Search

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. [ABSTRACT FROM AUTHOR]

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 article, 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 bodymovement 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. [ABSTRACT FROM AUTHOR]

Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through literature reviews, an overview of how pain is expressed in chronic pain and the motivation for detecting it in physical rehabilitation is provided. Second, a fully labelled multimodal dataset (named ‘EmoPain’) containing high resolution multiple-view face videos, head mounted and room audio signals, full body 3D motion capture and electromyographic signals from back muscles is supplied. Natural unconstrained pain related facial expressions and body movement behaviours were elicited from people with chronic pain carrying out physical exercises. Both instructed and non-instructed exercises were considered to reflect traditional scenarios of physiotherapist directed therapy and home-based self-directed therapy. Two sets of labels were assigned: level of pain from facial expressions annotated by eight raters and the occurrence of six pain-related body behaviours segmented by four experts. Third, through exploratory experiments grounded in the data, the factors and challenges in the automated recognition of such expressions and behaviour are described, the paper concludes by discussing potential avenues in the context of these findings also highlighting differences for the two exercise scenarios addressed. [ABSTRACT FROM PUBLISHER]

Our mental representations of our body are continuously updated through multisensory bodily feedback as we move and interact with our environment. Although it is often assumed that these internal models of body-representation are used to successfully act upon the environment, only a few studies have actually looked at how bodyrepresentation changes influence goal-directed actions, and none have looked at this in relation to body-representation changes induced by sound. The present work examines this question for the first time. Participants reached for a target object before and after adaptation periods during which the sounds produced by their hand tapping a surface were spatially manipulated to induce a representation of an elongated arm. After adaptation, participants' reaching movements were performed in a way consistent with having a longer arm, in that their reaching velocities were reduced. These kinematic changes suggest auditory-driven recalibration of the somatosensory representation of the arm morphology. These results provide support to the hypothesis that one's represented body size is used as a perceptual ruler to measure objects' distances and to accordingly guide bodily actions. [ABSTRACT FROM AUTHOR]

The introduction of full-body controllers has made computer games more accessible and promises to provide a more natural and engaging experience to players. However, the relationship between body movement and game engagement is not yet well understood. In this article, I consider how body movement affects the player's experience during game play. I start by presenting a taxonomy of body movements observed during game play. These are framed in the context of a body of previously published research that is then embedded into a novel model of engagement. This model describes the relationship between the taxonomy of movement and the type of engagement that each class of movement facilitates. I discuss the factors that may inhibit or enhance such relationship. Finally, I conclude by considering how the proposed model could lead to a more systematic and effective use of body movement for enhancing game experience. [ABSTRACT FROM PUBLISHER]

This study investigated the social judgments that are made about people who appear to be in pain. Fifty-six participants viewed 2 video clips of human figures exercising. The videos were created by a motion tracking system, and showed dots that had been placed at various points on the body, so that body motion was the only visible cue. One of the figures displayed pain behaviors (eg, rubbing, holding, hesitating), while the other did not. Without any other information about the person in each video, participants evaluated each person on a variety of attributes associated with interpersonal warmth, competence, mood, and physical fitness. As well as judging them to be in more pain, participants evaluated the person who displayed pain behavior as less warm and less competent than the person who did not display pain behavior. In addition, the person who displayed pain behavior was perceived to be in a more negative mood and to have poorer physical fitness than the person who did not, and these perceptions contributed to the impact of pain behaviors on evaluations of warmth and competence, respectively. The implications of these negative social evaluations for social relationships, well-being, and pain assessment in persons in chronic pain are discussed. [ABSTRACT FROM AUTHOR]