ABSTRACT
ObjectiveTo explore the effects of different intensity of wearable lower limb rehabilitation robot-assisted training on walking function, lower limb motor function, balance function and functional independence of stroke patients. MethodsFrom November, 2021 to December, 2022, 60 stroke patients hospitalized in Beijing Bo'ai Hospital were randomly divided into control group (n = 20), observation group 1 (n = 20) and observation group 2 (n = 20). All the groups received routine rehabilitation, while the control group received routine walking training 30 minutes a day, the observation group 1 received wearable lower limb rehabilitation robot-assisted training 30 minutes a day, and the observation group 2 received wearable lower limb rehabilitation robot-assisted training 60 minutes a day, for four weeks. They were assessed with Functional Ambulation Category scale (FAC), Fugl-Meyer Assessment-Lower Extremities (FMA-LE), Berg Balance Scale (BBS) and Rivermead Mobility Index (RMI) before and after treatment. ResultsOne case in the observation group 1 and three cases in the observation group 2 dropped down. The FAC, FMA-LE, BBS and RMI scores improved in all the three groups after treatment (|Z| > 3.448, |t| > 8.102, P < 0.001), and there was no significant difference in all the indexes among the three groups (|H| < 4.643, F = 1.454, P > 0.05); however, the improvement of BBS score was more in the observation group 1 than in the control group (P < 0.05), and the improvement of all the indexes was more in the observation group 2 than in the control group (P < 0.05). ConclusionThe wearable lower limb rehabilitation robot-assisted training may promote the recovery of walking function, lower limb motor function, balance function and functional independence of stroke patients, and high-intensity training seems to be more effective.
ABSTRACT
Spatiotemporal gait parameters provide important information for the rehabilitation of patients with gait dysfunction. These parameters are often obtained by complex systems such as optical motioncapture system and pressure plates. However, these systems cannot be deployed at the lower-limb rehabilitation robot easily because of high costs, large area occupation and wearable requirements. We present a gait measurement system with a Light Detection And Ranging(LIDAR) laser sensor based on the lower-limb rehabilitation robot. Firstly, to calculate gait parameters, the data are aggregated into left and right legs by the clustering algorithm and the legs contour is fitted with two circles respectively according to the least square method. Then, the spatiotemporal gait parameters are defined based on the time and position of initial contact(IC) and toe off(TO). Finally, to verify the validity of the proposed system, we compared the results of the proposed system with a 3D motion capture system based on a lower-limb rehabilitation robot. Experimental results showed that the gait detection system can measure the parameters within a small range of error that testified the validation of the proposed system. This system proved to be a valid and reliable method for the measurement of gait parameters.
Subject(s)
Humans , Biomechanical Phenomena , Gait , Lasers , Lower Extremity , Motion , RoboticsABSTRACT
Objective:To develop an automatic adjustment algorithm of bed height of multi-position lower limb rehabilitation robot, to meet the variety of leg lengths and training modes to avoid the collision between robot and ground. Methods:Six mathematical models of robot bed body height were established for six training modes of multi-position lower limb rehabilitation robot, which were described with leg length and bed tilt angle. The influence was analyzed that mechanical clearance and deflection as well as the jitter error of leg bracket during movement. Furthermore, a software related to these models was developed to automatically adjust the bed height for training. Volunteers were recruited to test actually. Results:The test data of bed height are consistent with the theoretical calculation of six mathematical models. Clearance and deflection did not affect the theoretical results of bed height. The end of robot's lower limb was always above the safe height during rehabilitation training. Conclusion:The automatic adjustment algorithm of bed height has been established, which can ensure that the rehabilitation robot runs at a safe height.
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Objective:To solve the problem of leg shaking caused by the sudden change of angular acceleration at the joint of periodic motion during circular gait training of lower limb rehabilitation robot. Methods:A kind of quasi-circular gait was proposed, which divided the periodic motion into three phases: start phase, middle phase and end phase. The time was equal in the start phase and the end phase, and could be adjusted with the parameter ratio. The joint trajectories of the two phases were planned by quintic polynomial, and the middle phase was still the circular gait joint trajectory. The trajectory of the proposed quasi-circular gait was simulated. Results:The angular velocity was continuously differentiable and 0, the angular acceleration was continuous and 0, and the end-effector trajectory became flat with the increase of ratio. The results on the physical prototype of the lower limb rehabilitation robot were principally consistent with the simulation, and the machine worked smoothly. Conclusion:The proposed variable quasi-circular gait had smooth angular acceleration at the junction of periodic motions, which effectively solved the jitter problem when using circular gait for lower limb rehabilitation training and avoided secondary injury to the patients.
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Objective:To solve the movement mode adapting to individual differences for the trajectory planning of lower limb rehabilitation robots. Methods:After summarizing the six movement modes of the lower limb rehabilitation robot, according to the multi-rigid body theory of the human body, the exoskeleton of the lower limb rehabilitation robot was simplified into a two-bar linkage mechanism, the inverse kinematics analysis of the motion mode was performed, and the motion pattern solving system was designed based on C#. Results:The motion mode joint angle value calculated based on the C# motion mode solving system was transmitted to the upper computer, and the six motion modes were successfully applied to the lower limb rehabilitation robot. Through the inversion kinematics analysis of the motor model, the C#-designed motion mode solving system could solve the motorized joint angle values that adapted to individual of different leg lengths with lower extremity motor dysfunction. Through physical prototype experiments, the lower limb rehabilitation robot could drive the human body model for rehabilitation training according to the planned exercise mode. The actual joint angle curve and the theoretical joint angle curve were basically coincident, the joint angle error was small. Conclusion:The individual difference motion pattern solution is valid and feasible.
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According to characteristics of gait-assisted training, exoskeleton-based lower limb rehabilitation robot can be classified into treadmill-based exoskeleton robot and over-ground exoskeleton robot. Both kinds of exoskeleton-based lower limb rehabilitation robot have been applied in stroke rehabilitation, both in subacute and chronic stages, that may do something in gait training, balance improvement and lower limb motor function recovery.
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Objective To observe the effects of a lower-limb rehabilitation robot plus psychological intervention on depression and lower limb motor function of the stroke patients.Methods Sixty stroke patients were randomly divided into a treatment group (n =30) and a control group (n =30).The patients of control group were treated with routine rehabilitation and psychological intervention,while the treatment group were treated with lowerlimb rehabilitation robot,in addition to the interventions of the control group,for 10-20 minutes daily,6 days per week for 8 weeks.Both groups were assessed with Hamilton Depression Scale (HAMD),Simplified Fugl-Meyer assessment (FMA) and Functional Ambulation Category (FAC) before and at the end of 2,4 and 8 weeks of treatment.Results There was no significant difference between the 2 groups in all the assessment parameters before and after 2 weeks of treatment.At the end of 4 weeks of treatment,both groups improved significantly with regard to the scores of HAMD,FMA and FAC classification,and the treatment group improved to a significantly greater extent than the control group (P ＜ 0.05).At the end of 8 weeks of treatment the scores of HAMD,FMA and FAC classification were further improved significantly in treatment groups when compared with values obtained at the end of 4 weeks after treatment,but the scores of the control group was not significantly improved at the end of 8 weeks when compared with those at the end of 4 weeks of treatment.Conclusion Lower-limb rehabilitation robot plus psychological intervention can significantly help relieve the post-stroke depression and improve lower motor function of stroke patients.