Abstract:
To detect the person's intention for the control of upper-limb exoskeleton robot, we propose a recognition frame of continuous hand gestures. This frame is mainly concent...Show MoreMetadata
Abstract:
To detect the person's intention for the control of upper-limb exoskeleton robot, we propose a recognition frame of continuous hand gestures. This frame is mainly concentrated on dynamic segmentation and real time gesture recognition based on sEMG. The hand gesture was modeled and decomposed by the use of Gaussian Mixture Model-Hidden Markov Models (GMM-HMM). GMMs are employed as a sub-states of HMMs to decode sEMG feature of gesture. The log-likelihood threshold and KL-divergence threshold are adopted to select target gesture model. In myoelectric control schemes, the sEMG data are collected by Myo armband 8-channels sEMG sensors. The proposed framework has ideal classification accuracy and its simpler acquisition armband make it attractive to a real-time myoelectric control system.
Date of Conference: 05-08 December 2017
Date Added to IEEE Xplore: 26 March 2018
ISBN Information:
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- IEEE Keywords
- Index Terms
- Threshold Model ,
- Hand Gestures ,
- Gesture Recognition ,
- Continuous Recognition ,
- Hand Gesture Recognition ,
- Upper Limb ,
- Hidden Markov Model ,
- sEMG Data ,
- Dynamic Process ,
- Signal Processing ,
- Probabilistic Model ,
- Probability Density Function ,
- Transition Probabilities ,
- 10-fold Cross-validation ,
- Signal Values ,
- Recognition Accuracy ,
- Root Mean Square Values ,
- K-means Algorithm ,
- Window Analysis ,
- Probability Matrix ,
- sEMG Signals ,
- Activation Segment ,
- Gaussian Mixture Distribution ,
- Wrist Extension ,
- Log-likelihood Values ,
- Data Feature Extraction
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Threshold Model ,
- Hand Gestures ,
- Gesture Recognition ,
- Continuous Recognition ,
- Hand Gesture Recognition ,
- Upper Limb ,
- Hidden Markov Model ,
- sEMG Data ,
- Dynamic Process ,
- Signal Processing ,
- Probabilistic Model ,
- Probability Density Function ,
- Transition Probabilities ,
- 10-fold Cross-validation ,
- Signal Values ,
- Recognition Accuracy ,
- Root Mean Square Values ,
- K-means Algorithm ,
- Window Analysis ,
- Probability Matrix ,
- sEMG Signals ,
- Activation Segment ,
- Gaussian Mixture Distribution ,
- Wrist Extension ,
- Log-likelihood Values ,
- Data Feature Extraction
- Author Keywords