What is it about?

Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen’s kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen’s kappa values were calculated.

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Why is it important?

Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability

Perspectives

This paper proposed a novel method to identify hand gestures related to navigation based on a gesture recognition model with compensations for user variances. An intelligent gesture identification system was introduced in order to clarify gestures with high precision. Bone angles with respect to metacarpal bone were introduced as novel features in order to elevate identification of gesture variances. The system is capable of eliminating complications due to user inability in executing precise hand gestures. An intelligent clarification system has been implemented to separate static and dynamic hand gestures. Experimental results confirmed that the wheelchair users with speech disabilities can remember and recall the proposed hand gesture system. Therefore, the proposed gesture model can be considered as user friendly, and it is concluded that the proposed intelligent gesture recognition system can recognize user hand gestures with a high accuracy.

H.M. Ravindu T. Bandara
University of Moratuwa

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This page is a summary of: An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation, Applied Bionics and Biomechanics, January 2020, Hindawi Publishing Corporation, DOI: 10.1155/2020/9160528.
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