Detection of visitors in elderly care using a low-resolution visual sensor network

Eldib, Mohamed; Deboeverie, Francis; Haerenborgh, Dirk Van; Philips, Wilfried; Aghajan, Hamid

doi:10.1145/2789116.2789137

Cited by 12 publications

(7 citation statements)

References 26 publications

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“…Examples of activities are going outside the home or receiving visits. 20 An example of a behavioral change is increased or decreased mobility measured from speed or walked distance. 21 A single PIR sensor records the occupant's activities with only a binary state indicating whether there is a motion detected within its detection range.…”

Section: Related Workmentioning

confidence: 99%

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Behavior analysis for elderly care using a network of low-resolution visual sensors

Eldib

Deboeverie

Philips

et al. 2016

J. Electron. Imaging

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Recent advancements in visual sensor technologies have made behavior analysis practical for in-home monitoring systems. The current in-home monitoring systems face several challenges: (1) visual sensor calibration is a difficult task and not practical in real-life because of the need for recalibration when the visual sensors are moved accidentally by a caregiver or the senior citizen, (2) privacy concerns, and (3) the high hardware installation cost. We propose to use a network of cheap low-resolution visual sensors (30×30 pixels) for long-term behavior analysis. The behavior analysis starts by visual feature selection based on foreground/background detection to track the motion level in each visual sensor. Then a hidden Markov model (HMM) is used to estimate the user’s locations without calibration. Finally, an activity discovery approach is proposed using spatial and temporal contexts. We performed experiments on 10 months of real-life data. We show that the HMM approach outperforms the k-nearest neighbor classifier against ground truth for 30 days. Our framework is able to discover 13 activities of daily livings (ADL parameters). More specifically, we analyze mobility patterns and some of the key ADL parameters to detect increasing or decreasing health conditions

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Section: Related Workmentioning

confidence: 99%

“…Then the statistical features are clustered using a random sample consensus principle method to detect the behavior patterns. Eldib et al 20 measured the socialization level of a senior citizen by detecting visits via HMM. In Ref.…”

Section: Related Workmentioning

confidence: 99%

Behavior analysis for elderly care using a network of low-resolution visual sensors

Eldib

Deboeverie

Philips

et al. 2016

J. Electron. Imaging

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“…We noticed that some articles mentioned SI in the title or abstract but did not meet our inclusion criteria of an objective measure related directly to SI. For example, the study by Eldib et al [39] tested the potential of a video camera to monitor the number of visitors, which can be an objective measure of SI. However, the study did not relate the objective measure to a self-report measure of SI.…”

Section: Discussionmentioning

confidence: 99%

Sensor-based assessment of social isolation incommunity-dwelling older adults: A scopingreview

Khan

Spinelli

et al. 2022

Preprint

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Social isolation (SI) is a state of low social interaction with peers associated with various adverse healthconsequences in older adults living in the community. SI is most often assessed through retrospective self-reports, which can be prone to recall or self-report biases and influenced by stigma. Ambient and wearable sensors have been explored to objectively assess SI based on interactions of a person within the environment and physiological data. However, because this field is in its infancy, there is a lack of clarity regarding the application of sensors and their data in assessing SI and the methods to develop these assessments. To understand the current state of research in sensor-based assessment of SI in older adults living in the community and to make recommendations for the field moving forward, we conducted a scoping review. The aims of the scoping review were to 1. Map the types of sensors (and their associated data) that have been used for objective SI assessment, 2. Identify the methodological approaches used to develop the SI assessment, and 3. Synthesize current understandings and knowledge gaps to be addressed in future research. Using an established scoping review methodology, we identified eight relevant articles. Data from motion sensors and actigraph were commonly applied and compared and correlated with self-report measures in developing objective SI assessments. Variability exists in defining SI, feature extraction and the use of sensors and self-report assessments. Inconsistent definitions and use of various self-report scales for measuring SI create barriers to studying the concept and extracting features to build predictive models. Recommendations include establishing a consistent definition of SI for sensor-based assessment research and development and consider capturing its complexity through innovative domain-specific features.

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“…Therefore, identifying activities in a multi-occupancy environment only based on ambient sensors has proven to be difficult, if not impossible (Alemdar and Ersoy 2017;Benmansour et al 2017). Therefore, the use of wearable sensors, visual sensors, and video cameras has become the norm to monitor ADLs in a multi-occupancy environment (Eldib et al 2015). However, a few studies focusing on the detection of visits in a home environment based on PIR sensors only (Aicha et al 2012;Petersen et al 2012;Nait Aicha et al 2013).…”

Section: Related Workmentioning

confidence: 99%

“…These techniques are classified into two main categories, which are statistical techniques and computational intelligence techniques. Most of the research which has been conducted in the context of activity recognition in multi-occupancy has used statistical techniques, including the Hidden Markov Model (HMM) (Alemdar and Ersoy 2017;Han et al 2004;Murphy 2012;Eldib et al 2015), Naive Bayes Classifier (NBC) (Benmansour et al 2016) and the conditional random field (CRF) (Hsu et al 2010). These techniques are utilised to detect relationships between temporal data generated by the sensors and identify the pattern of the user.…”

Section: Related Workmentioning

confidence: 99%

Employing entropy measures to identify visitors in multi-occupancy environments

Howedi

Lotfi

Pourabdollah

2020

J Ambient Intell Human Comput

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Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

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Detection of visitors in elderly care using a low-resolution visual sensor network

Cited by 12 publications

References 26 publications

Behavior analysis for elderly care using a network of low-resolution visual sensors

Behavior analysis for elderly care using a network of low-resolution visual sensors

Sensor-based assessment of social isolation incommunity-dwelling older adults: A scopingreview

Employing entropy measures to identify visitors in multi-occupancy environments

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