Development of a low-cost wearable sensing glove with multiple inertial sensors and a light and fast orientation estimation algorithm

Correct capturing the movement of hands and fingers provides natural ways of interacting with computers. However, developing a glove-based device for such interaction has been very expensive and there were technical problems such as a complicate motion measurement algorithm in limited embedded resources and a complicate calibration process. We present a practical development of a low-cost and lightweight wearable sensing glove using only one CPU and seventeen IMUs. It transmits the captured movement data of seventeen joints of hand and wrist to a host machine via Bluetooth communication. We also propose a light and fast orientation estimation algorithm for the glove system, which should compute orientations and calibrations for seventeen inertia measurement units (IMUs) in real time. The seventeen individual IMUs are composed of an accelerometer, a gyroscope and a magnetometer based on micro electro-mechanical system technology. The magnetometer has sensor bias and scale factor errors, which vary with temperatures and places. Moreover, as the wearable sensing glove has a limited battery life and a cheap embedded processor, it can only utilize limited memory and computation power. Therefore, the algorithm should compute the attitude of the IMUs and calibrate the magnetic sensor in real time with very low computational load, by maintaining only a valid subset of data points. Our experimental results indicate that the algorithm achieves sufficient levels of real-time computation and accuracy.