Abstract
The expansion of nursing-care assistant robots in smart infrastructure has provided more applications for homecare services, which has raised new demands for smart and natural interaction between humans and robots. This article proposed an innovative hand motion trajectory (HMT) gesture recognition system based on background velocity features. Here, a new wearable wrist-worn camera prototype for gesture’s video collection was designed, and a new method for the segmentation of continuous gestures was shown. Meanwhile, a nursing-care assistant robot prototype was designed for assisting the elderly, which is capable of carrying the elderly with omnidirectional motion and grabbing the specified object at home. In order to evaluate the performance of the gesture recognition system, 10 special gestures were defined as the move commands for interaction with the robot, and 1000 HMT gesture samples were obtained from five subjects for leave-one-subject-out (LOSO) cross-validation classification with an average recognition accuracy of up to 97.34%. Moreover, the performance and practicability of the proposed system were further demonstrated by controlling the omnidirectional movement of the nursing-care assistant robot using the predefined gesture commands.
Highlights
The evolution of the Internet of Things (IoT) network has made intelligent devices more available, which offers more possibilities to facilitate people’s lives [1,2]
The performance of the hand motion trajectory (HMT) gesture recognition system is tested and verified with five assistant robot by controlling the robot’s movement based on the predefined 10 gestures using the subjects in two postures; we conducted the application on the nursingwrist-worn camera
What is different from the experiments in the application is that the HMT gesture recognition is in real time, which is based on a specified training set using a representative method of cross-validation for the classification
Summary
Geng Yang 1 , Honghao Lv 1 , Feiyu Chen 2 , Zhibo Pang 3 , Jin Wang 1 , Huayong Yang 1 and Junhui Zhang 1, *. Received: 20 September 2018; Accepted: 19 November 2018; Published: 22 November 2018. Featured Application: Interaction with nursing-care assistant robot and appliances in smart infrastructure
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