Patient-Buddy-Build: Customized Mobile Monitoring for Patients With Chronic Diseases

Almeida, Vitor Pinheiro de; Endler, Markus; Hæusler, Edward Hermann

doi:10.17771/pucrio.acad.28691

Cited by 2 publications

References 18 publications

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A framework for customizing the mobile and remote monitoring of patients with chronic diseases

Almeida

Endler

Hæusler

2014

2014 IEEE 16th International Conference on E-Health Networking, Applications and Services (Healthcom)

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We present a means of generating a mobile clientserver application that enables customized remote monitoring of patients with chronic diseases. The customization is based on parameters and formal descriptions of: type of chronic disease, patient preferences, the monitoring procedure required by the doctor, prescribed medication and information about the current context (i.e., environment) of the patient, obtained from sensors. Based on this, the system determines which pieces of information should be regularly obtained from the patient through questionnaires and/or sensors of the smart phone and sensors connected to it. Relevant information are the ones that help to detect possible changes in the patient's health condition and the monitoring process of a patient by the doctor. The medical treatment and the kind of chronic disease will define the set of data to be collected. It should be stressed that the goal is not to support automatic medical diagnosis, but only to provide means for physicians to obtain updated information about their patients, so as to allow remote monitoring of patients.

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A framework for customizing the mobile and remote monitoring of patients with chronic diseases

Almeida

Endler

Hæusler

2014

2014 IEEE 16th International Conference on E-Health Networking, Applications and Services (Healthcom)

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

Filho

Silva²,

Coutinho³

et al. 2016

Journal of Applied Computing Research

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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.

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Patient-Buddy-Build: Customized Mobile Monitoring for Patients With Chronic Diseases

Cited by 2 publications

References 18 publications

A framework for customizing the mobile and remote monitoring of patients with chronic diseases

A framework for customizing the mobile and remote monitoring of patients with chronic diseases

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

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