MHARS: A mobile system for human activity recognition and inference of health situations in ambient assisted living

José Daniel Pereira Ribeiro Filho, Francisco José da Silva e Silva, Luciano Reis Coutinho, Berto de Tácio Pereira Gomes

Abstract


This paper presents MHARS (Mobile Human Activity Recognition System), a mobile system designed to monitor patients in the context of Ambient Assisted Living (AAL), which allows the recognition of the activities performed by the user as well as the detection of the activities intensity in real time. MHARS was designed to be able to gather data from different sensors, to recognize the activities and measure their intensity in different user mobility scenarios. The system allows the inference of situations regarding the health status of the patient and provides support for executing actions, reacting to events that deserve attention from the patient’s caregivers and family members. Experiments demonstrate that MHARS presents good accuracy and has an affordable consumption of mobile resources.

Keywords: Ambient Assisted Living, Human Activity Recognition, situation inference, mobile computing.


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This work is licensed under a Creative Commons Attribution 4.0 International License. [updated on August 2016]

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