Motion Analysis System Using Inter-Frame Differential Frames
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Solution Overview
Problem
Existing motion analysis techniques fail to consider the relationship between a person and the tool during a motion, limiting their effectiveness in analyzing prescribed motions performed using tools like golf clubs.
Innovation Solution
A motion analysis system that generates a predictive model using deep learning on video data and inter-frame differential frames to accurately predict the position of a measurement portion, such as a golf club head, by considering the tool's movement and its interaction with the person.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion analysis techniques are used, then the analysis process is simple, but the analysis accuracy is insufficient because the relationship between person and tool is not considered
Solution Approach 1:
The system segments the motion analysis into two distinct input streams: one for capturing the person's motion and another for capturing the tool's motion. By processing these separately and then integrating the results, the system achieves higher analysis accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system merges the motion data from the person and the tool by integrating their respective coordinate information. This combination allows the system to analyze the relationship between person and tool movements, significantly improving analysis accuracy for tool-assisted motions.
2Measurement precision
If deep learning models are trained separately for person and tool, then the training complexity increases, but the position prediction accuracy improves
Solution Approach 1:
The deep learning architecture is segmented into two independent prediction models: one dedicated to predicting person keypoint positions and another for tool keypoint positions. Each model is trained separately on its specific data, improving prediction accuracy while allowing independent optimization without increasing overall system complexity.
Solution Approach 2:
The system employs a universal coordinate transformation framework that works for both person and tool predictions. This multi-functional approach allows the same mathematical transformation process to be applied to both input streams, maintaining consistency and reducing complexity despite having separate prediction models.
3Measurement precision
If coordinate transformation is applied to align person and tool coordinates, then the analysis precision improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary coordinate transformations during the training phase to establish transformation matrices between person and tool coordinate systems. By pre-computing these transformations and storing them as learned parameters, the system achieves high coordinate alignment precision during inference without requiring heavy computational power at runtime.
Solution Approach 2:
The system replaces complex real-time mechanical coordinate transformation calculations with a learned transformation model from deep learning. Instead of performing heavy mathematical transformations during each prediction, the system uses the neural network to directly output transformed coordinates, significantly reducing computational power requirements while maintaining high precision.
Data Source
AI summary
A motion analysis system analyzing a prescribed motion performed by a person using a prescribed tool, with a prescribed portion of the person or portion of the tool in the motion being taken as a measurement portion, a predictive model generated by learning based on learning data including an image frame of a motion video, wherein the person performing the motion, and an inter-frame differential frame indicating a difference in pixel values between frames of each pixel of the image and an adjacent frames, which is adjacent to the image frame, in the motion video; generates an inter-frame differential frame of a given analysis object video; and predicts by using the predictive model a position of the measurement portion in an image frame of the analysis object video on the basis of the image frame of the analysis object video and the inter-frame differential frame of the analysis object video.