Short range wireless solutions enabling ambient assisted living to support people affected by the Down syndrome

Alesii, Roberto; Graziosi, Fabio; Marchesani, Stefano; Rinaldi, Claudia; Santic, Marco; Tarquini, Francesco

doi:10.1109/eurocon.2013.6625006

Cited by 13 publications

(18 citation statements)

References 7 publications

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“…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%

“…Only less than half (

) of the included papers mentioned privacy issues. Cameras were referred to in several papers as invasive monitoring [ 37 , 38 , 40 , 54 ]. Due to the intruding of privacy, some actions were taken regarding sensor selection and sensor placement.…”

Section: Resultsmentioning

confidence: 99%

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Wang

Spicher

Warnecke

et al. 2021

Sensors

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“…Cheaper applications can be designed by using low-cost, off-the-shelf solutions (Adlam and Orpwood 2008;Alesii et al 2013;Chaaraoui et al 2012;Chang and Wang 2010;Doctor et al 2014;Drajsajtl, Struk, and Bednárová 2013;Droes et al 2007;Fernandez-Luque et al 2013;Floeck, Litz, and Rodner 2011;Fuxreiter et al 2010;Grech et al 2010;Helal et al 2012;Hristova, Bernardos, and Casar 2008;James et al 2009;Koch 2006;Magjarevic 2007;Mokhtari et al 2012;Moon et al 2011;Olivier et al 2009;Rocha et al 2013;Rodrigues et al 2011;Sanchez-Pi and Molina 2009;Zhang et al 2014;Zheng et al 2012;Zhou et al 2011), such as motion sensors (Alwan 2009;Bamis et al 2010;Fuxreiter et al 2010;Kropf et al 2012) or web cameras (Elbouz, Alfalou, and Brosseau 2011;Mihailidis, Carmichael, and Boger 2004). A typical example is the one provided by Lopes, Jose, and Aguiar (2011) who described an approach for enabling digital photo frames (which are off-the-shelf by nature) to become generic key devices in the AAL ecosystem.…”

Section: Identified Trends and Future Solutions For The Building Envimentioning

confidence: 99%

Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study

Labonnote

Høyland

2015

Intelligent Buildings International

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Objective: To provide an overview of the state of research concerning smart technologies that support independent living in buildings, as well as a deeper literature review of the specific challenges for the building industry. Methods: A systematic mapping methodology was applied in this study to provide basis knowledge on the state of the art. The initial search on three databases gave 3485 articles. Of these, 610 articles were finally selected as relevant to the study after removing the duplicates and screening by applying inclusion and exclusion criteria based on titles and abstracts. Keywording was performed in order to answer the research questions of the study. Results: Results from the systematic mapping technology allow the detection of almost mature technologies, as well as promising technologies and research gaps. Results from the deeper literature review have identified challenges and new trends specific to the building industry. Conclusions: Efforts are still needed for the building industry in order to contribute significantly to meeting societal needs. Further research: Research fields that would benefit from further research are highlighted.

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“…Another scenario used for validation is related to a real home building, the apartment instrumented in Casa + project [29], where 6 anchor nodes belonging to an already deployed WSN infrastructure working at 868 MHz were used to collect data related to a blind node. 20 different locations were considered as shown in Fig.…”

Section: Experiments and Validationmentioning

confidence: 99%

Lightweight localisation approach for WSNs: performance analysis and validation

Santic

Pomante

Rinaldi

2020

IET wirel. sens. syst.

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Wireless sensor networks (WSNs) have become very popular in recent years due to their wide applications' spectrum. One of the main issues arising while conceiving with WSNs functionality is the opportunity of developing a proper localisation algorithm that has to be tailored basing on hw/sw nodes limitations. This study proposes a localisation algorithm for WSNs based on received signal strength measurements whose results, although comparable with existing localisation algorithms, proves here to be of practical implementation on devices with very low computational capabilities as well as efficient in real-time usages. Extensive simulations and indoor/outdoor experimental measurements have been performed to verify the effectiveness of the proposed algorithm, taking also into account implementation issues on commercial sensor nodes.

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Short range wireless solutions enabling ambient assisted living to support people affected by the Down syndrome

Cited by 13 publications

References 7 publications

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study

Lightweight localisation approach for WSNs: performance analysis and validation

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