Rehabilitation from cardiovascular disease (CVD) usually requires lifestyle changes, especially an increase in exercise and physical activity. However uptake and adherence to exercise is low for community based programmes. We propose a mobile application that allows users to choose the type of exercise and compete it at a convenient time in the comfort of their own home. Grounded in a behaviour change framework, the application provides feedback and encouragement to continue exercising and to improve on previous results. The application also utilizes wearable wireless technologies in order to provide highly personalized feedback. The application can accurately detect if a specific exercise is being done, and count the associated number of repetitions utilizing accelerometer or gyroscope signalsMachine learning models are employed to recognize individual local muscular endurance (LME) exercises, achieving overall accuracy of more than 98%. This technology allows providing a near real-time personalized feedback which mimics the feedback that the user might expect from an instructor. This is proved to motivate users to continue the recovery process.

The identification of different meat cuts for labelling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7987 meats cuts extracted from Semimembranosus muscles (i.e., Topside muscle), post-editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions); precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network (CNN) and residual network (ResNET). The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.

The third phase of the recovery from cardiovascular disease (CVD) is an exercise-based rehabilitation programme. However, adherence to an exercise regime is typically not maintained by the patient for a variety of reasons such as lack of time, financial constraints, etc. In order to facilitate patients to perform their exercises from the comfort of their home and at their own convenience, we have developed a mobile application, termed MedFit. It provides access to a tailored suite of exercises along with easy to understand guidance from audio and video instructions. Two types of wearable sensors are utilized to allow motivational feedback to be provided to the user for self monitoring and to provide near real-time feedback. Fitbit, a commercially available activity and fitness tracker, is used to provide in-depth feedback for self-monitoring over longer periods of time (e.g. day, week, month), whereas the Shimmer wireless sensing platform provides the data for near real-time feedback on the quality of the exercises performed. MedFit is a simple and intuitive mobile application designed to provide the motivation and tools for patients to help ensure faster recovery from the trauma caused by CVD. In this paper we describe the MedFit application as a demo submission to the 2 nd MMHealth Workshop at ACM MM 2017.

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