Evaluating the accuracy of a mobile Kinect-based gait-monitoring system for fall prediction

Staranowicz, Aaron; Brown, Garrett R.; Mariottini, Gian-Luca

doi:10.1145/2504335.2504396

Cited by 33 publications

(13 citation statements)

References 19 publications

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“…The gait patterns were collected from users over a period of time while performing activities of daily living (ADL) such as walking. Staranowicz et al [ 60 ] developed a system that monitors the walking patterns of older adults during their ADLs at home. Their approach identifies functional decline using an autonomous robot.…”

Section: Fall Prevention Systemsmentioning

confidence: 99%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Rajagopalan

Litvan

Jung

2017

Sensors

127

101

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Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

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Section: Fall Prevention Systemsmentioning

confidence: 99%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Rajagopalan

Litvan

Jung

2017

Sensors

127

101

View full text Add to dashboard Cite

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“…Whereas this system should be primarily designed to detect when a person falls, a system that also predicts falls before they happen would be ideal per participant comments. Some systems could combine a fall detector with other tools designed to predict falls by detecting changes in gait over time or even designed to prevent participants from falling or being injured during a fall (Kiselev, Haesner, Gövercin, & Steinhagen-Thiessen, 2015;Staranowicz, Brown, & Mariottini, 2013;"Swedes Develop Invisible Bike Helmet," 2013. ) The ideal device would have GPS capabilities and provide the user with the ability to customize when the GPS function was active.…”

Section: Device Recommendationsmentioning

confidence: 99%

Older Adults’ Perceptions of Fall Detection Devices

Chaudhuri

Kneale

et al. 2015

J Appl Gerontol

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A third of adults over the age of 65 are estimated to fall at least once a year. Perhaps as dangerous as the fall itself is the time spent after a fall if the person is unable to move. Although there are many devices available to detect when a person has fallen, little is known about the opinions of older adults regarding these fall detection devices (FDDs). We conducted five focus groups with 27 older adults. Transcripts from sessions were coded to generate themes that captured participants' perceptions. Themes were identified that related to two topics of interest: (a) personal influences on the participants' desire to have a FDD, including perceived need, participant values, and cost, and (b) participant recommendations regarding specific features and functionalities of these devices such as automation, wearable versus non-wearable devices, and device customization. Together, these themes suggest ways in which FDDs may be improved so that they are suitable for their intended population.

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“…However, Microsoft Kinect can only work well within the range of 3 meters [41]. There are many gait recognition systems based on Microsoft Kinect, such as References [40][41][42][43]. In general, there are two sorts of video-based gait approaches (model-based/model-free approaches) [8].…”

Section: Data Acquisitionmentioning

confidence: 99%

A Survey on Gait Recognition

2018

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Recognizing people by their gait has become more and more popular nowadays due to the following reasons. First, gait recognition can work well remotely. Second, gait recognition can be done from low-resolution videos and with simple instrumentation. Third, gait recognition can be done without the cooperation of individuals. Fourth, gait recognition can work well while other features such as faces and fingerprints are hidden. Finally, gait features are typically difficult to be impersonated. Recent ubiquity of smartphones that capture gait patterns through accelerometers and gyroscope and advances in machine learning have opened new research directions and applications in gait recognition. A timely survey that addresses current advances is missing. In this article, we survey research works in gait recognition. In addition to recognition based on video, we address new modalities, such as recognition based on floor sensors, radars, and accelerometers; new approaches that include machine learning methods; and examine challenges and vulnerabilities in this field. In addition, we propose a set of future research directions. Our review reveals the current state-of-art and can be helpful to both experts and newcomers of gait recognition. Moreover, it lists future works and publicly available databases in gait recognition for researchers.

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Evaluating the accuracy of a mobile Kinect-based gait-monitoring system for fall prediction

Cited by 33 publications

References 19 publications

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Older Adults’ Perceptions of Fall Detection Devices

A Survey on Gait Recognition

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