This paper proposes a markerless system whose purpose is to help preventing falls of elderly people at home. To track human movements, the Microsoft Kinect camera is used which allows to acquire at the same time a RGB image and a depth image.
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Detecting human falls with 3-axis accelerometer and depth sensor
In this work we present a novel approach to fall detection that allows us to achieve reliable fall detection in larger areas through using the Kinect sensor.
The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data
In the current study, we extend the application of wearable inertial sensors beyond post-impact fall detection, by developing and evaluating the accuracy of a sensor system for detecting falls prior to the fall impact.
Validation of the Kinect for gait analysis using the GAITRite walkway
In this study, an algorithm was developed which can measure step length and step time using the Kinect depth image. The validity of the measured step length and time is determined using the GAITRite walkway as a ground truth.
