Compound lower limb vibration training rehabilitation robot

Yin, Qiang; Hu, Ao; Li, Qian; Wei, Xin; Yang, Hongjun; Wang, Beihai; Zhang, Guoquan

doi:10.1002/cpe.6059

Cited by 9 publications

(4 citation statements)

References 50 publications

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“…If the treatment method is not selected properly, it will seriously reduce the patient's quality of life [13][14]. At present, in rehabilitation medicine, functional training of joint dysfunction and spasticity requires artificial treatment of ankle joint training [15][16].They are based on the theory of continuous passive motion, stimulate the human body's natural resilience, play a role in tissue compensation, and restore the original functions of joints to the greatest extent [17]. An-kle rehabilitation aids have the advantages of labor-saving, high accuracy, and fast recovery.…”

Section: Related Workmentioning

confidence: 99%

Research on 4-UPUS Parallel Mechanism Ankle Joint Rehabilitation Training Robot

Hu¹,

Yin²,

He³

et al. 2022

Preprint

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In-depth research on the rehabilitation mechanism and motion characteristics of a rehabilitation training robot based on 4-UPUS parallel mechanism. The overall design and the working principle of each key component are explained, and the robot rehabilitation scheme is described. The static analysis and modal analysis of the robot bearing structure are carried out to verify whether the static mechanical characteristics of the mechanism meet the application requirements under working conditions.A simplified mathematical model of the six-bar linkage mechanism is established, and the kinematics of the mechanism is solved by the geometric analysis method. The motion characteristic diagram of each key point is obtained through simulation calculation. Establish the motion model of the parallel mechanism, and discuss the working space and motion performance analysis of the mechanism. The forward solution analysis of the mechanism position is carried out by using the numerical analysis method, and the three-dimensional graphics of the attitude angle and linear displacement of the reachable working space are obtained.Taking the UPUS single branch chain as the analysis object, the single open chain analysis method is used to solve the kinematics image of the corresponding surface in the working space of the mechanism, which verifies the correctness of the kinematics theoretical solution and the feasibility of simulation.The research results show that the 4-UPUS parallel mechanism rehabilitation training robot can cooperate with the ankle joint for rehabilitation training. It makes up for the single movement of the current lower limb rehabilitation robot and the unsatisfactory rehabilitation effect, and provides a reference for the practical application of the subsequent ankle joint rehabilitation robot system.

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Section: Related Workmentioning

confidence: 99%

Research on 4-UPUS Parallel Mechanism Ankle Joint Rehabilitation Training Robot

Hu¹,

Yin²,

He³

et al. 2022

Preprint

Self Cite

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show abstract

“…Meanwhile, lower‐limb rehabilitation robotic has been proposed as an effective tool to restore the economy of bipedal gait. [ 27 , 28 ] For ambulation‐impaired patients, the sensitive and friendly HMI to achieve the dexterous and accurate manipulation of the lower‐limb rehabilitation robot is of vital importance. [ 22 , 29 ] Note that the waist always cooperates with the near lower limbs to maintain the walking balance, and provides valuable information of the motion intention.…”

Section: Introductionmentioning

confidence: 99%

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT‐Based Smart Healthcare Applications

et al. 2021

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Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost‐effective human‐machine interface to enhance the immersion during the long‐term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG‐based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower‐limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real‐time robotic manipulation and virtual game for immersion‐enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower‐limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming‐aided training, showing its potential in IoT‐based smart healthcare applications.

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“…The effect of fuzzy control can well suppress the chattering effect produced by the sliding mode variable structure control. In this way, the rehabilitation robot can conduct rehabilitation treatment for the human body more efficiently and safely [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Mechatronics in Multifunctional Rehabilitation Robot

2023

IJOMAM

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With the progress of science and technology, multifunctional rehabilitation robots have come into people's lives, so improving the robot's tactile perception has become crucial. This work first describes mechatronics and explains relevant technologies and their current applications in multifunctional rehabilitation robots. Then, a method of combining the neural network and a multifunctional rehabilitation robot's tactile sensor is proposed, which optimizes the sensor through neural network technology, thus improving tactile perception. Finally, Long Short-Term Memory (LSTM) is employed to further optimize the model and improve its stability. Comparative experiments are conducted to verify the model's effectiveness. The experimental results show that 25 iterations are the boundary value of the optimization model, regardless of training or testing. The learning speed of the first 25 iterations is relatively fast, and the loss of iteration starts to decline after the 25 iterations. The decline speed is relatively slow, but the accuracy has become higher. With the increase of iteration times, although the loss is still decreasing, the highest accuracy rate during the test has reached 97% and it tends towards stability. It proves the effectiveness and rationality of the optimized model. The soft and hard recognition of objects is distinguished through the second experiment. It suggests that when recognizing soft and hard objects, the average recognition accuracy of the model after optimization can reach 0.965, while the average recognition accuracy of the traditional model is only 0.915, which further verifies the feasibility of the optimized model. Therefore, this work has certain reference significance for optimizing multifunctional rehabilitation robots.

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Compound lower limb vibration training rehabilitation robot

Cited by 9 publications

References 50 publications

Research on 4-UPUS Parallel Mechanism Ankle Joint Rehabilitation Training Robot

Research on 4-UPUS Parallel Mechanism Ankle Joint Rehabilitation Training Robot

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT‐Based Smart Healthcare Applications

Application of Mechatronics in Multifunctional Rehabilitation Robot

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