Multimodal Situational Awareness for Eldercare

Huang, Weimin; Wai, Aung Aung Phyo; Foo, Siang Fook; Biswas, Jit; Hsia, Chin; Liou, Koujuch

doi:10.1007/978-3-642-13778-5_11

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…It utilized a mattress pad with 54 force sensitive resistors and 54 resistive temperature devices to estimate the body center of mass and degree of restlessness [19]. Huang and Gaddam also used force sensitive resistors mounted under the mattress and the four legs of the beds separately [20,21]. Zhu et al proposed an approach for long-term monitoring of respiration rhythm (RR), pulse rate (PR) and body movement (BM) during sleep [22].…”

Section: Related Workmentioning

confidence: 99%

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

Ni¹,

Wu²,

Abdulrazak³

et al. 2015

Front. Comput. Sci.

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The quality of sleep may be a reflection of an elderly individual's health state and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user's sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user's sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users' sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

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Section: Related Workmentioning

confidence: 99%

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

Ni¹,

Wu²,

Abdulrazak³

et al. 2015

Front. Comput. Sci.

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“…In current setup, one Pan-Tilt-Zoom (PTZ) Dome Camera (BE-320A, BlueEyes, Taiwan) is installed at the ceiling from the bottom of the bed pointing 45' angle towards the bed area. By applying foreground objects segmentation with short-term motion compensation [7], the presence and movements of the subject can be recognized not only in the bed but also around the bed.…”

Section: A Pressure Modalitymentioning

confidence: 99%

Evaluation and analysis of multimodal sensors for developing in and around the bed patient monitoring system

Aung

Foo

Huang

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Due to the decline in physical and cognitive abilities, many frail elderly may have to lie in the bed most of their time. It is not feasible to monitor them continuously through manual observations alone. This issue can be resolved by embedding a set of multimodal sensors into the bed and providing automated activity recognition intelligence. But it is important to design and develop such multimodal sensing intelligence system desirable to the demands made by the clinicians. This paper presents the comparison and evaluation of different sensing bed configurations to observe different granularities of patient's contexts and activities in and around the bed. Based on the achievements and lessons learned from the experimental analysis, we propose improved sensing bed hardware and software systems to meet the real needs of in and around the bed patient monitoring.

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Human-Centric Situational Awareness in the Bedroom

Yen

et al. 2011

Toward Useful Services for Elderly and People With Disabilities

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Multimodal Situational Awareness for Eldercare

Cited by 4 publications

References 11 publications

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

Evaluation and analysis of multimodal sensors for developing in and around the bed patient monitoring system

Human-Centric Situational Awareness in the Bedroom

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