Statistical Database of Human Motion Recognition Using Wearable IoT—A Review

Abstract

Wearable sensors and the Internet of Things (IoT) will be two buzzwords that will be heard commonly in the coming decades. The combination of these two technologies soon will create a great revolution in applications that require motion recognition, such as health care, sports, and entertainment. The development of technology has made wearable sensors one of the most basic tools for human motion analysis. We believe that IoT is the most powerful complement to the use of wearable sensors in the analysis of human body motion. Using wearable IoT, all necessary human data will be collected and delivered via the Internet to the experts who can make accurate decisions about the type of activity, falling situations, freezing of gait (fog), and so on. In this article, the human motion analysis is presented in a chart and is divided into two parts: movement measurement and movement classification. However, this article focuses on movement classification that includes three subsections, gait analysis (GA), gesture recognition (GR), and human activity recognition (HAR), and is closely related to human motion recognition. In this article, our goal is to first acquaint the reader with the important steps required to classify the movement of the human body by wearable sensors and then by using tables to determine the most used algorithms and methods for each step. After briefly reviewing IoT concepts, directions for further research will be provided.

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

“…Robotic vision in special forms with a focus on gesture recognition has developed into a state of the art of HRI researches in [38]- [120]. Application of gesture recognition involves virtual reality [121], augmented reality [122], bio medics [123], and robotics [37].…”