Real time motion intention recognition method with limited number of surface electromyography sensors for A 7-DOF hand/wrist rehabilitation exoskeleton

Mechatronics ◽  
2021 ◽  
Vol 79 ◽  
pp. 102642
Author(s):  
Feiyun Xiao ◽  
Liang Gu ◽  
Wenzhang Ma ◽  
Yanhe Zhu ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Surface Electromyography ◽  
Recognition Method ◽  
Time Motion ◽  
Intention Recognition ◽  
Motion Intention

scholarly journals A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2176
Author(s):  
Lu Zhu ◽  
Zhuo Wang ◽  
Zhigang Ning ◽  
Yu Zhang ◽  
Yida Liu ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Human Motion ◽  
Measurement Unit ◽  
Recognition Method ◽  
Metabolic System ◽  
Intention Recognition ◽  
Motion Modes ◽  
Deep Convolution Neural Network ◽  
Multi Mode ◽  
Motion Intention

To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.

scholarly journals Real-time motion detection in H.264 compressed domain for surveillance application

2021 ◽  
Vol 1780 (1) ◽  
pp. 012032
Author(s):  
Young Keun Kim ◽  
Yeong Gyoo Jeon ◽  
Seung Ho Shin
Keyword(s):  
Real Time ◽  
Motion Detection ◽  
Compressed Domain ◽  
Time Motion ◽  
Surveillance Application

A real-time motion planning scheme for collaborative robots using HRI-based cost function

2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Khawaja Fahad Iqbal ◽  
Akira Kanazawa ◽  
Silvia Romana Ottaviani ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge
Keyword(s):  
Motion Planning ◽  
Cost Function ◽  
Real Time ◽  
Collaborative Robots ◽  
Time Motion ◽  
Planning Scheme

Real-time motion tracking in image-guided oral implantology

2008 ◽  
Vol 4 (4) ◽  
pp. 339-347 ◽  
Author(s):  
Xiaojun Chen ◽  
Yanping Lin ◽  
Yiqun Wu ◽  
Chengtao Wang
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Image Guided ◽  
Time Motion ◽  
Oral Implantology

Detection of individual atoms in helium buffer gas and observation of their real-time motion

1980 ◽  
Vol 5 (11) ◽  
pp. 459 ◽  
Author(s):  
C. L. Pan ◽  
J. V. Prodan ◽  
W. M. Fairbank ◽  
C. Y. She
Keyword(s):  
Real Time ◽  
Buffer Gas ◽  
Time Motion

Automatic berthing for an underactuated unmanned surface vehicle: A real-time motion planning approach

2021 ◽  
Vol 235 ◽  
pp. 109352
Author(s):  
Sen Han ◽  
Yiting Wang ◽  
Lei Wang ◽  
Huacheng He
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Unmanned Surface Vehicle ◽  
Time Motion ◽  
Planning Approach
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