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.
Highlights
One of the goals of pervasive computing is to create Ambient Intelligence, in other words, to make environments sensitive to the presence of human beings (Cook et al, 2009)
The term Ambient Assisted Living (AAL) has been used to designate a multidisciplinary research area focused on the development of intelligent systems for remote monitoring of the daily activities of patients (ADL – Activities of Daily Living) transiting through intelligent environments, such as Smart Homes (Memon et al, 2014)
The aim of this article is to present the architecture, functionalities and evaluation results of MHARS (Mobile Human Activity Recognition System), an AAL system aimed at the recognition of user activities and measurement of their respective intensi
Summary
One of the goals of pervasive computing is to create Ambient Intelligence, in other words, to make environments sensitive to the presence of human beings (Cook et al, 2009). The term Ambient Assisted Living (AAL) has been used to designate a multidisciplinary research area focused on the development of intelligent systems for remote monitoring of the daily activities of patients (ADL – Activities of Daily Living) transiting through intelligent environments, such as Smart Homes (Memon et al, 2014). Patient monitoring by means of AAL systems differs from the traditional healthcare model. In the latter, it is necessary that patients periodically visit hospitals for medical evaluation. With the aid of AAL systems, healthcare professionals can follow the situation of the patient in real-time and quickly make decisions and perform actions to improve or stabilize the clinical situation
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