A multipurpose smart activity monitoring system for personalized health services

Park, Keehyun; Lim, SeungHyeon

doi:10.1016/j.ins.2014.10.036

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…In [12][13][14][15], web-based remote PHD management systems for activity monitors, a medication dispenser, and pulse oximeters were proposed and constructed. In terms of communication protocols, ISO/IEEE 11073 and OMA DM (Open Mobile Alliance Data Management) protocols were used to transmit measured data or remote commands.…”

Section: Related Studiesmentioning

confidence: 99%

“…Personal Healthcare Devices (PHDs) are portable electronic healthcare devices that sense and measure users' biomedical signals. Activity monitors, medication dispensers, pulse oximeters, ECG monitors, blood pressure monitors and falling detectors are representative examples of PHDs [10][11][12][13][14][15]. PHDs could be objects in an IoT system for health care, and healthcare workers could enjoy most of the advantages of an IoT system by monitoring patients at home via IoT systems.…”

Section: Introductionmentioning

confidence: 99%

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An IoT System for Remote Monitoring of Patients at Home

Park

Lee

2017

Applied Sciences

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Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To make it operable in IoT environments, a protocol conversion scheme between ISO/IEEE 11073 protocol and oneM2M protocol, and a Multiclass Q-learning scheduling algorithm based on the urgency of biomedical data delivery to medical staff are proposed. In addition, for the sake of patients' privacy, two security schemes are proposed-the separate storage scheme of data in parts and the Buddy-ACK authorization scheme. The experiment on the constructed system showed that the system worked well and the Multiclass Q-learning scheduling algorithm performs better than the Multiclass Based Dynamic Priority scheduling algorithm. We also found that the throughputs of the Multiclass Q-learning scheduling algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the Multiclass Based Dynamic Priority algorithm increase with decreases in the increasing ratio.

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Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An IoT System for Remote Monitoring of Patients at Home

Park

Lee

2017

Applied Sciences

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“…As stated in Woodside et al, model input parameters (such as service center demands, CPU requests, and call frequencies) can be estimated by measurements, or are based on the documentation plus expertise, or are observed from similar existing systems. In this paper, the values have been derived from the analysis of similar case studies in literature . We also set the probabilities of routing for the requests to the following values: in RouterVD (see Figure ) 70% of requests terminate their computation since there are no alerts on patient's parameters, whereas 30% of requests are sent back to the HC to trigger further analyses.…”

Section: Qos‐based Feedback In Practicementioning

confidence: 99%

“…In this paper, the values have been derived from the analysis of similar case studies in literature. [18][19][20][21][22] We also set the probabilities of routing for the requests to the following values: in RouterVD (see Figure 3) 70% of requests terminate their computation since there are no alerts on patient's parameters, whereas 30% of requests are sent back to the HC to trigger further analyses. These last requests are then forwarded by means of RouterHC (see Figure 3) where 80% of requests are delivered to the First Aid, and 20% are instead routed to the hospital.…”

Section: Qos-based Feedback In Practicementioning

confidence: 99%

Building design‐time and run‐time knowledge for QoS‐based component assembly

2017

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Summary Modern software systems are required to dynamically adapt to changing workloads, scenarios, and objectives and to achieve a certain Quality of Service (QoS). Guaranteeing QoS requirements is not trivial, as run‐time uncertainty might invalidate the design‐time rationale, where software components have been selected by means of off‐line analysis. In this work, we propose a QoS‐based feedback approach that makes a combined use of design‐time predictions and run‐time measurements to manage QoS data over time and support software architects while selecting software components that best fit QoS requirements. We illustrate the feasibility and efficacy of the approach on a case study, where the quantitative evaluation shows how the analysis effectively identifies the sources of QoS violations and indicates possible solutions to achieve QoS requirements.

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“…In recent years, PHDs (Personal Healthcare Devices) [1][2][3][4][5] have become popular mobile devices, and as a result remote PHD management/monitoring systems have been attracting attention in the u-healthcare field [6][7][8][9][10][11]. Some studies have proposed a hierarchical system for managing/monitoring a very large number of PHDs or objects [11][12].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Performance of a Multi-layered Management System

Park

Park²,

Woo³

2015

Advanced Science and Technology Letters

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Abstract. In recent years, PHDs (Personal Healthcare Devices) have become popular mobile devices, and as a result remote PHD management/monitoring systems have been attracting attention in the u-healthcare field. Some studies have proposed a hierarchical system for managing/monitoring a very large number of PHDs or objects. The purpose of this study is to measure the performance of the hierarchical management system for a large number of PHDs. In [11], a multi-layered system was constructed for the concept of the hierarchical PHD management system. The system performance is measured based on the number of layers used by the system uses. Through some experiments conducted in this study, it is found that the layered system proposed in [11] is useful for a PHD management system with a very large number of PHDs.

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A multipurpose smart activity monitoring system for personalized health services

Cited by 14 publications

References 12 publications

An IoT System for Remote Monitoring of Patients at Home

An IoT System for Remote Monitoring of Patients at Home

Building design‐time and run‐time knowledge for QoS‐based component assembly

A Study on the Performance of a Multi-layered Management System

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