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

Movement Representation Learning for Pain Level Classification

Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is particularly limited. In this paper, we propose a P-STEMR (Parallel Space-Time Encoding Movement Representation) architecture with the aim of addressing this gap and specifically leveraging the higher availability of human activity recognition datasets for pain-level classification. We evaluated and analyzed the architecture using three different datasets across four sets of experiments. We found statistically significant increase in average F1 score to 0.84 for pain level classification with two classes based on the architecture compared with the use of hand-crafted features. This suggests that it is capable of learning movement representations and transferring these from activity recognition based on data captured in lab settings to classification of pain levels with messier real-world data. We further found that the efficacy of transfer between datasets can be undermined by dissimilarities in population groups due to impairments that affect movement behaviour and in motion primitives (e.g. rotation versus flexion). Future work should investigate how the effect of these differences could be minimized so that data from healthy people can be more valuable for transfer learning

Multi-Rater Consensus Learning for Modeling Multiple Sparse Ratings of Affective Behaviour

The use of multiple raters to label datasets is an established practice in affective computing. The principal goal is to reduce unwanted subjective bias in the labelling process. Unfortunately, this leads to the key problem of identifying a ground truth for training the affect recognition system. This problem becomes more relevant in a sparsely-crossed annotation where each rater only labels a portion of the full dataset to ensure a manageable workload per rater. In this paper, we introduce a Multi-Rater Consensus Learning (MRCL) method which learns a representative affect recognition model that accounts for each rater’s agreement with the other raters. MRCL combines a multitask learning (MTL) regularizer and a consensus loss. Unlike standard MTL, this approach allows the model to learn to predict each rater’s label while explicitly accounting for the consensus among raters. We evaluated our approach on two different datasets based on spontaneous affective body movement expressions for pain behaviour detection and laughter type recognition respectively. The two naturalistic datasets were chosen for the different forms of labelling (different in affect, observation stimuli, and raters) that they together offer for evaluating our approach. Empirical results demonstrate that MRCL is effective for modelling affect from datasets with sparsely-crossed multi-rater annotation

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, with grant agreement No. 101002711

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

Guarding and flow in the movements of people with chronic pain: A qualitative study of physiotherapists' observations

BACKGROUND: Among the adaptations of movement consistently associated with disability in chronic pain, guarding is common. Based on previous work, we sought to understand better the constituents of guarding; we also used the concept of flow to explore the description of un/naturalness that emerged from physiotherapists' descriptions of movement in chronic pain. The aim was to inform the design of technical systems to support people with chronic pain in everyday activities. METHODS: Sixteen physiotherapists, experts in chronic pain, were interviewed while repeatedly watching short video clips of people with chronic low back pain doing simple movements; physiotherapists described the movements, particularly in relation to guarding and flow. The transcribed interviews were analysed thematically to elaborate these constructs. RESULTS: Moderate agreement emerged on the extent of guarding in the videos, with good agreement that guarding conveyed caution about movement, distinct from biomechanical variables of stiffness or slow speed. Physiotherapists' comments on flow showed slightly better agreement, and described the overall movement in terms of restriction (where there was no flow or only some flow), of tempo of the entire movement, and as naturalness (distinguished from normality of movement). CONCLUSIONS: These qualities of movement may be useful in designing technical systems to support self-management of chronic pain. SIGNIFICANCE: Drawing on the descriptions of movements of people with chronic low back pain provided by expert physiotherapists to standard stimuli, two key concepts were elaborated. Guarding was distinguished from stiffness (a physical limitation) or slowness as motivated by fear or worry about movement. Flow served to describe harmonious and continuous movement, even when adapted around restrictions of pain. Movement behaviours associated with pain are better understood in terms of their particular function than aggregated without reference to function

A Taxonomy of Noise in Voice Self-reports while Running

Smart earables offer great opportunities for conducting ubiquitous computing research. This paper shares its reflection on collecting self-reports from runners using the microphone on the smart eSense earbud device. Despite the advantages of the eSense in allowing researchers to collect continuous voice self-reports anytime anywhere, it also captured noise signals from various sources and created challenges in data processing and analysis. The paper presents an initial taxonomy of noise in runners’ voice self-reports data via eSense. This is based on a qualitative analysis of voice recordings based on eSense’s microphone with 11 runners across 14 in-the-wild running sessions. The paper discusses the details and characteristics of the observed noise, the challenges in achieving good-quality self-reports, and opportunities for extracting useful contextual information. The paper further suggests a noise-categorization API for the eSense or other similar platforms, not only for the purpose of noise-cancellation but also incorporating the mining of contextual information

Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour

Automatic Detection of Reflective Thinking in Mathematical Problem Solving based on Unconstrained Bodily Exploration

For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinking labels for 26 children solving mathematical problems in unconstrained settings where the body (full or parts) was required to explore these problems. Further, we provide qualitative analysis of behaviours that observers used in identifying reflective thinking moments in these sessions. The body movement cues from our compilation informed features that lead to average F1 score of 0.73 for binary classification of problem-solving episodes by reflective thinking based on Long Short-Term Memory neural networks. We further obtained 0.79 average F1 score for end-to-end classification, i.e. based on raw sensor data. Finally, the algorithms resulted in 0.64 average F1 score for subsegments of these episodes as short as 4 seconds. Overall, our results show the possibility of detecting reflective thinking moments from body movement behaviours of a child exploring mathematical concepts bodily, such as within serious game play