Single inertial sensor for local muscular endurance (LME) exercise detection

An activity recognition framework based on machine learning to automatically recognize LME exercises and to count the repetitions using a wrist-worn inertial sensor is proposed. Fourteen binary classifiers are trained using optimized SVM models [1, 3] to recognize individual LME exercises, achieving overall accuracy of more than 98%

Recognition and repetition counting for local muscular endurance exercises in exercise-based rehabilitation: a comparative study using artificial Intelligence models

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g. their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutional Neural Networks (CNNs). We investigated CNN models based on the AlexNet architecture and found that the performance was better than the traditional approaches, for exercise recognition (overall F1-score of 97.18%) and repetition counting (±1 error among 90% observed sets). To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. Also, we make the INSIGHT-LME dataset publicly available to encourage further research

MedFit: a mobile application for recovering CVD patients

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 2nd MMHealth Workshop at ACM MM 2017

Single inertial sensor for local muscular endurance (LME) exercise detection

An activity recognition framework based on machine learning to automatically recognize LME exercises and to count the repetitions using a wrist-worn inertial sensor is proposed. Fourteen binary classifiers are trained using optimized SVM models [1, 3] to recognize individual LME exercises, achieving overall accuracy of more than 98%

Design and development of the medFit app: a mobile application for cardiovascular disease rehabilitation

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 signals Machine 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 provided to motivate users to continue the recovery process.peer-reviewe