集成电路与嵌入式系统2024，Vol.24Issue(10)：56-61,6.DOI:10.20193/j.ices2097-4191.2023.0002
基于机器学习的羽毛球挥拍动作识别算法比较研究
Comparative study of badminton swing motion recognition algorithms based on machine learning
摘要
Abstract
Despite the intense attention to badminton,there are relatively few intelligent devices specifically designed for this sport.Therefore,this study verifies algorithms based on traditional shallow machine learning models such as Random Forest(RF),K-Nearest Neighbor(K-NN),Gradient Boosting(GB),Support Vector Machine(SVM),and Long Short-Term Memory(LSTM)deep learning models to accurately identify five common badminton swing actions:overhead forehand stroke,aerial backhand stroke,smash,under-arm forehand strokes,and underarm backhand stroke.This study collected 1 800 sets of swing motion data samples from 12 athletes u-sing a wireless inertial sensor module fixed at the bottom of the badminton racket grip.Low power Bluetooth was used for data trans-mission and collection.The real-time data collected was intercepted using a window cutting method that combines action window and sliding window.The feature of the intercepted action data was extracted.RF,K-NN,GB,SVM,and LSTM models were used to learn and verify the recognition of five swing movements during the experiment.The experimental results showed that LSTM reached a recognition accuracy of 99.42％,significantly outperforming traditional machine learning algorithms.Additionally,this paper selects STM32F476 ARM microcontroller as the edge computing unit,and deploys the badminton swing action recognition model into it.This deployment enables real-time inference and recognition of badminton swing types by athletes,demonstrating effective recognition per-formance.关键词
动作识别/机器学习/窗口分割/STM32F476Key words
action recognition/machine learning/window segmentation/STM32F476分类
信息技术与安全科学引用本文复制引用
郭容,杨健科,张佳进..基于机器学习的羽毛球挥拍动作识别算法比较研究[J].集成电路与嵌入式系统,2024,24(10):56-61,6.基金项目
云南农业大学新文科研究与改革实践项目(YNAU2021XGK06) （YNAU2021XGK06）
云南农业大学校级一流本科课程建设项目(2021YLKC126). （2021YLKC126）
