Paper 1: Analysis and Selection of Features for Gesture Recognition Based on a Micro Wearable Device
Abstract: More and More researchers concerned about designing a health supporting system for elders that is light weight, no disturbing to user, and low computing complexity. In the paper, we introduced a micro wearable device based on a tri-axis accelerometer, which can detect acceleration change of human body based on the position of the device being set. Considering the flexibility of human finger, we put it on a finger to detect the finger gestures. 12 kinds of one-stroke finger gestures are defined according to the sensing characteristic of the accelerometer. Feature is a paramount factor in the recognition task. In the paper, gestures features both in time domain and frequency domain are described since features decide the recognition accuracy directly. Feature generation method and selection process is analyzed in detail to get the optimal feature subset from the candidate feature set. Experiment results indicate the feature subset can get satisfactory classification results of 90.08% accuracy using 12 features considering the recognition accuracy and dimension of feature set.
Keywords: Internet of Things; Wearable Computing; Gesture Recognition; Feature analysis and selection; Accelerometer.