TagCare: Using RFIDs to Monitor the Status of the Elderly Living Alone

Zhu, Licai; Wang, Ruchuan; Wang, Zhongqin; Yang, Hao

doi:10.1109/access.2017.2716359

Cited by 30 publications

(13 citation statements)

References 23 publications

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“…The proposed method was based on the change in the power level for different human activities and no qualitative results were discussed. Another fall detection prototype based on RFID technology where the design and implementation of a motion detection system based on passive radio frequency identification tags known as TagCare was presented in [75]. The main findings of this work were Doppler frequency was exploited to detect human activities and predict fall event.…”

Section: Rfid For Fall Detectionmentioning

confidence: 99%

See 1 more Smart Citation

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

Shah

Fioranelli

2019

IEEE Aerosp. Electron. Syst. Mag.

131

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Section: Rfid For Fall Detectionmentioning

confidence: 99%

“…Falling is one of the major health threats to the independent living of elders, and falling has received much attention in academia and industry over the past two decades [75]. Many systems have been introduced in the healthcare sector to detect fall events in elderly patients.…”

Section: Rfid For Fall Detectionmentioning

confidence: 99%

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

Shah

Fioranelli

2019

IEEE Aerosp. Electron. Syst. Mag.

131

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“…The data collected are multimodal (physiological, actimetric, mechanical) and thus allow more thorough analysis for both prevention and detection of falls [9,10,23,24,25,26,27,28,29,30,31,32]. Different types of connections are possible, such as wearable sensors connected to an app via a smartphone.…”

Section: The Era Of Fall Detection and Prevention Devices For Elders Living At Homementioning

confidence: 99%

Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality? (Preprint)

Pech¹,

Sauzéon²,

Yebda³

et al. 2021

Preprint

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UNSTRUCTURED There is an exponential increase in the range of digital products and devices promoting aging in place in particular devices aiming at preventing or detecting falls. However, their deployment is still limited and few studies have been carried out in population-based settings. Such a matter of fact is due to the technological challenges that remain to overcome but also to the barriers that are specific to the users themselves such as the generational digital divide and acceptability factors specific to the elderly population. To date, scarce studies take into account these factors. In order to capitalize technological progress, the further step should be to better take into account these factors and to deploy, in a broader and more ecological way, these technologies designed for home care seniors, in order to assess their effectiveness in real life.

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“…The main advantage of these devices is that they are non-invasive and comfortable for users. Concerning detection and prevention of falls, different types of WS and IoT-based applications have been developed for older adults' care at home [10], [11], [12], [13], [14], [15], [16]. First, there is a smart and connected home health monitoring system [11].…”

Section: Related Work 21 It For Support Of Independent Livingmentioning

confidence: 99%

“…Another study reported the development of a novel passive Radio Frequency Identification using Doppler Frequency for fall detection. "Tagcare", the monitoring system's prototype has shown a high accuracy (98%) for movement detection and sudden falling [14]. Furthermore, wrist-worn wearables have been developed to improve fall detection [10]- [15].…”

Section: Related Work 21 It For Support Of Independent Livingmentioning

confidence: 99%

Detection of Semantic Risk Situations in Lifelog Data for Improving Life of Frail People

Yebda

Benois‐Pineau

Pech

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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The automatic recognition of risk situations for frail people is an urgent research topic for the interdisciplinary artificial intelligence and multimedia community. Risky situations can be recognized from lifelog data recorded with wearable devices. In this paper, we present a new approach for the detection of semantic risk situations for frail people in lifelog data. Concept matching between general lifelog and risk taxonomies was realized and tuned AlexNet was deployed for detection of two semantic risks situations such as risk of domestic accident and risk of fraud with promising results. CCS CONCEPTS • Applied computing → Health care information systems; • Computing methodologies → Neural networks.

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TagCare: Using RFIDs to Monitor the Status of the Elderly Living Alone

Cited by 30 publications

References 23 publications

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality? (Preprint)

Detection of Semantic Risk Situations in Lifelog Data for Improving Life of Frail People

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