Recognizing Hand Gestures With Pressure-Sensor-Based Motion Sensing

This paper proposes a novel framework to process pressure signals for real-time and robust gesture recognition, which includes an innovative segmentation scheme, a gesture recognition scheme and a pressure-parameter adaptive updating strategy. A prototype system, including a wearable gesture sensing device with four pressure sensors and the corresponding algorithmic framework, is developed to realize real-time gesture-based interaction. With the device worn on the wrist, the user can interact with the computer using 8 predefined gestures. Experimental results show that the delay of gesture recognition is about 100 ms, with the average accuracy of 95.28% in the experienced-user test and 86.20% in the inexperienced-user test. Finally, the system is evaluated by a mouse-controlling interaction task and performs well. Both experienced and inexperienced people can easily and quickly complete interactive tasks. These results demonstrate that a pressure-sensor based wristband can be used to classify hand gestures well and to control the mouse interaction. This approach provides an interactive way to replace the mouse for decreasing the risk of the carpal tunnel syndrome (CTS).