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.
At present, upper limb motor rehabilitation relies on specific rehabilitation aids, ignoring the initiative of upper limb motor of patients in the middle and late stages of rehabilitation. This paper proposes a fuzzy evaluation method for active participation based on trajectory error and surface electromyography (sEMG) for patients who gradually have the ability to generate active force. First, the level of motor participation was evaluated using trajectory error signals represented by computer vision. Then, the level of physiological participation was quantified based on muscle activation (MA) characterized by sEMG. Finally, the motor performance and physiological response parameters were used as inputs to the fuzzy inference system (FIS) to construct the fuzzy decision tree (FDT) output active participation level. A controlled experiment of upper limb flexion and extension exercise in 16 healthy subjects demonstrated that the method presented in this paper was effective in quantifying difference in the active participation level of the upper limb in different force-generating states. The calculation results of this method and the active participation assessment method based on sEMG during the task cycle showed that the active participation evaluation values of both methods peaked in the initial cycle: (82.34 ± 9.3) % for this paper’s method and (78.44 ± 7.31) % for the sEMG method. In the subsequent cycles, the values of both showed a dynamic change trend of rising first and then falling. Trend consistency verifies the effectiveness of the active participation assessment strategy in this paper, providing a new idea for quantifying the participation level of patients in middle and late stages of upper limb rehabilitation without special equipment mediation.
To solve the safety problems caused by the restriction of interaction space and the singular configuration of rehabilitation robot in terminal traction upper limb rehabilitation training, a trajectory planning and tracking control scheme for rehabilitation training is proposed. The human-robot safe interaction space was obtained based on kinematics modeling and rehabilitation theory, and the training trajectory was planned based on the occupational therapy in rehabilitation medicine. The singular configuration of the rehabilitation robot in the interaction space was avoided by exponential adaptive damped least square method. Then, a nonlinear controller for the upper limb rehabilitation robot was designed based on the backstepping control method. Radial basis function neural network was used to approximate the robot model information online to achieve model-free control. The stability of the controller was proved by Lyapunov stability theory. Experimental results demonstrate the effectiveness and superiority of the proposed singular avoidance control scheme.