scholarly journals Analysis and Selection of Features for Gesture Recognition Based on a Micro Wearable Device

Author(s):  
Yinghui Zhou ◽  
Lei Jing ◽  
Junbo Wang ◽  
Zixue Cheng
Author(s):  
Eichi Tamura ◽  
◽  
Yoshihiro Yamashita ◽  
Taisei Yamashita ◽  
Eri Sato-Shimokawara ◽  
...  

Finger pointing is an intuitive method for people to direct a robot to move to a certain location. We propose a system that enables the movement operation of a mobility robot by using finger-pointing gestures for an automatic and intuitive driving experience. We employ a method to recognize gestures by using video images from a USB camera mounted on a wearable device. Our method does not require the use of infrared sensors. Three movement commands for forward motion, turning, and stopping are chosen based on gesture recognition, face orientation detection, and an intelligent safety system. We experimentally demonstrate the usefulness of the system using a scooter-type mobility robot.


2020 ◽  
Vol 17 (1) ◽  
pp. 177-181 ◽  
Author(s):  
Amritha Purushothaman ◽  
Suja Palaniswamy

Smart home has gained popularity not only as a luxury but also due to the numerous advantages. It is especially useful for senior citizens and children with disabilities. In this work, home automation is achieved using gesture for controlling appliances. Gesture recognition is an area in which lot of research and innovations are blooming. This paper discusses the development of a wearable device which captures hand gestures. The wearable device uses accelerometer and gyroscopes to sense and capture tilting, rotation and acceleration of the hand movement. Four different hand gestures are captured using this wearable device and machine learning algorithm namely Support Vector Machine has been used for classification of gestures to control ON/OFF of appliances.


2020 ◽  
Vol 25 (6) ◽  
pp. 2447-2458 ◽  
Author(s):  
Shu Shen ◽  
Kang Gu ◽  
Xin-Rong Chen ◽  
Cai-Xia Lv ◽  
Ru-Chuan Wang

2019 ◽  
Vol 3 (6) ◽  
pp. 7-8
Author(s):  
Jingwei Dai

This paper is based on gesture recognition. It aims to transform gesture signal into electronic communication with home facilities. The main procedure consists of following steps: First, Curie chip collects the command gesture. Then, the gesture will be translating into accelerometer and gyroscope information. The Curie chip will integrate them into a motion vector and transport them to the k-means neural network as training. After training, each gesture represents a command attached to a facility control, and gesture control is revealed for smart home. Additionally, the whole system can be integrated as a wearable device, which relives complex button controller and improve portability. In brief, the research aims to replace remote controller and create a more convenient and pleasant living environment for users.


2020 ◽  
Vol 20 (24) ◽  
pp. 14703-14712
Author(s):  
Meng-Kun Liu ◽  
Yu-Ting Lin ◽  
Zhao-Wei Qiu ◽  
Chao-Kuang Kuo ◽  
Chi-Kang Wu

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