Calibration of kinematic body sensor networks: Kinect-based gauging of data gloves “in the wild”

Abstract

Our hands generic precision and agility is yet unmatched by technology, hence the quantitative study of its daily life kinematics is fundamental to neurology/prosthetics & robotics and creative industries. State-of-the-art solutions capturing hand movements ‘in the wild’ requires wearable body sensor networks: data gloves. Yet, fast-accurate calibration is challenging due to variability in hand anatomy and complexity of finger joints. We present here novel methods for calibration using streaming information from depth cameras (Microsoft Kinect). Our low-cost system calibrates the data glove by observing a user wiggling their hands while wearing data gloves. Using inverse kinematics we reconstruct in real-time hand configuration, enabling augmented reality by superimposing the virtual and real hand veridically. We achieve accuracies of ±5 degrees RMSE over all 21 joints, almost 20% more accurate than standard calibration methods and accurately capture touching of fingertips and thumb — our benchmark test unmatched by other calibration methods.