NDLI: A movement activity recognition pervasive system for patient monitoring in ambient assisted living

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A movement activity recognition pervasive system for patient monitoring in ambient assisted living

Content Provider	ACM Digital Library
Author	Pereira, José Daniel Endler, Markus Coutinho, Luciano Reis de Tácio Pereira Gomes, Berto da Silva e Silva, Francisco José
Abstract	This paper presents the main features and architecture of MHARS (Mobile Human Activity Recognition System), a pervasive system for monitoring the daily movement activities of patients in the context of Ambient Assisted Living. MHARS is based on smartphones, and allows data gathering from the different sensors usually available on this class of devices, as well as from external sensors, either the ones comprising a body sensor network as well as from ambient deployed sensors. MHARS uses accelerometer data for inferring the patient performed activity and her/his heart rate for computing its intensity. It also allows to correlate the inferred data with other context data (such as altitude, body and environment temperature, etc) for detecting user defined situations related with the patient current health status and provides a decision making engine for defining an action plan (set of actions) that must be executed whenever relevant health situations are detected. This paper emphasizes the MHARS service responsible for inferring the patient movement activity. We present a comprehensive set of experiments used for building the movement activity classifier and the evaluation of its performance.
Starting Page	155
Ending Page	161
Page Count	7
File Format	PDF
ISBN	9781450337397
DOI	10.1145/2851613.2851689
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-04-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ambient assisted living Human movement activity recognition Mobile computing
Content Type	Text
Resource Type	Article

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