Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Liu, Yang; Li, Xiaoling; Zhu, Aibin; Zheng, Ziming; Zhu, Huijin

doi:10.1177/17298814211003461

Cited by 19 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the biomechanical characteristics of human shoulder joint, shoulder joint is composed of clavicle, humerus, and scapula, which is a typical ball and socket joint and can carry out multiaxial flexible movement. Shoulder joint motion can be specifically decomposed into three orthogonal motions: abduction or adduction in the coronal plane, flexion or extension in the sagittal plane, and internal rotation or external rotation in the horizontal plane [11].…”

Section: Structural Design Of the Shoulder Rehabilitation Robotmentioning

confidence: 99%

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

et al. 2022

View full text Add to dashboard Cite

In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was 0.21 ± 1.35 ° . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.

show abstract

Section: Structural Design Of the Shoulder Rehabilitation Robotmentioning

confidence: 99%

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Fourth, the process of rehabilitation is based on qualitative observation, lacking objective monitoring basis, which is not conducive to the further determination of treatment plan. Fifth, there is no objective rehabilitation evaluation index, so it is impossible to conduct in-depth study on the neurorehabilitation law of patients [1]. Based on the above points, it is extremely urgent to find a convenient and effective means of rehabilitation training to enable patients to recover their active sports ability, improve their quality of life, and reduce the burden on families and society.…”

Section: Introductionmentioning

confidence: 99%

Motion Rehabilitation Robot Control Based on Human Posture Information

He¹

2022

Journal of Sensors

View full text Add to dashboard Cite

In order to meet the needs of postoperative rehabilitation training of lower limbs, a motion rehabilitation robot control system based on human posture information is proposed in this paper. The functions of active/passive training mode control, movement posture and EMG signal acquisition, WiFi communication, safety protection, etc. of the lower limb rehabilitation robot are realized. The recognition and analysis of the training process are realized by using random forest machine learning algorithm and linear regression algorithm. The experimental results show that in the first row of the confusion matrix of the random forest algorithm, 7316 data are correctly identified as speed a and only one data is incorrectly identified as speed B , which is superior to other algorithms. In conclusion, the developed control and monitoring system of lower limb rehabilitation robot can be portable controlled by Android and can realize intelligent analysis of the training process through the monitoring signals in the training process. At the same time, the random forest algorithm has more advantages than the linear regression algorithm in motion recognition, which is of positive significance to the automatic monitoring and intelligent control of the training process.

show abstract

“…One study [ 43 ] reduced the mass of an exoskeleton to only 1.3 kg by using a flexible neoprene material, which significantly improved portability and wearing comfort. Liu et al [ 24 ] contributed to the lightweight development of a rehabilitation exoskeleton based on 3D printing technology and virtual simulation. The printing material was polylactic acid with a 50% print fill.…”

Section: Resultsmentioning

confidence: 99%

Human Factor Engineering Research for Rehabilitation Robots: A Systematic Review

Song

Liu

Gao

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The application of human factors engineering for rehabilitation robots is based on a “human-centered” design philosophy that strives to provide safe and efficient human-robot interaction training for patients rather than depending on rehabilitation therapists. Human factors engineering for rehabilitation robots is undergoing preliminary investigation. However, the depth and breadth of current research do not provide a complete human factor engineering solution for developing rehabilitation robots. This study aims to provide a systematic review of research at the intersection of rehabilitation robotics and ergonomics to understand the progress and state-of-the-art research on critical human factors, issues, and corresponding solutions for rehabilitation robots. A total of 496 relevant studies were obtained from six scientific database searches, reference searches, and citation-tracking strategies. After applying the selection criteria and reading the full text of each study, 21 studies were selected for review and classified into four categories based on their human factor objectives: implementation of high safety, implementation of lightweight and high comfort, implementation of high human-robot interaction, and performance evaluation index and system studies. Based on the results of the studies, recommendations for future research are presented and discussed.

show abstract

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Cited by 19 publications

References 38 publications

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

Motion Rehabilitation Robot Control Based on Human Posture Information

Human Factor Engineering Research for Rehabilitation Robots: A Systematic Review

Contact Info

Product

Resources

About