A paper entitled 'Selection of Sensors Suitable for Integration into Smart Sport Equipment' has been published recently in Sensors (MDPI). This paper is a joint work with colleagues of University of Ljubljana, Slovenia, and Dr Yuan Zhang is the corresponding author. This paper can be accessedhere.

Abstract: Wearable devices and smart sport equipment are being increasingly used in amateur and professional sports. Smart sport equipment employs various sensors for detecting its state and actions. The correct choice of the most appropriate sensor(s) is of paramount importance for efficient and successful operation of sport equipment. When integrated into the sport equipment, ideal sensors are unobstructive, and do not change the functionality of the equipment. The article focuses on experiments for identification and selection of sensors that are suitable for the integration into a golf club with the final goal of their use in real time biofeedback applications. We tested two orthogonally affixed strain gage (SG) sensors, a 3-axis accelerometer, and a 3-axis gyroscope. The strain gage sensors are calibrated and validated in the laboratory environment by a highly accurate Qualisys Track Manager (QTM) optical tracking system. Field test results show that different types of golf swing and improper movement in early phases of golf swing can be detected with strain gage sensors attached to the shaft of the golf club. Thus they are suitable for biofeedback applications to help golfers to learn repetitive golf swings. It is suggested that the use of strain gage sensors can improve the golf swing technical error detection accuracy and that strain gage sensors alone are enough for basic golf swing analysis. Our final goal is to be able to acquire and analyze as many parameters of a smart golf club in real time during the entire duration of the swing. This would give us the ability to design mobile and cloud biofeedback applications with terminal or concurrent feedback that will enable us to speed-up motor skill learning in golf.

最近,USLab与斯洛文尼亚University of Ljubljana的三位同行合作,在MDPI期刊Sensors发表了论文“Selection of Sensors Suitable for Integration into Smart Sport Equipment”。张远博士是该论文通讯作者,论文提出了将合适的传感器嵌入智能球杆,以辅助高尔夫球手挥杆训练的新方法,全文可以在此处获取。