Data glove based on hand motion model for mine equipment training

Authenticity of interactive operation is poor when virtual reality technology adopted in mine equipment training. Data processing algorithm is optimized to reduce the number of micro-inertial sensors for data glove but results in low accuracy. According to the above problems, on the basis of introducing human hand structure and hand motion mechanics model, basic movement and motion characteristics of mine equipment training were analyzed, and hand motion model for mine equipment training of two-finger, six-link and seven-degree of freedom was put forward, so as to reduce the number of micro-inertial sensors. Position and attitude calculation algorithm of phalanges was deduced based on quaternion method. A data glove based on the hand motion model for mine equipment training was developed. Tracker is fixed at wrist of the glove to obtain absolute coordinates of forearm, micro-inertial sensors are arranged at thumb and forefinger of the glove to provide carrier coordinates of phalanges, and position and attitude of phalanges in navigation coordinate system are calculated through calculation of position and attitude of phalanges, so as to realize real-time generation of hand motion attitude in virtual scene. The test results show that the maximum error of basic movements of mine equipment training is 9 mm, which is smaller than the minimum diameter of control, and meets needs of mine equipment training operation.