To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn’t work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.

Grasp using a prosthetic hand in real life can be a difficult task. The amputee users are often capable of planning the reaching trajectory and hand grasp location selection, however, failed in precise finger movements, such as adapting the fingers to the surface of the object without excessive force. It is much efficient to leave that part to the machine autonomy. In order to combine the intention and planning ability of users with robotic control, the shared control is introduced in which users’ inputs and robot control methods are combined to achieve a goal. The shared control problem can be formulated as a Partially Observable Markov Decision Process. To find the optimal control policy, we adopt an adaptive dynamic programming and reinforcement learning-based control algorithm-Deep Deterministic Policy Gradient combined with Hindsight Experience Replay. We proposed the algorithm with a prediction layer using the reparameterization technique. The system was tested in a modified simulation environment for the ability to follow the user’s intention and keep the contact force in boundary for safety.

Objective . Despite the encouraging pilot results of transcranial direct current stimulation (tDCS) revealing its effectiveness in neuromodulation, there are also studies reporting inconsistent outcomes. Apart from previously studied factors, such as the differences in head model structures, anodal displacements, electrode shape and size, and connector position, the hypothesis that the inevitable spatial mismatch between the electrolyte buffer and electrode might shape current flow in the cerebral cortex was tested in this work, and our results potentially explain some of the reported inconsistent outcomes. Approach . A finite element head model was built using cylinder electrodes with an arbitrary diameter of 2 cm. Current flow induced by different spatial mismatch types, degrees, and directions was simulated for three montages targeting the left motor cortex. Voxel-level current density differences and Jaccard index values of different percentiles for each mismatched configuration were calculated and compared throughout the cerebral cortex to determine the effect of electrode-electrolyte geometric mismatch. Main results . Spatial mismatch between the electrolyte buffer and electrode affected the current density distribution in the cerebral cortex to different extents, depending on the position of the return electrode and mismatch type, degree, and direction. Single cortical voxel current-density variance induced by the 50% excess or insufficient mismatch was as high as 14.44% or 38.04%, respectively. Moreover, the distribution of variance changed directionally with the mismatch orientation. Compared with the insufficient mismatch and single-directional mismatch, the excessive and symmetrical mismatch caused a less obvious effect on the current density distribution of tDCS. Specifically, the symmetrical excess electrolyte caused around 2%–4% current density changes for all the montages, with different degrees and directions of mismatch. When the target position was fixed at C3, maximum sensitivity to the electrolyte-electrode mismatch was achieved with Iz as the return electrode, compared with the other two choices. Further, more than 20% voxels with >90% percentile of the peak current density values would shift position if >30% insufficient geometric mismatch occurred for montage C3-Iz. Significance . Our findings suggest that special attention is required regarding the spatial matching of the electrolyte buffer to the electrode during tDCS to avoid unexpected large changes in current distribution.

Objective. The evaluation of animals’ motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation methods are unreliable, unpractical, and inaccurate. Approach. Data augmentation (random scaling, random standard deviation Gaussian blur, random contrast, and random uniform color quantization) is adopted to augment image dataset. For the key points recognition, we present a novel efficient convolutional deep learning framework (PMotion), which combines modified ConvNext using multi-kernel feature fusion and self-defined stacked Hourglass block with SiLU activation function. Main results. PMotion is useful to predict the key points of dynamics of unmarked animal body joints in real time with high spatial precision. Gait quantification (step length, step height, and joint angle) was performed for the study of lateral lower limb movements with rats on a treadmill. Significance. The performance accuracy of PMotion on rat joint dataset was improved by 1.98, 1.46, and 0.55 pixels compared with deepposekit, deeplabcut, and stacked hourglass, respectively. This approach also may be applied for neurobehavioral studies of freely moving animals’ behavior in challenging environments (e.g. Drosophila melanogaster and openfield-Pranav) with a high accuracy.