Hand Movements and Gestures Characterization Using Quaternion Dynamic Time Warping Technique

Trends show that wearable devices with high-range sensors offer new business opportunities in enriching user experience in swing-based outdoor games, such as tennis, golf, and/or indoor gaming applications. Such applications providing insights into a player's abilities to play these games require methods that efficiently distinguish and capture intricacies of hand movements and/or gestures. In this paper, we show that quaternions-based dynamic time warping (QDTW) technique provides an efficient means for characterizing different arm/hand movements and gestures. A complete methodology and results for the pursued case study of outdoor tennis game are provided in this paper. We propose a new and unique approach for training data for various tennis shots and then using DTW and QDTW at the two levels of a hierarchical classifier for classification of an incoming tennis shot. The achieved accuracy for tennis shots detection is more than 99% and that for classification is 90%. Furthermore, the concept of consistency in a player's shots and how a played shot differs from a professional's similar shot are considered to suggest recommendations for improvement to the player.