Advanced information processing of MEMS motion sensors for gesture interaction
Sensor-based gesture interaction technology has been widely adopted in consumer electronics. Nevertheless, bias, drift, and noise existing in sensor signals are difficult to eliminate, and accurate movement trajectory information is still needed to achieve flexible interaction application. This paper presents micro-electro-mechanical system (MEMS) motion sensor information processing algorithms designed on a gesture interaction system which integrates multiple low-cost MEMS motion sensors with ZigBee wireless technology to support embodied communication while acting together with machines. Sensor signal processing systems mainly solve noise removal, signal smoothing, gravity influence separation, coordinate system conversion, and position information retrieval. The attitude information which is an important movement parameter and required by position estimation is calculated with a quaternion-based extended Kalman filter (EKF). The effectiveness of the movement information retrieval of this gesture interface is verified by experiments and test analysis, both in static and moving cases. In the end, related applications of the described sensor information processing are discussed.