Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect

Area of Science:

• Gerontology
• Biomedical Engineering
• Rehabilitation Technology

Background:

• Traditional fall risk assessments often lack continuous, real-world data.
• The Microsoft Kinect sensor has shown promise for gait analysis in laboratory settings.
• Older adults in independent living facilities require effective methods for monitoring health and preventing falls.

Purpose of the Study:

• To present a system for continuous, in-home gait measurement using an environmentally mounted Microsoft Kinect depth camera.
• To compare automated gait estimates derived from Kinect data with traditional fall risk assessment tools.
• To explore the application of this technology for monitoring older adults' gait and fall risk.

Main Methods:

• Deployment of a single Kinect sensor and computer in older adults' apartments for continuous data capture.
• Implementation of a probabilistic methodology for automated gait estimation from Kinect data.
• Conducting monthly clinical fall risk assessments, including Timed Up-and-Go and habitual gait speed tests.

Main Results:

• The study details a probabilistic methodology for generating automated gait estimates over time.
• Results from the in-home Kinect system are compared against traditional fall risk assessment tools.
• The system demonstrated feasibility for continuous, in-home gait monitoring in older adults.

Conclusions:

• The Kinect-based system offers a viable approach for habitual, in-home gait measurement.
• Automated gait analysis can supplement traditional assessments for fall risk evaluation in older adults.
• This technology holds potential for remote patient monitoring and proactive fall prevention strategies.