Remote health monitoring of elderly through wearable sensors

Al-Khafajiy, Mohammed; Baker, Thar; Chalmers, Carl; Asim, Muhammad; Kolivand, Hoshang; Fahim, Muhammad; Waraich, Atif

doi:10.1007/s11042-018-7134-7

Cited by 276 publications

(120 citation statements)

References 43 publications

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“…Al-Khafajiy et al. [ 39 ] presented a smart healthcare monitoring system capable of observing elderly people remotely. In [ 40 ], an elderly health care (EHC) system for covering the real health needs of the elderly was introduced.…”

Section: Related Workmentioning

confidence: 99%

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

Chen

Saiki

Nakamura

2020

Sensors

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In contrast to the physical activities of able-bodied people at home, most people who require long-term specific care (e.g., bedridden patients and patients who have difficulty walking) usually show more low-intensity slow physical activities with postural changes. Although the existing devices can detect data such as heart rate and the number of steps, they have been increasing the physical burden relying on long-term wearing. The purpose of this paper is to realize a noninvasive fine-grained home care monitoring system that is sustainable for people requiring special care. In the proposed method, we present a novel technique that integrates inexpensive camera devices and bone-based human sensing technologies to characterize the quality of in-home postural changes. We realize a local process in feature data acquisition once per second, which extends from a computer browser to Raspberry Pi. Our key idea is to regard the changes of the bounding box output by standalone pose estimation models in the shape and distance as the quality of the pose conversion, body movement, and positional changes. Furthermore, we use multiple servers to realize distributed processing that uploads data to implement home monitoring as a web service. Based on the experimental results, we conveyed our findings and advice to the subject that include where the daily living habits and the irregularity of home care timings needed improvement.

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Section: Related Workmentioning

confidence: 99%

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

Chen

Saiki

Nakamura

2020

Sensors

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“…An IoT-based health care monitoring system has been proposed in [22]. Its aim is to provide home-based health care monitoring.…”

Section: Related Workmentioning

confidence: 99%

HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services

et al. 2019

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Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.

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“…Wireless medical Communications using IEEE 802. 15 monitoring using wireless network is expressed as wireless medical sensor networks (WMSNs) [7]. Some medical applications require secured transmission of the patient's health condition.…”

Section: Literature Surveymentioning

confidence: 99%

“…The body area network field with internet could allow less expensive real-time updates of continuous health monitoring. Computer-assisted rehabilitation or early detection of medical conditions could be possible with wearable wireless body area network [15] which includes integration of different intelligent physiological sensors. Fig.…”

Section: Introductionmentioning

confidence: 99%

IoT based Sensor Fusion Algorithm for Online Smart Health Monitoring

Vijayalakshmi¹,

B²,

Stephen³

2019

IJRTE

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Emerging Internet of Things technology plays the major role in modern healthcare not only for sensing but also in recording, communication and display results. The major role of an intensive care unit (ICU) is to improve patient health such as bringing about a change in the treatment or move the patient to a step-down unit etc. Monitoring also shows the extent of observance with a formulated standard of care. In ICU, care should be taken to monitor medical parameters, such as EEG, EMG, BP etc , continuously. In recent health care applications such as real time human health condition monitoring, patient information management etc, IoT technology brings convenience of general practitioner and human, since it is applied in various medical areas, the Body Sensor Network (BSN) is one of the main technology of IoT based medical applications, where a tiny smart and lightweight wireless sensor nodes are used for monitoring patient’s health condition. Hence, this paper proposes BSN integrated with IoT based sensor fusion algorithm to save human life those who are in critical condition. Sensor fusion algorithm is used to detect the criticality of the patient’s health condition and IoT technology is used for communicating information. The testbed has been developed using Rasberry Pi controller, EMG sensor,, BP sensor etc and tested. The tested results also analyzed.

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Remote health monitoring of elderly through wearable sensors

Cited by 276 publications

References 43 publications

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services

IoT based Sensor Fusion Algorithm for Online Smart Health Monitoring

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