Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone

Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine)

Promoting Behaviour Change in Long term Conditions using a Self-Management Platform

Physical rehabilitation of brain injuries and strokes is a time consuming and costly process. Over the past decade several studies have emerged looking at the use of highly sophisticated technologies, such as robotics and virtual reality to tap into the needs of clinicians and patients. While such technologies can be a valuable tool to facilitate intensive movement practice in a motivating and engaging environment, success of therapy also depends on self-administered therapy beyond hospital stay. With the emergence of low-cost gaming consoles such as the Nintendo Wii, new opportunities arise for home-therapy paradigms centred on social interactions and values, which could reduce the sense of isolation and other depression related complications. In this paper we examine the potential, user acceptance and usability of an unmodified Nintendo Wii gaming console as a low-cost treatment alternative to complement current rehabilitation programmes