A Controlled Experiment Between Two Methods on Ten-Digits Air Writing
Yiming Zhou, Zeyang Dai, Lei Jing · 2016
Recently, gesture recognition by the motion sensor embedded in wearable device has gradually been a popular research topic in pervasive computing. Traditionally, most of gesture recognition researches are based on sensor waveform of time and frequency domains. Another method based on trajectory estimation is emerging in gesture recognition. This method seems like more effect since it can extract more features compared with waveform-based recognition. But a quantitative experiment have not been taken to compare the trajectory-based method with waveform-based method. In this paper, we present an argument that the method based on trajectory estimation can provide higher and more stable recognition. To verify our viewpoint, we performed a controlled experiment based on an online finger gesture recognition system. As the result of our experiment, comparing trajectory estimation method with waveform method, we found that the recognition rate of trajectory-based method can reach 96.20% (SD) in userdependent and 91.20% accuracy in user-independent experiments respectively, which outperform waveform method by 4.9% and 3.9%.