Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems.

The finite element analysis method, combined with impact analysis, was used to study the dynamic behaviour of grape berries in the horizontal and vertical directions under excitation. It was shown that grapes exhibit various behaviour forms, such as the single-berry bouncing, squeezing two-berries jumping, squeezing three-berries shaking, squeezing four-berries swing and micro-swinging of grape in the first 30 modals. The grapes oscillated the most at approximately 14.45 Hz and 15.35 Hz. The fruit-stem system was extremely active under vertical impact, often showing jumping and rebounding phenomena, and occurring a certain degree of bending and twisting as well, while the berry under horizontal impact only showed the characteristics of shaking. Therefore, vertical impact is more likely to cause violent movements of berries than horizontal impact. Thus, the finite element analysis method can accurately simulate the dynamic behaviour of berries under extrusion, and can provide a theoretical basis for analysing postharvest damage of grapes.

Functional electrical stimulation (FES) and powered exoskeletons are widely applied methods for lower limb rehabilitation. These approaches assist human movement by utilizing the body's own muscles along with motors in the exoskeleton. However, the complexity and uncertainty of system modeling hinder the widespread adoption of model-based control schemes. This paper proposes a hybrid control scheme to track the dynamic knee joint angle and provides a Lyapunov stability proof. The scheme initially optimizes the control input using model predictive control (MPC), supplemented by sliding mode control (SMC) to provide compensatory torque against system disturbances. Simulation results show that, compared to PID control, the new method achieved a 55.1% reduction in the sum of squared torques (SST) at the cost of an increase of 0.55 degrees in RMSE. Compared to NMPC control, the new method reduced RMSE by 41.2% while increasing SST by 2.8%. The fusion control method retains the advantages of the original NMPC while achieving a significant reduction in tracking error with a relatively small increase in motor torque, demonstrating higher robustness.

During mechanized table grape harvesting, berries are subjected to vibration and collision, which can cause shedding and damage to the fruit. Research on table grape berry shedding has primarily focused on macroscopic swing modes, which are reflected in the integrated grape cluster structure and idealized particle interactions, as well as static response treatments. However, these approaches are unable to accurately explain the characteristics of berry wobbling during picking, predict shedding-prone areas, or identify factors affecting shedding. In this paper, we study the dynamic response characteristics of grape berries in the X, Y, and Z directions by establishing a dynamic model and combining harmonic response and random vibration characteristics with finite element analysis. Our studies revealed that grape berries exhibit various forms (swinging and rebounding) under the same stimulus during harvesting. The grape berry amplitude in the X, Y, and Z directions were 14.71, 12.46, and 27.10 mm, respectively, with the most obvious response being in the Z direction and the flattest response in the Y direction. Berries in the lower cob system part were relatively stable, while those in the upper right side were more prone to swinging and falling, with areas most likely to fall off concentrated in the upper right side. This system accurately predicted the dynamic response characteristics of fruit during vibration harvesting and provided an ideal basis for mechanized grape harvesting. Optimization and research on fruit collection equipment may benefit from this theoretical basis.

The problem of breakage and shedding in the mechanical harvesting of edible grapes has not been fully studied. Particularly, the responsiveness and shedding of berries caused by excited vibrations when the stem is sheared. This work combines modal analysis and swept frequency analysis to study the oscillation response and critical shedding charac-teristics of excited grape systems. First, a dynamic grape main stem-split stem-berry plane double pendulum model is established based on shedding theory, and the theoretical angular velocity of berry swing is deduced. Then, the berry separation and shedding pro-cesses are accurately simulated by a simplified multi-berry model that eliminates inter-ference and penetration effects. The modal analysis shows that the berries exhibited multidirectional and multistate disordered swings and rotations at different frequencies. Finally, the critical shedding parameters, including the acceleration, velocity, displace-ment, stress and shedding frequency of the dynamic responses of the berries in the sim-ulations and picking experiments are compared. The results show that the dynamic response trends of the simulation and picking experiments are consistent, and the berries fall off when the amplitude is 49.88 mm and the frequency is approximately 4 Hz. Therefore, this study provides theoretical parameters for optimising the picking mechanism.(c) 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.

The measurement of grapevine phenotypic parameters is crucial to quantify crop traits. However, individual differences in grape bunches pose challenges in accurately measuring their characteristic parameters. Hence, this study explores a method for estimating grape feature parameters based on point cloud information: segment the grape point cloud by filtering and region growing algorithm, and register the complete grape point cloud model by the improved iterative closest point algorithm. After estimating model phenotypic size characteristics, the grape bunch surface was reconstructed using the Poisson algorithm. Through the comparative analysis with the existing four methods (geometric model, 3D convex hull, 3D alpha-shape, and voxel-based), the estimation results of the algorithm proposed in this study are the closest to the measured parameters. Experimental data show that the coefficient of determination (R2) of the Poisson reconstruction algorithm is 0.9915, which is 0.2306 higher than the coefficient estimated by the existing alpha-shape algorithm (R2 = 0.7609). Therefore, the method proposed in this study provides a strong basis for the quantification of grape traits.

针对夹剪式鲜食葡萄采摘中的断梗振动激励引起的果粒脱落问题,对断梗激励下鲜食葡萄的振动脱落特性与动态响应进行研究.首先建立葡萄果实-分梗动力学模型,推导果实脱落的理论角速度,分析果实-果梗摆动脱落的临界分离条件.然后利用ABAQUS软件分析单颗粒葡萄在断梗激振下的动态响应与摆动趋势,探索在无挤压状态下果实形变过程,从而预测串型葡萄在断梗激励下的实际振动响应.最后对串型葡萄的简化模型进行振动有限元分析,获得葡萄果实在脱落前瞬间相对于果梗结合处的位移、速度、加速度和应力应变等数据,从而确定葡萄的临界振动脱落参数组合.通过仿真试验和采摘振动试验验证模型的准确性.结果表明:在断梗激励下,葡萄果实出现不确定的各向异性扭转摆动;对整串葡萄进行0～25 Hz的扫频分析可知受振果实的临界脱落频率约为4 Hz;受振果实摆动幅度为49.88 mm,速度峰值0.92 mm/s,加速度峰值39.08 mm/s2时开始脱落;同一激励下,虽然各个果实位置不同,但它们振动特性变化趋势相同.该研究可为防脱落采摘机构参数设计提供理论依据.

Counting grape berries and measuring their size can provide accurate data for robot picking behavior decision-making, yield estimation, and quality evaluation. When grapes are picked, there is a strong uncertainty in the external environment and the shape of the grapes. Counting grape berries and measuring berry size are challenging tasks. Computer vision has made a huge breakthrough in this field. Although the detection method of grape berries based on 3D point cloud information relies on scanning equipment to estimate the number and yield of grape berries, the detection method is difficult to generalize. Grape berry detection based on 2D images is an effective method to solve this problem. However, it is difficult for traditional algorithms to accurately measure the berry size and other parameters, and there is still the problem of the low robustness of berry counting. In response to the above problems, we propose a grape berry detection method based on edge image processing and geometric morphology. The edge contour search and the corner detection algorithm are introduced to detect the concave point position of the berry edge contour extracted by the Canny algorithm to obtain the best contour segment. To correctly obtain the edge contour information of each berry and reduce the error grouping of contour segments, this paper proposes an algorithm for combining contour segments based on clustering search strategy and rotation direction determination, which realizes the correct reorganization of the segmented contour segments, to achieve an accurate calculation of the number of berries and an accurate measurement of their size. The experimental results prove that our proposed method has an average accuracy of 87.76% for the detection of the concave points of the edge contours of different types of grapes, which can achieve a good edge contour segmentation. The average accuracy of the detection of the number of grapes berries in this paper is 91.42%, which is 4.75% higher than that of the Hough transform. The average error between the measured berry size and the actual berry size is 2.30 mm, and the maximum error is 5.62 mm, which is within a reasonable range. The results prove that the method proposed in this paper is robust enough to detect different types of grape berries.