Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

Abstract

In the design of gesture-based user interfaces, continuously recognizing complex dynamic gestures is a challenging task, because of the high-dimensional information of gestures, ambiguous semantic meanings of gestures, and the presence of unpredictable non-gesture body motions. In this paper, we propose a hybrid model that can leverage the time-series modeling ability of hidden Markov model and the fuzzy inference ability of fuzzy neural network. First, a complex dynamic gesture is decomposed and fed into the hybrid model. The likelihood probability of an observation sequence estimated by the hidden Markov model is used as fuzzy membership degree of the corresponding fuzzy class variable in fuzzy neural network. Next, fuzzy rule modeling and fuzzy inference are performed by fuzzy neural network for gesture classification. To spot key gestures accurately, a threshold model is introduced to calculate the likelihood threshold of an input pattern and provide a reliability measure of whether to accept the pattern as a gesture. Finally, the proposed method is applied to recognize ten user-defined dynamic gestures for controlling interactive digital television in a smart room. Results of our experiment show that B Huiyue Wu the proposed method performed better in terms of spotting reliability and recognition accuracy than conventional gesture recognition methods.

Brain Imaging Big Data Mining and Fusion Method Based on Cognitive Intelligence

“…Self-organization is essentially a dynamic process in which the ant colony algorithm mechanism increases the system without external influences, and reflects the dynamic evolution from disorder to order. The first ant algorithm was proposed by Dorigo and is called the ant system [12,13]. The ant system incorporates heuristic information and designs the transition probability k ij p ,and the taboo table has been added to enhance the algorithm memory function.…”

“…In formula (13), n represents the total number of sample attributes of the training set, and i b is the number of values in the value field of the attribute ij D .…”

“…In the specific experiment, 1200 data instances were randomly selected for label clustering mode initialization, and then other unlabeled data were equally divided into three in random order to join the existing clustering results to complete the incremental clustering. For the K-means and BigDataACO algorithms, the initial cluster number set [6,5,12,16,13] five different values to complete the experiment, the DS evidence theory and the Bayesian algorithm's iteration number and shared feature dimension are set to [121 , 260, 400] and [6,8,12]. Each experiment was performed 20 times randomly and the cluster average results were recorded.…”

Understanding freehand gestures: a study of freehand gestural interaction for immersive VR shopping applications

“…The Form dimension distinguishes between static and dynamic gestures. Static gesture refers to hand shape or finger configuration while dynamic gesture involves the spatiotemporal movement of the hand [42].…”

“…The gestural VR system was implemented based on the Leap Motion SDK, Virtual Reality Toolkit (VRTK) and the Unity game engine. Using our gesture recognition toolkit [42,44,46], the 10 userdefined gestures were tested on an average recognition rate of 98.6%. We used a web camera to capture the participants' gesture behaviors and voices.…”