Transtrack: Online meta-transfer learning and Otsu segmentation enabled wireless gesture tracking

•Individual diversity causes poor performance of the current gesture tracking systems when they were directly applied to new users.•An online meta-transfer learning method to learning the individual characters with low data collection cost.•A data augmentation method that leverages the redundant information to generate virtual instances at the premises of the accurate detection result of recursive Otsu segmentation.•A datum-based data alignment strategy that breaks the limitation of available classifiers for recognition without distort the instance.