Learning Bodily and Temporal Attention in Protective Movement Behavior Detection

For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative temporal and body configurational cues characterizing specific movements and the strategies used to perform them. We propose an end-to-end deep learning architecture named BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts that are more informative to the detection of protective behavior. The approach addresses the variety of ways people execute a movement (including healthy people) independently of the type of movement analyzed. Through extensive comparison experiments with other state-of-the-art machine learning techniques used with motion capture data, we show statistically significant improvements achieved by using these attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state of the art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201

Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain

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. / Note: As originally published there is an error in the document. The following information was omitted by the authors: "The project was funded by the EPSRC grant Emotion & Pain Project EP/H017178/1 and Olugbade was supported by the 2012 Nigerian PRESSID PhD funding." The article PDF remains unchanged

The affective body argument in technology design

In this paper, I argue that the affective body is underused in the design of interactive technology despite what it has to offer. Whilst the literature shows it to be a powerful affective communication channel, it is often ignored in favor of the more commonly studied facial and vocal expression modalities. This is despite it being as informative and in some situations even more reliable than the other affective channels. In addition, due to the proliferation of increasingly cheaper and ubiquitous movement sensing technologies, the regulatory affective functions of the body could open new possibilities in various application areas. In this paper, after presenting a brief summary of the opportunities that the affective body offers to technology designers, I will use the case of physical rehabilitation to discuss how its use could lead to interesting new solutions and more effective therapies

Protective Behavior Detection in Chronic Pain Rehabilitation: From Data Preprocessing to Learning Model

Chronic pain (CP) rehabilitation extends beyond physiotherapist-directed clinical sessions and primarily functions in people's everyday lives. Unfortunately, self-directed rehabilitation is difficult because patients need to deal with both their pain and the mental barriers that pain imposes on routine functional activities. Physiotherapists adjust patients' exercise plans and advice in clinical sessions based on the amount of protective behavior (i.e., a sign of anxiety about movement) displayed by the patient. The goal of such modifications is to assist patients in overcoming their fears and maintaining physical functioning. Unfortunately, physiotherapists' support is absent during self-directed rehabilitation or also called self-management that people conduct in their daily life. To be effective, technology for chronic-pain self-management should be able to detect protective behavior to facilitate personalized support. Thereon, this thesis addresses the key challenges of ubiquitous automatic protective behavior detection (PBD). Our investigation takes advantage of an available dataset (EmoPain) containing movement and muscle activity data of healthy people and people with CP engaged in typical everyday activities. To begin, we examine the data augmentation methods and segmentation parameters using various vanilla neural networks in order to enable activity-independent PBD within pre-segmented activity instances. Second, by incorporating temporal and bodily attention mechanisms, we improve PBD performance and support theoretical/clinical understanding of protective behavior that the attention of a person with CP shifts between body parts perceived as risky during feared movements. Third, we use human activity recognition (HAR) to improve continuous PBD in data of various activity types. The approaches proposed above are validated against the ground truth established by majority voting from expert annotators. Unfortunately, using such majority-voted ground truth causes information loss, whereas direct learning from all annotators is vulnerable to noise from disagreements. As the final study, we improve the learning from multiple annotators by leveraging the agreement information for regularization

A Comprehensive Study on Pain Assessment from Multimodal Sensor Data

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed

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

Clinicians tailor intervention in chronic pain rehabilitation to movement related self-efficacy (MRSE). This motivates us to investigate automatic MRSE estimation in this context towards the development of technology that is able to provide appropriate support in the absence of a clinician. We first explored clinical observer estimation, which showed that body movement behaviours, rather than facial expressions or engagement behaviours, were more pertinent to MRSE estimation during physical activity instances. Based on our findings, we built a system that estimates MRSE from bodily expressions and bodily muscle activity captured using wearable sensors. Our results (F1 scores of 0.95 and 0.78 in two physical exercise types) provide evidence of the feasibility of automatic MRSE estimation to support chronic pain physical rehabilitation. We further explored automatic estimation of MRSE with a reduced set of low-cost sensors to investigate the possibility of embedding such capabilities in ubiquitous wearable devices to support functional activity. Our evaluation for both exercise and functional activity resulted in F1 score of 0.79. This result suggests the possibility of (and calls for more studies on) MRSE estimation during everyday functioning in ubiquitous settings. We provide a discussion of the implication of our findings for relevant areas

Roles for Personal Informatics in Chronic Pain

Self-management of chronic pain is a complex and demanding activity. Multidisciplinary pain management programs are designed to provide patients with the skills to improve, maintain functioning and self-manage their pain but gains diminish in the long-term due to lack of support from clinicians. Sensing technology can be a cost-effective way to extend support for self-management outside clinical settings but they are currently under-explored. In this paper, we report studies carried out to investigate how Personal Informatics Systems (PIS) based on wearable body sensing technology could facilitate pain self-management and functioning. Five roles for PIS emerged from a qualitative study with people with chronic pain and physiotherapists: (i) assessment, planning and prevention (ii) a direct supervisory and co-management role, (iii) facilitating deeper understanding, (iv) managing emotional states, and (v) sharing for social acceptability. A web-based survey was conducted to understand the parameters that should be tracked to support self-management and what tracked information should be shared with others. Finally, we suggest an extension to previous PIS models and propose design implications to address immediate, short-term and long-term information needs for personal use of people with chronic pain and for sharing with others. / Note: As originally published there is an error in the document. The following information was omitted by the authors: "The project was funded by the EPSRC grant Emotion & Pain Project EP/H017178/1 rather than the EPSRC grant EP/G043507/1: Pain rehabilitation: E/Motion-based automated coaching.." The article PDF remains unchanged