A novel upper limb training system based on UR5 using sEMG and IMU sensors
This paper intends to design a system which acquires the trainer's motion and force information in order to manipulate a robot arm applied for rehabilitations. Patients who suffering physical disability also can receive the professorial guiding and cheirapsis even excellent trainers are very bu...
| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2016
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| _version_ | 1796862106369261568 |
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| author | Liu, Z. Chang, W. Sheng, S. Li, L. Soo, Y. G. Yeong, C. F. Odagaki, M. Duan, F. |
| author_facet | Liu, Z. Chang, W. Sheng, S. Li, L. Soo, Y. G. Yeong, C. F. Odagaki, M. Duan, F. |
| author_sort | Liu, Z. |
| collection | ePrints |
| description | This paper intends to design a system which acquires the trainer's motion and force information in order to manipulate a robot arm applied for rehabilitations. Patients who suffering physical disability also can receive the professorial guiding and cheirapsis even excellent trainers are very busy and insufficient. The key point of this article is data acquisition and reconstruction of the movement of the upper limb by controlling the UR5 robot arm. Upper limb's postures are sensed by Inertial Measurement Unit (IMU) and transferred to STM32 microcontroller using I2C communication protocol. We employed the STM32 microcontroller to calculate attitude angles of both the upper arm and the forearm. And the method with using quaternions to calculate attitude angles is detailedly expounded in this paper. Besides, we employed the MYO armband to acquire upper limb's surface electromyography (sEMG) signals for estimating the muscle force of the upper limb. To verify the feasibility of the proposed system, we make three experiments including analyzing fluctuation range of the attitude angles from IMU signals, classifying muscle force using sEMG signals, and evaluating the effect of motion reconstruction. And the results show that the fluctuation range of acquired data are less than 1 degree, 4 typical motions of upper limb can be reconstructed. The proposed system can be used to reconstruct some upper limb's movement. |
| first_indexed | 2024-03-05T20:06:32Z |
| format | Conference or Workshop Item |
| id | utm.eprints-73629 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:06:32Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-736292017-11-28T05:01:13Z http://eprints.utm.my/73629/ A novel upper limb training system based on UR5 using sEMG and IMU sensors Liu, Z. Chang, W. Sheng, S. Li, L. Soo, Y. G. Yeong, C. F. Odagaki, M. Duan, F. TK Electrical engineering. Electronics Nuclear engineering This paper intends to design a system which acquires the trainer's motion and force information in order to manipulate a robot arm applied for rehabilitations. Patients who suffering physical disability also can receive the professorial guiding and cheirapsis even excellent trainers are very busy and insufficient. The key point of this article is data acquisition and reconstruction of the movement of the upper limb by controlling the UR5 robot arm. Upper limb's postures are sensed by Inertial Measurement Unit (IMU) and transferred to STM32 microcontroller using I2C communication protocol. We employed the STM32 microcontroller to calculate attitude angles of both the upper arm and the forearm. And the method with using quaternions to calculate attitude angles is detailedly expounded in this paper. Besides, we employed the MYO armband to acquire upper limb's surface electromyography (sEMG) signals for estimating the muscle force of the upper limb. To verify the feasibility of the proposed system, we make three experiments including analyzing fluctuation range of the attitude angles from IMU signals, classifying muscle force using sEMG signals, and evaluating the effect of motion reconstruction. And the results show that the fluctuation range of acquired data are less than 1 degree, 4 typical motions of upper limb can be reconstructed. The proposed system can be used to reconstruct some upper limb's movement. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item PeerReviewed Liu, Z. and Chang, W. and Sheng, S. and Li, L. and Soo, Y. G. and Yeong, C. F. and Odagaki, M. and Duan, F. (2016) A novel upper limb training system based on UR5 using sEMG and IMU sensors. In: 2016 IEEE International Conference on Robotics and Biomimetics, ROBIO 2016, 3-7 Dec 2016, Qingdao, China. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016812894&doi=10.1109%2fROBIO.2016.7866467&partnerID=40&md5=8c7e745f3340dabacf1c69dd1e65814f |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Liu, Z. Chang, W. Sheng, S. Li, L. Soo, Y. G. Yeong, C. F. Odagaki, M. Duan, F. A novel upper limb training system based on UR5 using sEMG and IMU sensors |
| title | A novel upper limb training system based on UR5 using sEMG and IMU sensors |
| title_full | A novel upper limb training system based on UR5 using sEMG and IMU sensors |
| title_fullStr | A novel upper limb training system based on UR5 using sEMG and IMU sensors |
| title_full_unstemmed | A novel upper limb training system based on UR5 using sEMG and IMU sensors |
| title_short | A novel upper limb training system based on UR5 using sEMG and IMU sensors |
| title_sort | novel upper limb training system based on ur5 using semg and imu sensors |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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