Dynamic Jointed Skeleton Model for Psychomotor Learning Feedback
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Solution Overview
Problem
Current methods for psychomotor learning, such as video image overlays, are ineffective due to size and proportion mismatches between trainees and experts, poor video resolution, unsuitable camera angles, and inability to synchronize motions, leading to limited utility in providing active feedback for improving motor skills.
Innovation Solution
A dynamic jointed skeleton (DJS) model created using AI to scale and synchronize expert movements with those of trainees, providing real-time visual feedback through augmented cognition, allowing for accurate comparison and correction of motor skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video image overlays are used to provide feedback, then visual comparison is enabled, but size and proportion mismatches between trainees and experts make the feedback ineffective
Solution Approach 1:
The system dynamically extracts skeletal models from video frames and scales them in real-time to match the trainee's body proportions. The skeletal model is not static but adapts its size and orientation based on the detected trainee dimensions, enabling accurate visual comparison despite size differences between trainees and experts.
Solution Approach 2:
The system changes the parameters of the expert's skeletal model (scale, rotation, position) to match the trainee's parameters extracted from video analysis. By transforming the expert model's physical parameters to align with the trainee's actual measurements, the system makes the feedback universally applicable across different body types.
2Reliability
If traditional coaching instruction is used, then learning retention improves beyond 75%, but high cost and scheduling inconvenience reduce accessibility
Solution Approach 1:
The system enables trainees to practice independently by capturing their own video, extracting their skeletal model, comparing it with the scaled expert model, and receiving immediate visual feedback without requiring a physical coach present. The automated feedback mechanism allows solitary practice to be as effective as coached practice.
Solution Approach 2:
The system provides immediate visual feedback by overlaying the scaled expert skeletal model on the trainee's video feed in real-time. This continuous feedback loop allows trainees to see their deviations from the expert technique instantly and correct them, replicating the instructional value of live coaching without the scheduling constraints.
3Productivity
If repetitive training under a coach is used, then neural programming is facilitated, but lack of feedback during solitary practice allows bad habits to develop
Solution Approach 1:
The system provides continuous visual feedback during solitary practice by displaying the scaled expert skeletal model overlaid on the trainee's movements. This immediate feedback allows trainees to detect and correct deviations from proper technique in real-time, preventing bad habits from forming while practicing independently.
Solution Approach 2:
The scaled skeletal model acts as an intermediary between the expert's technique and the trainee's practice. It translates the expert's movements into a visually comparable format that guides the trainee's corrections, serving as a virtual coach that maintains technique correctness during unsupervised practice sessions.
4Loss of information
If video cameras are used to capture trainee motions, then visual feedback is provided, but poor resolution and unsuitable camera angles reduce feedback quality
Solution Approach 1:
The system extracts only the essential skeletal information from the video frames rather than relying on the entire video image quality. By isolating and tracking key anatomical points (joints, limbs, torso), the system achieves precise motion capture even from lower resolution or angled camera views, filtering out the impact of poor video quality.
Solution Approach 2:
The system focuses on capturing and analyzing specific local features (skeletal keypoints) rather than requiring overall high video quality. By concentrating computational resources on detecting and tracking critical motion points, the system maintains measurement precision even when the general video resolution or camera angle is suboptimal.
Data Source
AI summary
A method of creating a scalable dynamic jointed skeleton (DJS) model for enhancing psychomotor leaning using augmented cognition methods realized by an artificial intelligence (AI) engine or image processor. The method involves extracting a DJS model from either live motion images of video files of an athlete, teacher, or expert to create a scalable reference model for using in training, whereby the AI engine extracts physical attributes of the subject including arm length, length, torso length as well as capturing successive movements of a motor skill such as swinging a gold club including position, stance, club position, swing velocity and acceleration, twisting, and more.