Behavior Analysis for Aging-in-Place using Similarity Heatmaps

Eldib, Mohamed; Bo, Nyan Bo; Deboeverie, Francis; Xie, Xingzhe; Philips, Wilfried; Aghajan, Hamid

doi:10.1145/2659021.2659038

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…In Ref. 57, the authors proposed a novel measure to find similar patterns of behavior between each pair of days from the users' detected positions, based on heatmaps and Earth Movers Distance. Then an exemplar-based approach is used to identify sleeping, eating, sitting activities, and walking patterns.…”

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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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%

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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“…Due to privacy, the toilet could be sensor-free [ 72 ]. For data security, some research has adopted different solutions: raw data were not transmitted [ 35 , 76 ], a sensor network was isolated [ 42 ], data transmission was encrypted [ 50 ], and data access was authorized [ 40 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

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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“…The use of different sensors such as Passive Infrared Motion Sensor (PIR) [16,2], visual sensors [17,12] and Kinect sensors [15] became popular among researchers to analyze the Activities of Daily Living (ADL). PIR sensors detect motion and do not provide detailed information on motion patterns.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, visitor detection methods from literature are also only analyzed over short periods. Visual sensors have shown promising results in person tracking [11,17] and behavior analysis [25,12]. They are designed to offer an acceptable sensing mechanism for addressing the privacy concerns of the users, while the kinect sensor requires post-processing algorithms to solve the privacy concern problem [6].…”

Section: Related Workmentioning

confidence: 99%

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

Eldib

Deboeverie

Haerenborgh

et al. 2015

Proceedings of the 9th International Conference on Distributed Smart Cameras

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Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth.

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Behavior Analysis for Aging-in-Place using Similarity Heatmaps

Cited by 6 publications

References 21 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

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

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

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