A robust and fast gesture recognition method for wearable sensing garments

There is an increasing demand for motion capture and full body gesture recognition due to fast paced ubiquitous computing developments and their requirements for natural input modalities. Analysis and synthesis of human body movements are significant issues in bio-medical applications for rehabilitation and identification purposes as in post-stroke patient rehabilitation and gait recognition studies. This paper presents the development of a robust gesture and posture recognition algorithm based on an emerging research field Compressed Sensing (CS) and Sparse Representation (SR), in signal processing for the wearable sensing garment which consists of a sensor network having piezo-resistive properties. The gesture recognition algorithm presented in this study is highly accurate regardless of the signal acquisition method used and gives excellent results even for high dimensional signals and large gesture dictionaries. Our findings state that gestures can be recognized with over 99% accuracy rate using Sparse Representation-based Classification (SRC) algorithm. We tested the algorithm using 3 different gesture dictionaries acquired in 3 different gesture domains with user dependent and user independent test gesture and dictionaries. The system gives 100% recognition accuracy for the gestures performed by sensing t-shirt with two different gesture sets.