![]() ![]() Leap Trainer (LeapTrainer: (accessed on 12 March 2018)) for gesture design. Multi LMC, covariance intersection and Kalman (fusion), FPs, joints HMM(recognition )Īccuracy: Multi LMC ≥ 84.68%, Single LMC ≥ 68.78% ![]() Recognition with machine learningħ2.78% (k-NN), 79.83% (SVM) recognition rate LMC hand tracking, Tool Center Point (TCP) mapped to hand positionįingertip Positions (FPs) mapped to robot TCPįPs, position of joints, tip velocity, pinch strength. Hand position tracking, map gestures to robot commands Rotation gesture and grab strength inverse kinematics LMC SDK-Hand model values k-NN, NN, SVM, logistic regression, functional trees, logic trees Normalization scheme and DTW to calculate distance between gesturesĩ9% static, 98% dyn. Gaussian skin colour model LDA dimension reduction and classificationįinger length and distance to palm NB, RDF and NNĪcceptance rate (1% false positive): 75.78% (NB), 78.04% (RDF), 78.55% (NN) provide a detailed survey on Kinect based gesture recognition systems EMG data compared with BioFlex EMG sensors. LMC evaluated with optical motion capture system. ![]()
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