Department of Kinesiology and Physical Education, McGill Research Centre for Physical Activity and Health, McGill University, 475 Pine Avenue West, Montreal, QC H2W 1S4, Canada.
Centre for Interdisciplinary Research in Rehabilitation, Lethbridge-Layton-MacKay Rehabilitation Centre, the School of Physical and Occupational Therapy, McGill University, Montreal, QC H2W 1S4, Canada.
Sensors (Basel). 2022 Apr 29;22(9):3419. doi: 10.3390/s22093419.
The aims of this study were to evaluate the feasibility of using IMU sensors and machine learning algorithms for the instantaneous fitting of ice hockey sticks. Ten experienced hockey players performed 80 shots using four sticks of differing constructions (i.e., each stick differed in stiffness, blade pattern, or kick point). Custom IMUs were embedded in a pair of hockey gloves to capture resultant linear acceleration and angular velocity of the hands during shooting while an 18-camera optical motion capture system and retroreflective markers were used to identify key shot events and measure puck speed, accuracy, and contact time with the stick blade. MATLAB R2020a's Machine Learning Toolbox was used to build and evaluate the performance of machine learning algorithms using principal components of the resultant hand kinematic signals using principal components accounting for 95% of the variability and a five-fold cross validation. Fine k-nearest neighbors algorithms were found to be highly accurate, correctly classifying players by optimal stick flex, blade pattern, and kick point with 90-98% accuracy for slap shots and 93-97% accuracy for wrist shots in fractions of a second. Based on these findings, it appears promising that wearable sensors and machine learning algorithms can be used for reliable, rapid, and portable hockey stick fitting.
本研究的目的是评估使用 IMU 传感器和机器学习算法对冰球杆进行即时拟合的可行性。十名经验丰富的曲棍球运动员使用四支不同结构的球杆（即每支球杆在硬度、刀片图案或踢点上有所不同）进行了 80 次射门。定制的 IMU 嵌入在一对曲棍球手套中，以在射门过程中捕获手部的合成线性加速度和角速度，同时使用 18 个摄像机的光学运动捕捉系统和反射标记来识别关键射门事件并测量冰球速度、准确性和与球杆叶片的接触时间。MATLAB R2020a 的机器学习工具箱用于使用手部运动学信号的主成分构建和评估机器学习算法的性能，使用占变异性 95%的主成分和五重交叉验证。精细 k-最近邻算法被发现具有很高的准确性，能够在几分之一秒内以 90-98%的准确率正确分类最佳球杆硬度、刀片图案和踢点的运动员，以 93-97%的准确率正确分类腕球运动员。基于这些发现，似乎很有希望使用可穿戴传感器和机器学习算法进行可靠、快速和便携式冰球杆拟合。
