Dynamic and interactive gesture recognition algorithm based on Kinect

Dai, Guangjun; Lei, Yu; Huang, Jun

doi:10.1109/ccdc.2017.7979108

Cited by 4 publications

(1 citation statement)

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“…Kinect is one of the popular products which has been widely used in this area and has improved the recognition accuracy. As Kinect successfully extracts the gesture from background, many machine learning algorithms have combined to this application such as neural network (NN), 17 support vector machine (SVM), 18 K-means, 19 Markov model, 20 and AdaBoost 21 with promising recognition accuracy. All those gesture recognition as mentioned has high accuracy when it identifies relatively dramatical movements of hand and fingers.…”

Section: Introductionmentioning

confidence: 99%

The use of a cascaded Kinect and electromyography gesture decoding algorithm in an initial robot-aided hand neurorehabilitation

Wang

Huang

et al. 2018

Advances in Mechanical Engineering

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In order to improve the neurological recovery of hand neurorehabilitation, target-oriented, intensive, repetitive activities of daily living are used, such as training with recognition of hand gestures during robot-aided exercise. In this article, a cascade control algorithm integrating electromyography bio-feedback into hand gesture recognition is proposed. The outer loop is the trajectory motion tracking with Kinect-based gesture decoding classifier, and the inner loop is torque control with electromyography bio-feedback in the real time. This proposed method improves the tracking accuracy. The tracking error is effectively reduced from 70.56 to 28.07 in the simulation experiment. The initial test proves that the proposed method with additional torque control allows active assistance on the human-machine interface of other rehabilitation robots in future.

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