Authors:
Igor Bisio
;
Fabio Lavagetto
;
Mario Marchese
and
Andrea Sciarrone
Affiliation:
University of Genoa, Italy
Keyword(s):
Remote Monitoring, Activity Recognition, Accelerometer, Decision Trees, Windowed Decision, Android Smartphones.
Related
Ontology
Subjects/Areas/Topics:
Context
;
Context-Aware Applications
;
Detection and Estimation
;
Digital Signal Processing
;
Mobile and Pervasive Computing
;
Mobile Computing
;
Paradigm Trends
;
Pervasive Health
;
Software Engineering
;
Telecommunications
;
Ubiquitous Computing Systems and Services
Abstract:
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.
(More)