A signal pattern recognition approach for mobile devices and its application to braking state classification on robotic mobility devices

Personal Mobility Robots, such as the Seqway may be the remedy for the transportation related problems in the congested environment, especially for the last and first mile problems of the elderly people. However, the vehicle segmentation issues for the mobility robots, impede the use of these devices on the shared paths. The mobility reports can only be used in the designated areas and private facilities. The traffic regulatory institutions lack robot-society interaction database. In this study, we proposed methods and algorithms which can be employed on a widespread computing device, such as an Android tablet, to gather travel information and rider behavior making use of the motion and position sensors of the tablet PC. The methods we developed, first filter the noisy sensor readings using a complementary filter, then align the body coordinate system of the device to the Segway's motion coordinate. A couple of state of the art classification methods are integrated to classify the braking states of the Segway. The classification algorithms are not limited to classification of the braking states, but they can be used for other motion related maneuvers on the road surfaces. The detected braking states and the other classified features related to the motion are reflected to the screen of the Android tablet to inform the rider about the riding and motion conditions. The developed Android application also gathers these travel information to build a National database for further statistical analysis of the robot-society interaction.