Activity Recognition for Diabetic Patients Using a Smartphone.

Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines machine-learning and symbolic reasoning to recognise high-level lifestyle activities using sensor data obtained primarily from the patient's smartphone. We compare five methods for machine-learning which differ in the amount of manually labelled data by the user, to investigate the trade-off between the labelling effort and recognition accuracy. In an evaluation on real-life data, the highest accuracy of 83.4 % was achieved by the MCAT method, which is capable of gradually adapting to each user.

Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors.

This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The approach first evaluates different sensors and their combinations. After that, multiple regression models are trained utilizing data from different sensors. The EE estimation method designed in this way was evaluated on a dataset containing a wide range of activities. It was compared against three competing state-of-the-art approaches, including the BodyMedia Fit armband, the leading consumer EE estimation device. The results show that the proposed method outperforms the competition by up to 10.2 percentage points.