Ai Yano

Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices

Mobile phones or video game controllers using gesture recognition technologies enable easy and intuitive operations. However, usually only one of each type of sensor is installed in each device, and the effect of multiple sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We captured data for 27 kinds of gestures by using a mobile device with 9 accelerometers and 9 gyroscopes, we then experimentally investigated the effects on recognition accuracy of changing the number and positions of sensors, and the number and kinds of gestures. The results showed that the use of multiple sensors and of sensors positioned at specific positions affects accuracy. It was also shown that gestures are interdependent and selecting specific gestures improves recognition accuracy.

Evaluation study on sensor placement and gesture selection for mobile devices

Mobile phones and video game controllers using gesture recognition technologies enable easy and intuitive operations, such as scrolling a browser and drawing objects. However, usually only one of each kind of sensor is installed in a device, and the effect of multiple homogeneous sensors on recognition accuracy has not been investigated. Moreover, the effect of the differences in the motion of a gesture has not been examined. We have investigated the use of a test mobile device with nine accelerometers and nine gyroscopes. We have captured the data for 27 kinds of gestures for a mobile tablet. We experimentally investigated the effects on recognition accuracy of changing the number and positions of the sensors and of the number and kinds of gestures. The results showed that the use of multiple homogeneous sensors has zero or negligible effect on recognition accuracy, but that using an accelerometer along with a gyroscope improves recognition accuracy. They also showed that some gestures were not consistent among test subjects and interdependent, so selecting specific gestures to use can improve recognition accuracy.

Estimating Trajectory of Inhabitants with Sparsely Aligned Infrared Sensors

We propose in this paper a method for estimating trajectories of the inhabitants in a home environment, which exploits the synergy between location and movement to provide the information necessary for intelligent home appliance control. Our goal is to carry out accurate movement estimation for multiple people in a home environment. We propose an approach that uses information gathered using only passive infrared sensors commonly found in lighting control systems. No special devices or video cameras are needed. Moreover, it is not necessary to carry out data collection for training. We evaluated our approach by conducting experiments in a real home fitted with sensors and we confirmed that trajectories were almost completely detected for four inhabitants who moved upon scenarios.