In order to help the patients with upper-limb disfunction go on rehabilitation training, this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom (DOF), and realized two control schemes, i.e., voice control and electromyography control. The hardware and software design of the voice control system was completed based on RSC-4128 chips, which realized the speech recognition technology of a specific person. Besides, this study adapted self-made surface eletromyogram (sEMG) signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing, extracting time domain features and fixed threshold algorithm. In addition, the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system. Voice control and electromyography control experiments were then carried out, and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1% and 90.9%, respectively. The results proved the feasibility of the control system. This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.
This article presents the design of a motion control system for seated lower-limb rehabilitation training. The system is composed of lower limb exoskeleton, motor drive circuit, program of motion control, and so forth. The power of lower limbs joints is provided by six motors. The PCI-1240 motion control card is used as the core. This study achieved repetitive rotation training and gait trajectory training of lower limbs joints, of which the velocity, angle and time can be accurately controlled and adjusted. The experimental results showed that the motion control system can meet the requirement of repetitive rehabilitation training for patients with lower limb dysfunction. This article provides a new method to the research of motion control system in rehabilitation training, which can promote industrial automation technique to be used for health care, and conducive to the further study of the rehabilitation robot.
Robot rehabilitation has been a primary therapy method for the urgent rehabilitation demands of paralyzed patients after a stroke. The parameters in rehabilitation training such as the range of the training, which should be adjustable according to each participant’s functional ability, are the key factors influencing the effectiveness of rehabilitation therapy. Therapists design rehabilitation projects based on the semiquantitative functional assessment scales and their experience. But these therapies based on therapists’ experience cannot be implemented in robot rehabilitation therapy. This paper modeled the global human-robot by Simulink in order to analyze the relationship between the parameters in robot rehabilitation therapy and the patients’ movement functional abilities. We compared the shoulder and elbow angles calculated by simulation with the angles recorded by motion capture system while the healthy subjects completed the simulated action. Results showed there was a remarkable correlation between the simulation data and the experiment data, which verified the validity of the human-robot global Simulink model. Besides, the relationship between the circle radius in the drawing tasks in robot rehabilitation training and the active movement degrees of shoulder as well as elbow was also matched by a linear, which also had a remarkable fitting coefficient. The matched linear can be a quantitative reference for the robot rehabilitation training parameters.
Using intelligent rehabilitation robot to intervene hand function after stroke is an important physical treatment. With the development of biomedical engineering and the improvement of clinical demand, the comprehensive intervention of hand-function rehabilitation robot combined with new technologies is gradually emerging. This article summarizes the hand rehabilitation robots based on electromyogram (EMG), the brain-computer interface (BCI) hand rehabilitation robots, the somatosensory hand rehabilitation robots and the hand rehabilitation robots with functional electrostimulation. The advantages and disadvantages of various intervention methods are discussed, and the research trend about comprehensive intervention of hand rehabilitation robot is analyzed.
ObjectiveTo explore the rehabilitation effect of a domestic lower limb rehabilitation robot on patients with chronic stroke.MethodsChronic stroke patients who were hospitalized in the Department of Rehabilitation Medicine, the First Affiliated Hospital of Chongqing Medical University from September 2017 to August 2019 were collected. These patients underwent A3 robot-assisted gait training for 6 weeks. The differences of gait parameters, spatiotemporal asymmetries, total score and score of each item of Barthel Index were analyzed before and after 6 weeks training.ResultsA total of 15 patients were included, and 12 patients finally completed the trial. After the training, the gait parameters of patients with chronic stroke were significantly improved. Comparing with the baseline data, the cadence, stride length, velocity, step length of the affected leg, and step length of the healthy leg significantly increased (P<0.05) after the training; the stride time and the double-support time were significantly shorter (P<0.05); the stance phase of the affected leg was shortened (P<0.05); the swing phase of the affected leg was prolonged (P<0.05); While no significant difference in the stance phase or swing phase of the healthy leg was found (P>0.05). The spatiotemporal asymmetries had no significant change compared with the baseline data, including the ratio of step length [(1.26±0.23) vs. (1.13±0.10); t=1.816, P=0.097] and the ratio of swing phase of both lower limbs [1.14 (0.23) vs. 1.10 (0.38); Z=−0.153, P=0.878]. The activities of daily living were improved after the training, and the total score of Barthel Index [(72.92± 13.05) vs. (85.42±14.38); t=−6.966, P<0.001] was significantly higher than that before the training. Among the items, the scores of bathing [0.00 (3.75) vs. 5.00 (5.00); Z=−2.000, P=0.046], walking on the flat ground [10.00 (3.75) vs. 15.00 (5.00); Z=−3.000, P=0.003], and going up and down stairs [5.00 (5.00) vs. 7.50 (5.00), Z=−3.000, P=0.003] were higher than the baseline data, and the differences were statistically significant.ConclusionsA3 robot-assisted gait training can effectively improve the walking ability and activities of daily living of patients with chronic stroke but not the spatiotemporal asymmetries. Whether the spatiotemporal asymmetries can be improved by adjusting the robot equipment parameters needs to be further studied.
Rapid development is undergoing in the field of rehabilitation robots, and more countries (regions) are participating in international cooperation and becoming academic contributors. Here in this study, the bibliometric method is used to determine the dynamics and developments of international cooperation in China. The publication data are indexed in Web of Science with search term of rehabilitation robot from 2000 to 2019. Compared with other countries (regions), publication with international co-authors and institutes participating in international cooperation are studied by assessment of indicators such as the cooperation degree, cooperation frequency, and the frequency of citations. The results show that in the past two decades, international scientific cooperation has shown a positive tendency in China, and participating in international collaboration could improve China’s impact on the global rehabilitation robot. The United States, England and Japan are the top three countries in number of cooperation with China. Our findings provide valuable information for researchers to better understand China’s international scientific collaboration in rehabilitation robot.
The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.
ObjectiveTo observe the effects of upper limb rehabilitation robot-assisted training combined with mirror therapy on unilateral spatial neglect (USN) in stroke patients.MethodsA total of 40 patients with USN admitted to the Department of Rehabilitation Medicine of the Second Affiliated Hospital of Nantong University from January 2017 to December 2018 were selected and randomly divided into the trial group and the control group, with 20 cases in each group. The trial group used upper limb rehabilitation robot-assisted training combined with mirror therapy and USN comprehensive rehabilitation treatment. The control group patients only received USN comprehensive rehabilitation treatment. All patients continued treatment for 4 weeks. Before treatment and after 4 weeks of treatment, the modified Barthel index (MBI) was used to assess the activities of daily living, the Fugl-Meyer assessment (FMA) was used to assess motor function, and the Catherine-Bergego scale was used to assess the degree of USN.ResultsThere was no statistically significant difference in general information between the two groups of patients (P>0.05). There was no significant difference in MBI, FMA or USN degree scores between the two groups before treatment (P>0.05). After 4 weeks of treatment, the MBI, FMA and USN degree scores of the two groups were improved compared with those before treatment (P<0.05). The improvements in MBI, FMA and USN degree scores of the trial group were 14.75±1.97, 17.05±3.93 and 5.25±2.29, respectively, and those of the control group were 9.75±4.44, 8.30±2.06 and 3.10±0.72, respectively, and the differences were statistically significant (P<0.05).ConclusionsUpper limb rehabilitation robot-assisted training combined with mirror therapy can effectively improve the spatial neglect of USN patients, and improve the ability of daily living and motor functions.
Objective To investigate the effect of lower limb rehabilitation robot combined with virtual reality training on walking ability after anterior cruciate ligament reconstruction (ACLR). Methods Patients after ACLR treated in the Rehabilitation Medical Center of the Second Hospital of Jiaxing between May 2019 and July 2021 were selected. The patients were randomly divided into two groups. The patients with conventional ACLR rehabilitation training + lower limb rehabilitation robot combined with virtual reality training were used as the treatment group, and only the patients with conventional ACLR rehabilitation training were used as the control group. The rehabilitation training lasted for 8 weeks. After 4 and 8 weeks of treatment, the two groups were evaluated by Lysholm knee score scale (LKSS), Holden walking score and Gait watch gait analysis. Results A total of 40 patients were included, with 20 patients in each group. There was no significant difference between the two groups in LKSS score, Holden walking score and Gait watch gait analysis dynamic data at 4 weeks of treatment (P>0.05). After 8 weeks of treatment, the LKSS score [(77.74±5.53) vs. (69.53±5.26) points], Holden walking score [(4.79±0.34) vs. (4.45±0.39) points] and Gait watch gait analysis dynamic data [step size: (78.35±2.43) vs. (73.64±3.35) cm, step frequency: (115.10±4.49) vs. (107.71±5.14) step/min, step speed: (108.63±8.55) vs. (96.78±8.47) cm/s] of the treatment group were better than those of the control group (P<0.05), The above indexes of the two groups were improved compared with those at 4 weeks of treatment (P<0.05). Conclusion Lower limb rehabilitation robot combined with virtual reality training can effectively improve walking ability after ACLR.
Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.