Video Based Fall Detection using Features of Motion, Shape and Histogram

Albawendi, Suad; Lotfi, Ahmad; Powell, Heather M.; Appiah, Kofi

doi:10.1145/3197768.3201539

Cited by 17 publications

(9 citation statements)

References 25 publications

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“…Thus far human pose tracking has been explored for various methods of measuring gait and mobility in the older adult population, including automatically acquiring the clinical parameters measured in the timed up-and-go (TUG) test, which is a clinical measure of mobility [26], step monitoring [27], and general gait parameter extraction [28]. Another application which has been extensively researched is the detection of falls [29][30][31]. While many studies have used pose tracking to generally analyse gait or identify when a fall occurs, there is limited research addressing ways to prospectively determine when future falls are likely.…”

Section: B Human Pose Estimationmentioning

confidence: 99%

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

Mehdizadeh

Iaboni

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a preexisting pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach.

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Section: B Human Pose Estimationmentioning

confidence: 99%

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

Mehdizadeh

Iaboni

et al. 2020

IEEE J. Transl. Eng. Health Med.

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“…Espinosa et al [7] separated the person in the picture from the background and extracted the ratio of length to width of the human body to recognize standing and falling. In addition, some researchers extracted human contour features and recognized activities through changes in contour [8][9][10]. Rougier et al [11] used an ellipse rather than a bounding box on HAR.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Zhao

Zhou

et al. 2022

Applied Sciences

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Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve the accuracy of neural networks for HAR. We addressed this problem by proposing a method that combines the improved principal component analysis (PCA) and the improved VGG16 model (a pre-trained 16-layer neural network model) to enhance the accuracy of HAR under small-scale datasets. This method used the improved PCA to enhance features of the extracted components and reduce the dimensionality of the data. The VGG16 model was improved by deleting the complex Fully-Connected layers and adding a Dropout layer between them to prevent the loss of useful information. The experimental results show that the accuracy of our proposed method on HAR is 96.34%, which is 4.27% higher after improvement, and the training time of each round is 10.88 s, which is 12.8% shorter than before.

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“…Fall detection -Multiple studies have demonstrated that video surveillance technology is able to detect falls among patients with high an accuracy rate typically of 90%. 42 Generally, the video-based fall detection system is segmented into 3 stages of video acquisition, video analysis, and notification communication. Therefore, the basic operation principle of this system is to record the patient in an ambient environment and then the video system's AI should recognize a fall immediately and notify the ambulance or the family of the patient.…”

Section: Stroke Symptom Detection Applicationsmentioning

confidence: 99%

Automatic Acute Stroke Symptom Detection and Emergency Medical Systems Alerting by Mobile Health Technologies: A Review

Bat-Erdene¹,

Saver²

2021

Journal of Stroke and Cerebrovascular Diseases

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Objectives: To survey recent advances in acute stroke symptom automatic detection and Emergency Medical Systems (EMS) alerting by mobile health technologies. Materials and methods: Narrative review Results: Delayed activation of EMS for stroke symptoms by patients and witnesses deprives patients of rapid access to brain-saving therapies and occurs due to public unawareness of stroke features, cognitive and motor deficits produced by the stroke itself, and sleep onset. A promising emerging approach to overcoming the inherent biologic constraints of patient capacity to self-detect and respond to stroke symptoms is continuous monitoring by mobile health technologies with wireless sensors and artificial intelligence recognition systems. This review surveys 11 sensing technologies -accelerometers, gyroscopes, magnetometers, pressure sensors, touch screen and keyboard input detectors, artificial vision, and artificial hearing; and 10 consumer device form factors in which they are increasingly implemented: smartphones, smart speakers, smart watches and fitness bands, smart speakers/voice assistants, home health robots, smart clothing, smart beds, closed circuit television, smart rings, and desktop/laptop/tablet computers. Conclusions: The increase in computing power, wearable sensors, and mobile connectivity have ushered in an array of mobile health technologies that can transform stroke detection and EMS activation. By continuously monitoring a diverse range of biometric parameters, commercially available devices provide the technologic capability to detect cardinal language, motor, gait, and sensory signs of stroke onset. Intensified translational research to convert the promise of these technologies to validated, accurate real-world deployments are an important next priority for stroke investigation.

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Video Based Fall Detection using Features of Motion, Shape and Histogram

Cited by 17 publications

References 25 publications

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Automatic Acute Stroke Symptom Detection and Emergency Medical Systems Alerting by Mobile Health Technologies: A Review

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