Depth Camera Gait Parameter Extraction for Fall Risk Assessment
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Solution Overview
Problem
Current methods for monitoring gait parameters in elderly individuals are expensive, labor-intensive, and infrequently conducted, making them inadequate for continuous and accurate assessment of fall risk, which is a significant health concern among older adults.
Innovation Solution
A system utilizing depth cameras, such as Microsoft Kinect, to capture depth image data and process it into temporal and spatial gait parameters, enabling continuous monitoring and health risk assessments in a home environment, integrating with electronic health records to alert healthcare professionals of potential risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used, then measurement precision of gait parameters is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex mechanical measurement systems with a depth camera-based optical system. The Microsoft Kinect camera captures depth images and uses skeletal tracking algorithms to extract gait parameters, eliminating the need for mechanical sensors, force plates, or motion capture equipment while maintaining measurement precision.
Solution Approach 2:
The patent creates a virtual 3D model (skeletal representation) of the patient's body from depth camera images. This digital copy allows for non-contact measurement of gait parameters by tracking the positions and movements of skeletal joints, providing accurate measurements without physical interference.
2Measurement precision
If traditional monitoring methods are used, then measurement precision is improved, but ease of operation deteriorates due to labor-intensive procedures
Solution Approach 1:
The system enables patients to perform gait assessments independently in their own environment. The depth camera automatically captures depth images and the processing system extracts gait parameters without requiring trained operators, making the monitoring process self-service and eliminating the need for clinical staff intervention.
Solution Approach 2:
The system performs continuous periodic capture of depth images at high frame rates, automatically processing each frame to track skeletal movements and calculate gait parameters. This continuous periodic monitoring replaces manual, intermittent assessments with automated continuous evaluation.
3Measurement precision
If traditional monitoring methods are used, then measurement precision is improved, but productivity deteriorates due to infrequent assessments
Solution Approach 1:
The system enables continuous gait monitoring by continuously capturing depth images and processing them in real-time to extract gait parameters. This continuous action allows for frequent assessments throughout the day, capturing temporal variations in gait that intermittent traditional methods would miss, thereby improving productivity through increased assessment frequency.
4Productivity
If depth camera system is implemented, then productivity is improved through continuous monitoring, but measurement precision may worsen compared to clinical-grade equipment
Solution Approach 1:
The system changes the measurement parameters by using depth information from camera images rather than direct mechanical measurements. By processing depth images to extract skeletal joint positions and calculating gait parameters from these positional data, the system achieves acceptable precision for fall risk assessment while enabling continuous monitoring.
Data Source
AI summary
Aspects of the present disclosure include methods and corresponding systems for performing health risk assessments for a patient in the home environment. In various aspects, depth image data for a person may be obtained and subsequently processed to generate one or more parameters, such as temporal and spatial gait parameters. Subsequently, the generated parameters may be processed with other medical information related to the patient, such as electronic health records, to perform various health risk assessments.