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

Automatic Recognition of Protective Behaviour in Chronic Pain Rehabilitation

Exergames are increasingly being proposed for physical rehabilitation in chronic pain. They can be engaging, fun and can facilitate the setting of targets and evaluating performances through body movement tracking and multimodal feedback. While these attributes are important, it is also essential that psychological factors that lead to avoidance of physical activity are addressed in the game design. Anxiety about increased pain and/or of further damage often causes people to behave in a self-protective manner (e.g., guarding movement) and to avoid particular movements. Protective behaviour may itself cause increased pain or strain. In this paper we investigate the possibility to automatically detect such behavior. Automatic detection of protective behaviour can be used to adapt the exergame at run time to alleviate anxiety and increase treatment efficacy

Development of a Rule Based Prognostic Tool for HER 2 Positive Breast Cancer Patients

International audienceA three stage development process for the production of a hierarchical rule based prognosis tool is described. The application for this tool is specific to breast cancer patients that have a positive expression of the HER 2 gene. The first stage is the development of a Bayesian classification neural network to classify for cancer specific mortality. Secondly, low-order Boolean rules are extracted form this model using an orthogonal search based rule extraction (OSRE) algorithm. Further to these rules additional information is gathered from the Kaplan-Meier survival estimates of the population, stratified by the categorizations of the input variables. Finally, expert knowledge is used to further simplify the rules and to rank them hierarchically in the form of a decision tree. The resulting decision tree groups all observations into specific categories by clinical profile and by event rate. The practical clinical value of this decision support tool will in future be tested by external validation with additional data from other clinical centres