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Estimating infant upper extremities motion with an RGB-D camera and markerless deep neural network tracking: A
Insights
This study introduces a low-cost, markerless motion tracking method for infant upper body movement analysis. This technique aids in identifying biomarkers for predicting movement disorders in infants.
Area of Science:
- Biomedical Engineering
- Developmental Pediatrics
- Computer Vision
Background:
- Quantitative biomarkers of infant motion are crucial for predicting movement disorders.
- Current methods for motion tracking can be costly or complex.
- Markerless motion analysis offers a potential low-cost alternative.
Purpose of the Study:
- To present and validate a low-cost, markerless motion tracking method for estimating infant upper body kinematics.
- To extract quantitative biomarkers from infant motion for movement disorder prediction.
- To assess the accuracy of the proposed method using a validated setup.
Main Methods:
- Utilized a single RGB-D camera and the DeepLabCut 2D motion tracking software.
- Developed an algorithm to generate 3D coordinates from 2D points, handling missing data.
- Validated the method with a doll on a rotating turntable, testing two camera orientations.
Main Results:
- Evaluated upper body segment lengths, joint angles, and linear point velocity against known values.
- Determined Mean Absolute Errors (MAE) for each kinematic measure, indicating expected margins of error.
- The 45° camera angle simulation highlighted challenges with occlusions, relevant to live infant recordings.
Conclusions:
- The validated markerless motion tracking method provides a feasible approach for obtaining quantitative biomarkers from infant upper body motion.
- The identified MAE offers a benchmark for expected accuracy in clinical applications.
- Future improvements in hardware and tracking software can further reduce error margins for enhanced predictive capabilities in movement disorders.
Abstract:
Quantitative biomarkers of infant motion may be predictive of the development of movement disorders. This study presents and validates a low cost, markerless motion tracking method for the estimation of upper body kinematics of infants from which proper biomarkers may be extracted. The method requires a single RGB-D camera, a 2D motion tracking software publicly available (DeepLabCut) and an algorithm generating 3D point coordinates from the 2D tracked points, dealing with missing data, originating from various sources, for estimating joint kinematics. The proposed method is validated using known point kinematics obtained from a doll, with size and shape of an infant, lying on a turntable rotating at 33⅓ rpm. Two camera image plane orientations are tested: parallel to the turntable motion plane and forming a 45° angle with respect to the motion plane. The latter enhances the occurrence of body parts occlusions during motion as expected in live infant motion recordings. The length of upper body segments, elbow and shoulder joint angles and the linear point velocity determined with the proposed method are evaluated against reference values obtained from the known motion of the turntable. The relevant Mean Absolute Errors (MAE) found indicate the margin of error to expect when processing live infant motion. The proposed method may be improved if enhanced hardware and tracking software are employed, therefore reducing the above-mentioned margin of error. Clinical Relevance - The validation of the proposed method carried out in this study allows clinicians to select proper quantitative biomarkers obtained from infants upper body motion that may be useful for predicting movement disorders.
