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Autores principales: Abdelaleem, Dina E., Ahmed, Hassan M., Soliman, M. Sami, Said, Tarek M.
Formato: Preprint
Publicado: 2024
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Acceso en línea:https://arxiv.org/abs/2409.08508
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author Abdelaleem, Dina E.
Ahmed, Hassan M.
Soliman, M. Sami
Said, Tarek M.
author_facet Abdelaleem, Dina E.
Ahmed, Hassan M.
Soliman, M. Sami
Said, Tarek M.
contents Daily activity monitoring systems used in households provide vital information for health status, particularly with aging residents. Multiple approaches have been introduced to achieve such goals, typically obtrusive and non-obtrusive. Amongst the obtrusive approaches are the wearable devices, and among the non-obtrusive approaches are the movement detection systems, including motion sensors and thermal sensor arrays (TSAs). TSA systems are advantageous when preserving a person's privacy and picking his precise spatial location. In this study, human daily living activities were monitored day and night, constructing the corresponding activity time series and spatial probability distribution and employing a TSA system. The monitored activities are classified into two categories: sleeping and daily activity. Results showed the possibility of distinguishing between classes regardless of day and night. The obtained sleep activity duration was compared with previous research using the same raw data. Results showed that the duration of sleep activity, on average, was 9 hours/day, and daily life activity was 7 hours/day. The person's spatial probability distribution was determined using the bivariate distribution for the monitored location. In conclusion, the results showed that sleeping activity was dominant. Our study showed that TSAs were the optimum choice when monitoring human activity. Our proposed approach tackled limitations encountered by previous human activity monitoring systems, such as preserving human privacy while knowing his precise spatial location.
format Preprint
id arxiv_https___arxiv_org_abs_2409_08508
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Identifying Human Indoor Daily Life Behavior employing Thermal Sensor Arrays (TSAs)
Abdelaleem, Dina E.
Ahmed, Hassan M.
Soliman, M. Sami
Said, Tarek M.
Computer Vision and Pattern Recognition
Signal Processing
Medical Physics
Daily activity monitoring systems used in households provide vital information for health status, particularly with aging residents. Multiple approaches have been introduced to achieve such goals, typically obtrusive and non-obtrusive. Amongst the obtrusive approaches are the wearable devices, and among the non-obtrusive approaches are the movement detection systems, including motion sensors and thermal sensor arrays (TSAs). TSA systems are advantageous when preserving a person's privacy and picking his precise spatial location. In this study, human daily living activities were monitored day and night, constructing the corresponding activity time series and spatial probability distribution and employing a TSA system. The monitored activities are classified into two categories: sleeping and daily activity. Results showed the possibility of distinguishing between classes regardless of day and night. The obtained sleep activity duration was compared with previous research using the same raw data. Results showed that the duration of sleep activity, on average, was 9 hours/day, and daily life activity was 7 hours/day. The person's spatial probability distribution was determined using the bivariate distribution for the monitored location. In conclusion, the results showed that sleeping activity was dominant. Our study showed that TSAs were the optimum choice when monitoring human activity. Our proposed approach tackled limitations encountered by previous human activity monitoring systems, such as preserving human privacy while knowing his precise spatial location.
title Identifying Human Indoor Daily Life Behavior employing Thermal Sensor Arrays (TSAs)
topic Computer Vision and Pattern Recognition
Signal Processing
Medical Physics
url https://arxiv.org/abs/2409.08508