Modern smart phone devices are equipped with several space positioning MEMS sensors. Most of them are inaccurate low-cost silicon devices, not designed for motion tracking. The paper presents the results of several constraint motion tracking experiments using iPhone 4 sensors. Motion tracking errors can be reduced through sensor fusion - a simultaneous usage of accelerometer and gyroscope data. The experiments confirm that iPhone MEMS are accurate enough for short-time motion tracking (up to 10 s), limiting their use to confined space.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 10612564In this paper we present an autonomous wearable personal training system, which includes a gesture user interface and real-time biofeedback. The system employs inertial sensors of a smartphone attached to the body. The system processes sensor data and provides users with real-time audio feedback. The operation of the system is driven by user gestures, defined by user’s body movements that are detected through their characteristic inertial sensor responses. As an example of such a system, we have designed the application for golf swing error detection and correction. The application helps golfers correct the unwanted head movements during the golf swing, when it provides them with the real-time audio feedback, signaling head movement errors. Field test results show that our system is an efficient tool for the detection and correction of head movement errors during the golf swing.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 10906964