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In the framework of health remote monitoring applications for individuals with disabilities or particular pathologies, quantity and type of physical activity performed by an individual/patient constitute important information. On the other hand, the technological evolution of Smartphones, combined with their increasing diffusion, gives mobile network providers the opportunity to offer real-time services based on captured real world knowledge and events. This paper presents a Smartphone-based Activity Recognition (AR) method based on decision tree classification of accelerometer signals to classify the user’s activity as Sitting, Standing, Walking or Running. The main contribution of the work is a method employing a novel windowing technique which reduces the rate of accelerometer readings while maintaining high recognition accuracy by combining two single-classification weighting policies. The proposed method has been implemented on Android OS smartphones and experimental tests have produced satisfying results. It represents a useful solution in the aforementioned health remote applications such as the Heart Failure (HF) patients monitoring mentioned below.

SMARTPHONE-BASED USER ACTIVITY RECOGNITION METHOD FOR HEALTH REMOTE MONITORING APPLICATIONS