Multi-Sensor Hitting Action Recognition for Sports Motion Monitoring
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Solution Overview
Problem
Current wearable smart devices in ball sports, such as badminton and tennis, cannot accurately recognize hitting actions, limiting comprehensive analysis of a player's sports capability due to the inability to measure lower limb and vital sign parameters during hitting.
Innovation Solution
A motion monitoring system comprising multiple data collection apparatuses at specific locations (ankle, racket, and shooting locations) collects motion data to determine gait, swing, and image characteristics, using foot acceleration, hand angular velocity, and image analysis to accurately recognize hitting actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only basic wearable smart devices are used, then device complexity is reduced, but measurement precision of hitting actions deteriorates
Solution Approach 1:
The system divides the data collection function into multiple independent apparatuses: a first data collection apparatus on the player's body, a second data collection apparatus on the racket, and a third data collection apparatus for image capture. Each apparatus collects specific types of motion data independently, which are then integrated for comprehensive hitting action recognition.
Solution Approach 2:
The system uses multiple types of sensors and data collection methods (acceleration sensors, gyroscopes, image cameras) that can serve multiple purposes. For example, the motion data collection apparatus can detect both player movement and racket movement, while image data can provide both visual confirmation and supplementary motion information.
2Measurement precision
If multiple data collection apparatuses are deployed, then measurement precision of hitting actions is improved, but device complexity increases
Solution Approach 1:
The system specifically segments the data collection function by placing a first data collection apparatus on the player's lower body to capture lower limb motion data, a second apparatus on the racket for upper limb data, and a third apparatus for image capture. This segmentation enables precise measurement of lower limb actions during hitting while distributing system complexity across multiple specialized components.
3Reliability
If comprehensive motion data is collected from multiple sources, then reliability of sports capability analysis is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary classification and filtering of motion data immediately after collection from each apparatus. Data are organized into categories (player motion, racket motion, image data) with key features extracted in advance, preparing them for faster integrated processing and reducing overall data processing time.
Data Source
AI summary
This application relates to the field of electronic technologies, and provides an action recognition method and apparatus, a terminal device, and a motion monitoring system. Characteristic extraction and action recognition are performed based on motion data collected by data collection apparatuses; a gait characteristic, a swing gesture characteristic, and an image action characteristic of a user are recognized by using a plurality of pieces of motion data; and a type of a hitting action of a player is determined based on the gait characteristic, the swing gesture characteristic, and the image action characteristic. In this way, the hitting action of the player in a motion process can be accurately recognized. This is conducive to performing comprehensive analysis on a comprehensive sports capability of the player, and is more helpful to formulating a personalized training plan for the player.