A Wearable Wireless Sensing System for Capturing Human Arm Motion

Compact wearable technology is required in a wide variety of fields including engineering, medical and sport science. The usability of wearable technology is versatile in its application, where it can be used to monitor and track the human movement including clinical applications. Classification studies of trajectory data are required for a diversity of hand and limb movements tracking experiments. Automatic classification using machine learning techniques has the potential to increase the reliability and efficiency of predicting the outcome of results without the need of human manual intervention. This work presents a wearable sensing electronic device for tracking and classify real-time hand strike motion in a combat sport activity. The developed low-cost system consists of a small footprint printed-circuit board of dimensions 11 mm x 24 mm x 4 mm with integrated motion sensors operating at 3.3 V, contributing to a battery running time of 3 hours. This meets the requirements for the targeted application. The K-Nearest Neighbor machine learning algorithm was adopted for the classification of hand combat techniques, yielding a classification and an optimal strike prediction accuracy of 99%, using only 20% of the available dataset.