Automatic Monitoring of Physical Activity Related Affective States for Chronic Pain Rehabilitation

Chronic pain is a prevalent disorder that affects engagement in valued activities. This is a consequence of cognitive and affective barriers, particularly low self-efficacy and emotional distress (i.e. fear/anxiety and depressed mood), to physical functioning. Although clinicians intervene to reduce these barriers, their support is limited to clinical settings and its effects do not easily transfer to everyday functioning which is key to self-management for the person with pain. Analysis carried out in parallel with this thesis points to untapped opportunities for technology to support pain self-management or improved function in everyday activity settings. With this long-term goal for technology in mind, this thesis investigates the possibility of building systems that can automatically detect relevant psychological states from movement behaviour, making three main contributions. First, extension of the annotation of an existing dataset of participants with and without chronic pain performing physical exercises is used to develop a new model of chronic disabling pain where anxiety acts as mediator between pain and self-efficacy, emotional distress, and movement behaviour. Unlike previous models, which are largely theoretical and draw from broad measures of these variables, the proposed model uses event-specific data that better characterise the influence of pain and related states on engagement in physical activities. The model further shows that the relationship between these states and guarding during movement (the behaviour specified in the pain behaviour literature) is complex and behaviour descriptions of a lower level of granularity are needed for automatic classification of the states. The model also suggests that some of the states may be expressed via other movement behaviour types. Second, addressing this using the aforementioned dataset with the additional labels, and through an in-depth analysis of movement, this thesis provides an extended taxonomy of bodily cues for the automatic classification of pain, self-efficacy and emotional distress. In particular, the thesis provides understanding of novel cues of these states and deeper understanding of known cues of pain and emotional distress. Using machine learning algorithms, average F1 scores (mean across movement types) of 0.90, 0.87, and 0.86 were obtained for automatic detection of three levels of pain and self-efficacy and of two levels of emotional distress respectively, based on the bodily cues described and thus supporting the discriminative value of the proposed taxonomy. Third, based on this, the thesis acquired a new dataset of both functional and exercise movements of people with chronic pain based on low-cost wearable sensors designed for this thesis and informed by the previous studies. The modelling results of average F1 score of 0.78 for two-level detection of both pain and self-efficacy point to the possibility of automatic monitoring of these states in everyday functioning. With these contributions, the thesis provides understanding and tools necessary to advance the area of pain-related affective computing and groundbreaking insight that is critical to the understanding of chronic pain. Finally, the contributions lay the groundwork for physical rehabilitation technology to facilitate everyday functioning of people with chronic pain

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

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

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

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

How Can Affect Be Detected and Represented in Technological Support for Physical Rehabilitation?

Although clinical best practice suggests that affect awareness could enable more effective technological support for physical rehabilitation through personalisation to psychological needs, designers need to consider what affective states matter and how they should be tracked and addressed. In this paper, we set the standard by analysing how the major affective factors in chronic pain (pain, fear/anxiety, and low/depressed mood) interfere with everyday physical functioning. Further, based on discussion of the modality that should be used to track these states to enable technology to address them, we investigated the possibility of using movement behaviour to automatically detect the states. Using two body movement datasets on people with chronic pain, we show that movement behaviour enables very good discrimination between two emotional distress levels (F1=0.86), and three pain levels (F1=0.9). Performance remained high (F1=0.78 for two pain levels) with a reduced set of movement sensors. Finally, in an overall discussion, we suggest how technology-provided encouragement and awareness can be personalised given the capability to automatically monitor the relevant states, towards addressing the barriers that they pose. In addition, we highlight movement behaviour features to be tracked to provide technology with information necessary for such personalisation