Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink

Stone, Erik E.; Skubic, Marjorie

doi:10.1007/978-3-319-14424-5_5

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…After a fall detection is encountered, an alert is sent to caregiver by using a SIM900A GSM modem. In addition, Stone and Skubic [11] proposed a method for a real-time fall alert with embedded depth video clip based on hyperlink. When a fall occurs, notification is sent to facility staff members via email.…”

Section: Related Workmentioning

confidence: 99%

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Watanapa

Patsadu

Dajpratham

et al. 2018

Applied Computational Intelligence and Soft Computing

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This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond.

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Section: Related Workmentioning

confidence: 99%

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Watanapa

Patsadu

Dajpratham

et al. 2018

Applied Computational Intelligence and Soft Computing

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“…Recent advances in 3-D image capture enable effective model-based inferences. These have been used to assess gait parameters and exercise performance [49]–[52]. One useful example deploys hybrid models of gait that combine continuous dynamical systems with discrete phase switching [50].…”

Section: Models Of Sensing and Activity Recogntionmentioning

confidence: 99%

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care

Pavel

Jimison

Korhonen

et al. 2015

IEEE Trans. Biomed. Eng.

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Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.

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“…[1] Videos are stored on local machines as 15 minutes avi files and then transferred to the central file storage over available Wi-Fi network considering the bandwidth limitations. A real-time fall alarm system generates alarms upon detecting a fall to the designated hospital nurses for immediate actions [7].…”

Section: A Novel Web-based Depth Video Rewind Approach Toward Fall Prmentioning

confidence: 99%

A novel web-based depth video rewind approach toward fall preventive interventions in hospitals

Enayati

Banerjee

Popescu

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The purpose of this study was to implement a web based application to provide the ability to rewind and review depth videos captured in hospital rooms to investigate the event chains that led to patient's fall at a specific time. In this research, Kinect depth images are being used to capture shadow-like images of the patient and their room to resolve concerns about patients' privacy. As a result of our previous research, a fall detection system has been developed and installed in hospital rooms, and fall alarms are generated if any falls are detected by the system. Then nurses will go through the stored depth videos to investigate for possible injury as well as the reasons and events that may have caused the patient's fall to prevent future occurrences. This paper proposes a novel web application to ease the process of search and reviewing the videos by means of new visualization techniques to highlight video frames that contain potential risk of fall based on our previous research.

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Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink

Cited by 7 publications

References 23 publications

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care

A novel web-based depth video rewind approach toward fall preventive interventions in hospitals

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