In this study, a wearable sensing device (WSD) based on microelectromechanical systems motion sensors (an inertial measurement unit consisting of sensors with three axes of acceleration and three axes of angular rate) was built to assess the skill levels of volleyball spikers. The developed WSD is inexpensive and requires much less computational power than conventional videography analysis in monitoring motions of volleyball players during spikes. This paper presents the hardware and software design and the data processing algorithms used in the system. Six right-handed male subjects wore the WSD on their wrists and performed 120 spiking trials in a volleyball court. Skill of the volleyball spikers was accessed by classifying them into three different levels from the recorded data with support vector machine. The results demonstrate that this system is capable of assessing the difference between elite, sub-elite, and amateur volleyball players with an average accuracy of 94%. The proposed method can be extended to analyze the skill levels of players in other sports, where wrist actions are important (e.g., basketball, badminton, and baseball).
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