Study on indoor activity monitoring by using electric field sensor

Tsukamoto, Sosuke; Hoshino, Hiroshi; Tamura, Toshiyo

doi:10.4017/gt.2008.07.02.163.00

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Electrical signatures are also another path being tested [19], [20] to detect different various activities of daily living. Indeed, by using pattern recognition on the electrical network it is possible to infer what material is being used, and when it is turned on and off.…”

Section: Related Researchmentioning

confidence: 99%

SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

Fleury

Vacher

Noury

2010

IEEE Trans. Inform. Technol. Biomed.

430

252

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Abstract-By 2050, about a third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international ADL or the French AGGIR scales, by automatically classifying the different Activities of Daily Living performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, Infra-Red Presence Sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs on postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors, is then used to classify each temporal frame into one of the activities of daily living that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using Support Vector Machines. We performed a one-hour experimentation with 13 young and healthy subjects to determine the models of the different activities and then we tested the classification algorithm (cross-validation) with real data.

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Section: Related Researchmentioning

confidence: 99%

SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

Fleury

Vacher

Noury

2010

IEEE Trans. Inform. Technol. Biomed.

430

252

View full text Add to dashboard Cite

show abstract

“…In another project [3], objects and foods are tagged to create a model that distinguishes the activities of preparing a drink (cold or hot) from the hygiene. Electrical signatures analysis is another path that is tested in [4], [5] to detect the different activities of daily living. In this article, we focused on the use of Support Vector Machine to learn seven different activities and classify them using sensors inside the flat that will be described in the next sections.…”

Section: Introductionmentioning

confidence: 99%

Supervised classification of activities of daily living in health smart homes using SVM

Fleury

Noury

Vacher

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-By 2050, about a third of the French population will be over 65. To face this modification of the population, the current studies of our laboratory focus on the monitoring of elderly people at home. This aims at detect, as early as possible, a loss of autonomy by objectivizing criterions such as the international ADL or the French AGGIR scales implementing automatic classification of the different Activities of Daily Living. A Health Smart Home is used to achieve this goal. This flat includes different sensors. The data from the various sensors were used to classify each temporal frame into one of the activities of daily living that has been previously learnt (seven activities: hygiene, toilets, eating, resting, sleeping, communication and dressing/undressing). This is done using Support Vector Machines. We performed an experimentation with 13 young and healthy subjects to learn the model of activities and then we tested the classification algorithm (crossvalidation) on real data.

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“…Tsukamoto et al [20] evaluated a monitoring system that consisted by electric field sensors. While this system can be used to measure the usage of various household electronic devices, they report accurate measuring of television usage.…”

Section: Introductionmentioning

confidence: 99%

Enabling active and healthy ageing decision support systems with the smart collection of TV usage patterns

Billis

Batziakas

Bratsas

et al. 2016

Healthcare Technology Letters

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Smart monitoring of seniors behavioural patterns and more specifically activities of daily living have attracted immense research interest in recent years. Development of smart decision support systems to support the promotion of health smart homes has also emerged taking advantage of the plethora of smart, inexpensive and unobtrusive monitoring sensors, devices and software tools. To this end, a smart monitoring system has been used in order to extract meaningful information about television (TV) usage patterns and subsequently associate them with clinical findings of experts. The smart TV operating state remote monitoring system was installed in four elderly women homes and gathered data for more than 11 months. Results suggest that TV daily usage (time the TV is turned on) can predict mental health change. Conclusively, the authors suggest that collection of smart device usage patterns could strengthen the inference capabilities of existing health DSSs applied in uncontrolled settings such as real senior homes.

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Study on indoor activity monitoring by using electric field sensor

Cited by 12 publications

References 5 publications

SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

Supervised classification of activities of daily living in health smart homes using SVM

Enabling active and healthy ageing decision support systems with the smart collection of TV usage patterns

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