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Automated extraction and validation of children's gait parameters with the Kinect
Saeid Motiian1, Paola Pergami2, Keegan Guffey3
1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA. samotiian@mix.wvu.edu.
Insights
This study presents a low-cost, portable, in-home gait analysis system for children using Microsoft Kinect. The automated method accurately measures gait parameters, improving accessibility for rural families.
Area of Science:
- Biomedical Engineering
- Pediatric Rehabilitation
- Motion Analysis
Background:
- Traditional gait analysis requires specialized clinic equipment, posing accessibility challenges, especially for young children in rural areas.
- Existing methods are costly and inconvenient, limiting frequent monitoring essential for pediatric therapy.
Purpose of the Study:
- To develop a low-cost, portable, and automated in-home gait analysis system for children.
- To validate the accuracy and reliability of the proposed system using Microsoft Kinect technology.
Main Methods:
- A novel method for extracting gait parameters from noisy Kinect skeleton tracking data in children.
- Temporal segmentation using probabilistic matching of stride templates and scaling estimation.
- Comparison with GAITRite for accuracy, precision, repeatability, and concurrent validity in 2-4 year olds.
Main Results:
- The automated method demonstrated excellent accuracy and good precision in segmenting gait cycles.
- Estimated spatial and temporal gait parameters showed good concurrent validity and very good repeatability against GAITRite.
- Agreements for nine gait parameters ranged from good to excellent.
Conclusions:
- The study validates the automated use of Kinect for children's gait analysis.
- This approach advances the development of a low-cost, parent-operated in-home tool for pediatric clinicians.
Background:
Gait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect.
Methods:
A robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children's gait parameters.
Results:
The method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate.
Conclusion:
This work enables and validates the automated use of the Kinect for children's gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children.
