W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Activity Summarisation and Fall Detection in a Supportive Home Environment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer Vision and Image Understanding
Older adults' privacy considerations for vision based recognition methods of eldercare applications
Technology and Health Care
On the disambiguation of passively measured in-home gait velocities from multi-person smart homes
Journal of Ambient Intelligence and Smart Environments
Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
IEEE Transactions on Circuits and Systems for Video Technology
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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 essential. Daily measurements of temporal and spatial gait parameters would greatly facilitate such an assessment. Ideally, these measurements would be obtained passively, in normal daily activity, without the need for wearable devices or expensive equipment. In this work, the use of the inexpensive Microsoft Kinect for obtaining measurements of temporal and spatial gait parameters is evaluated against an existing web-camera based system, along with a Vicon marker-based motion capture system for ground truth. Techniques for extracting gait parameters from the Kinect data are described, as well as the potential advantages of the Kinect over the web-camera system for passive, in-home gait assessment.