Kinect Badminton Training System with Dynamic Thresholds
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Solution Overview
Problem
Existing Kinect-based badminton movement guidance systems have a limited application range and low accuracy due to the lack of consideration for individual differences such as age, posture, gender, and dominant hand, and inadequate threshold determination, which affects the recognition of basic badminton movements.
Innovation Solution
A Kinect-based auxiliary training system that uses a data collection module with a Kinect v2 device to collect three-dimensional coordinate data from 25 joint points, a movement feature extraction and recognition module that employs included angle features of key bones to establish a standard template and recognize user movements, and a movement standard degree analysis and guidance module that determines the similarity and sets thresholds based on normal distribution and training experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DTW algorithm and similarity measurement method are adopted to recognize and evaluate badminton movements, then movement recognition capability is provided, but application range is limited and accuracy is low due to lack of consideration for individual differences
Solution Approach 1:
The patent applies local quality by selecting and extracting only the key bone included angle features that are most relevant to badminton movements, rather than using all global torso features. This localized feature extraction focuses on the specific anatomical regions and movement characteristics that matter for badminton, improving recognition accuracy while reducing computational complexity and enabling better adaptation to individual differences.
Solution Approach 2:
The patent implements dynamics by establishing dynamic threshold ranges for bone included angles based on normal distribution of training data and coaching experience. Instead of using fixed thresholds, the system adapts thresholds according to the statistical characteristics of different movements and individual variations, allowing the recognition system to dynamically adjust to different athletes, ages, and movement styles, thereby expanding application range while maintaining accuracy.
2Reliability
If global torso features are collected for movement recognition, then comprehensive movement data is obtained, but calculation complexity increases and recognition accuracy is affected
Solution Approach 1:
The patent applies the extraction principle by isolating and selecting only the essential bone included angle features from the complete set of torso joint data. Instead of processing all global torso features, the system extracts specific included angles from key bones involved in badminton movements (such as arm, leg, and trunk bones), removing redundant information and reducing calculation complexity while maintaining recognition reliability.
Solution Approach 2:
The patent applies segmentation by dividing the complex torso movement analysis into separate, manageable bone segment analyses. Each bone's included angles are calculated and evaluated independently based on its specific role in badminton movements. This segmented approach breaks down the complex global feature calculation into simpler local calculations, reducing overall computational complexity while improving reliability through focused analysis of movement-critical segments.
Data Source
AI summary
A Kinect-based auxiliary training system for basic badminton movements, includes a data collection module, a movement feature extraction and recognition module, and a movement standard degree analysis and guidance module. The data collection module is provided with a Kinect v2 somatosensory device for monitoring athletes in real time, and collecting 3D coordinate data of 25 joint points of athletes' whole body. The movement feature extraction and recognition module is provided for establishing a standard template, and obtaining a similarity between the movement data and the standard template. The movement standard degree analysis and guidance module is provided for determining a category of the current movement of the user to be tested according to the similarity, and further analyzing whether the current movement of the user to be tested meets a standard according to a threshold range of the bone included angle set by a technology evaluation rule.