Remote monitoring of elderly or partially disabled people living in their homes through the measurement of environmental variables

Abeledo, Maria Claudia; Bruschetti, Fabio; Aguilera, Gastón; Iriso, Pedro; Marsicano, Matias; Lacapmesure, Aldana

doi:10.1109/cacidi.2016.7785993

Cited by 4 publications

(4 citation statements)

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“…In the included work, stoves/ovens and water kettles, which can indicate nutrition activities, were of particular interest [ 33 , 47 , 62 , 73 ]. Besides PIR motion sensors, video cameras and air-relevant sensors also monitored room areas [ 34 , 35 , 48 , 56 ]. Some research designed customized sensors to monitor the operations of some specific objects.…”

Section: Resultsmentioning

confidence: 99%

“…A bed sensor, as an integrated sensor system, successfully delivered heart rate and respiration rate [ 52 ]. In room areas, environmental sensors and microphones measured environmental parameters such as gas concentration and sound level, which were used for in-door positioning [ 48 , 56 ].…”

Section: Resultsmentioning

confidence: 99%

“…Emergency monitoring relied on video/images, presence detection, and abnormal detection of harmful gas concentrations [ 29 , 34 , 35 , 61 ]. Safety and security monitoring used environmental data and presence at the entry door [ 38 , 43 , 48 , 70 ]. Only one paper covers the social interaction, in which the phone and computer usage was monitored as the indicator [ 50 ].…”

Section: Resultsmentioning

confidence: 99%

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Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Wang

Spicher

Warnecke

et al. 2021

Sensors

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With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Wang

Spicher

Warnecke

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It helps in establishing a diagnosis and provides reminder and alert to clinicians when new patterns in a patient's data are discovered (Payne 2000). The most promising applications of this technology in healthcare are for diagnosis of chronic and cardiovascular diseases such as heart disease (Abeledo et al 2016), Brain disease (Albert et al 2016), Kidney disease (Ahmad et al 2017) and Diabetic disease (Kumar, Sharma, and Agarwal 2014), as well as monitoring of the activity of elderly people, using intelligent home monitoring devices (Abeledo et al 2016;Mighali et al 2017). It uses context data, in combination with learning algorithms to provide proactive services and highly adaptable context-aware system (Wang and Ahmad 2010).…”

Section: Introductionmentioning

confidence: 99%