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— We present an investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive fall risk assessment in home environments. In order to allow older adults to safely continue living in independent settings as they age, the ability to assess their risk of falling, along with detecting the early onset of illness and functional(More)
An investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive gait assessment in home environments is presented. In order to allow older adults to safely continue living in independent settings as they age, the ability to assess their risk of falling, along with detecting the early onset of illness and functional decline, is(More)
A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the(More)
A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over(More)
— We present a method for improving human segmentation results in calibrated, multi-view environments using features derived from both pixel (image) and voxel (volume) space. The main focus of this work is to develop a low-cost, vision-based system for passive activity monitoring of older adults in the home, to capture early signs of illness and functional(More)
A two-stage fall detection technique developed by our team was tested in a real hospital setting with falls acted out in a patient room. To further test the algorithm, data were collected at the University of Missouri hospital with actual patients. Features extracted from three dimensional point clouds created from Kinect depth images were used as input to(More)
In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion(More)
Results are presented for measuring the gait parameters of walking speed, stride time, and stride length of five older adults continuously, in their homes, over a four month period. The gait parameters were measured passively, using an inexpensive, environmentally mounted depth camera, the Microsoft Kinect. Research has indicated the importance of measuring(More)
In-home gait measurement results from the apartments of seven older adults obtained using an environmentally mounted depth camera, the Microsoft Kinect, are presented. Previous work evaluating the use of the Kinect for in-home gait assessment in a lab setting has shown the potential of this approach. In this work, a single Kinect sensor and computer have(More)
In this paper, we present a method for extracting gait parameters including walking speed, step time and step length from a three-dimensional voxel reconstruction, which is built from two calibrated camera views. These parameters are validated with a GAITRite Electronic mat and a Vicon motion capture system. Experiments were conducted in which subjects(More)