Activity Monitoring Systems in Health Care

Kröse, B.J.A.; Oosterhout, Tim van; Kasteren, Tim van

doi:10.1007/978-0-85729-994-9_12

Cited by 13 publications

(8 citation statements)

References 73 publications

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“…Greatest interest receives the potential recognition of activities of daily living (ADLs), which can lead to monitor habits and routines related to a person's health, as well as to abnormal behaviour detection, which is of special interest for early detection of mental impairment. In this sense, performing an ADL, such as a kitchen task, can serve as a functional measure of cognitive health [15]. Recently, there is also an increasing interest in the use of wearable cameras for recognising ADLs [16][17][18].…”

Section: Applicationsmentioning

confidence: 99%

Computer vision for active and assisted living

Planinc¹,

Chaaraoui

Kampel³

et al. 2016

Active and Assisted Living: Technologies and Applications

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Section: Applicationsmentioning

confidence: 99%

Computer vision for active and assisted living

Planinc¹,

Chaaraoui

Kampel³

et al. 2016

Active and Assisted Living: Technologies and Applications

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“…Considering customer activity as a unitized system, an action consists of simple motions, whereas activity is thought of as a complicated pattern of actions [11]. Bobick classified behavior into movement, activity, and action.…”

Section: Unitizing Activitymentioning

confidence: 99%

Activity-Driven PSS Design Method based on Emotional Customer Activity Modeling Integrating Expectation and Experience Factors

Kim

Lee

Nam

2016

IJAE

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“…For instance, in elderly care, behavior analysis based on track statistics such as walking speed, track smoothness and average activity levels can yield important information about physical and mental health degradation over long periods of time [Kröse et al 2011]. Similarly, in a work environment, statistics about the time workers spend sitting, walking, and standing up can help to pinpoint productivity problems or potential health hazards, such as taking too few breaks.…”

Section: Introductionmentioning

confidence: 98%

Low-complexity scalable distributed multicamera tracking of humans

Gruenwedel

Jelaca

Niño-Castañeda

et al. 2014

ACM Trans. Sen. Netw.

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Real-time tracking of people has many applications in computer vision, especially in the domain of surveillance. Typically, a network of cameras is used to solve this task. However, real-time tracking remains challenging due to frequent occlusions and environmental changes. Besides, multicamera applications often require a trade-off between accuracy and communication load within a camera network. In this article, we present a real-time distributed multicamera tracking system for the analysis of people in a meeting room. One contribution of the article is that we provide a scalable solution using smart cameras. The system is scalable because it requires a very small communication bandwidth and only light-weight processing on a "fusion center" which produces final tracking results. The fusion center can thus be cheap and can be duplicated to increase reliability.In the proposed decentralized system all low level video processing is performed on smart cameras. The smart cameras transmit a compact high-level description of moving people to the fusion center, which fuses this data using a Bayesian approach. A second contribution in our system is that the camera-based processing takes feedback from the fusion center about the most recent locations and motion states of tracked people into account. Based on this feedback and background subtraction results, the smart cameras generate a best hypothesis for each person.We evaluate the performance (in terms of precision and accuracy) of the tracker in indoor and meeting scenarios where individuals are often occluded by other people and/or furniture. Experimental results are presented based on the tracking of up to 4 people in a meeting room of 9 m by 5 m using 6 cameras. In about two hours of data, our method has only 0.3 losses per minute and can typically measure the position with an accuracy of 21 cm. We compare our approach to state-of-the-art methods and show that our system performs at least as good as other methods. However, our system is capable to run in real-time and therefore produces instantaneous results.

show abstract

Activity Monitoring Systems in Health Care

Cited by 13 publications

References 73 publications

Computer vision for active and assisted living

Computer vision for active and assisted living

Activity-Driven PSS Design Method based on Emotional Customer Activity Modeling Integrating Expectation and Experience Factors

Low-complexity scalable distributed multicamera tracking of humans

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