Hand Gesture Classification Model for Intelligent Wheelchair with Improved Gesture Variance Compensation

The rapid increase in the elderly and disabled population has been identified as a growing socioeconomic problem. Due to reasons such as a lack of reliable caretakers and the need to empower the elderly and disabled population, it is important to have assistive devices. The interactive capabilities of these devices should match the nature of the interaction that prospective users would have with their companions. Humans communicate with each other in many modalities, such as speech, hand gestures, head gestures, gaze, etc. Hand gestures have been a popular modality that has been used in these interactive devices for speech and mobility-impaired wheelchair users. There have been many gesture models that have been developed recently for hand gesture-controlled wheelchair navigation. Natural hand gestures that are used in human-human interactions include both static and dynamic gestures. Therefore, it was logical to include both of these gestures in a navigational gesture model or hand gesture-controlled navigational system. However, hand tremors that are prevalent among the elderly and disabled community could affect the nature of the hand gesture. These tremors can vary from person to person, and hence the fixed ranges cannot be used for hand features. Hand features such as palm velocity, fingertip velocity, and others will have different ranges from person to person. Due to these reasons, a static hand gesture intended by the human user could be identified as a dynamic gesture. Further, this could lead to the misrecognition of gestures defined in gesture models. Therefore, a system is proposed in this paper to validate the gestures by considering the activity of hand features in 3D regions defined for the gesture. The accuracies of the improved system for static and dynamic gestures were 0.9849 and 0.9840, which were improvements from the accuracies of 0.8994 and 0.8479.