Ultrasound can non-invasively detect muscle deformations and has great potential applications in prosthetic hand control. Traditional ultrasound equipment was usually too bulky to be applied in wearable scenarios. This research presented a compact ultrasound device that could be integrated into a prosthetic hand socket. The miniaturized ultrasound system included four A-mode ultrasound transducers for sensing musculature deformations, a signal excitation/acquisition module, and a prosthetic hand control module. The size of the ultrasound system was 65*75*25mm, weighing only 85g. For the first time, we integrated the ultrasound system into a prosthetic hand socket to evaluate its performance in practical prosthetic hand control. We designed an experiment requiring twenty subjects to perform six commonly used gestures. The performance of decoding ultrasound signals was analyzed offline using four classification algorithms and then was assessed in online control. The average values of online classification accuracy with and without wearing the physical prosthetic were 91.5 $\pm 6.4\%$ and 96.5 $\pm 3.0\%$ , respectively. We found that wearing the prosthetic hand influenced the ultrasound gestures classification accuracy, but remarkable online classification performance could still be maintained. These experimental results demonstrated the efficacy of the designed integrated ultrasound system for practical use, paving the way for an effective HMI system that could be widely used in prosthetic hand control.

Motion estimation plays an important role in motor coordination exploration and optimal control of human-machine interaction. Muscular strength training, such as isokinetic exercise, aims to improve one’s muscular strength and endurance with adjustable resistance at a constant speed. To explore motion estimation and muscle coordination, this paper proposes a sEMG based musculoskeletal model to estimate joint angle trajectory of elbow isokinetic exercise to build the relationship between surface-Electromyography (sEMG) and its related joint movement. A set of motion protocol is designed to evaluate the model, including isokinetic movements under 100% and 50% joint torque levels (TL), and isotonic exercise. sEMG sensors and isokinetic dynamometer are employed in which muscle activations are calculated from sEMG signals as the input of musculoskeletal model. The experiment of three motion protocols is conducted by seven healthy subjects. The synergy patterns indicate the muscle coordination consistency with $\text{R}^{2} {>}$ 0.92. The estimated joint angle trajectory confirms that the musculoskeletal model predicts the isokinetic movement and isotonic movement accurately with $\text{R}^{2} {>}$ 0.90 and the normalized root mean squared deviation NRMSD < 0.18. The research outcome in different levels of muscle forces paves the way for tailoring isokinetic exercise in clinical therapy such as stroke rehabilitation.

Cranial microsurgery is a prerequisite for neuroscience research, which provides an accessible brain for neural recording and optogenetic stimulation. As the scale of electrode implantation increases, massive delicate craniotomies are required to perform on the sub-millimeter-thick skull of small animals (e.g. mice). Automated craniotomy (AMC) is ideal for improving surgery efficiency and ensuring consistent results. However, the current AMC platforms are prone to over-drilling and injuring brain tissues due to the lack of effective feedback. Fragile and ultrathin skulls of these small objects also make it hard to integrate suitable sensors into the end-effector for accurate perception. Thus, this paper proposes an integrated electrical bioimpedance measurement (EBIM) solution for the closed-loop AMC. A sensing drill and a custom EBIM device are implemented to form a reliable electrical contact with the object and accurately measure the impedance. After evaluating the integrated EBIM device’s accuracy(<2% relative error) and stability, we provide a modified judgment signal for breakthrough detection and further simulate closed-loop craniotomies on an equivalent model. The experimental results demonstrate that the proposed system can achieve sensitive breakthrough detection (<0.1s delay at 100Hz excitation) and consistent drilling depth (10.3μm deviation with 10mm/min feed rate) in contrast to the gold standard (LCR meter).

The implantation of ultra-flexible neural probes is one of the key technical challenges in the field of brain-computer interfaces. Existing implantation techniques are constrained in both the overall surgical procedure and operational reliability. This study introduces a novel microneedle-forceps mechanism designed to implant ultra-flexible probes individually into brain tissue. A multimode motion control framework based on multiple linear actuators and a hybrid PID controller is developed for delicate manipulation of the probes. A needle-probe interaction model is established for force analysis in the peeling and insertion process. Detailed experiments are conducted to demonstrate the clamping ability of forceps to promote probe peeling and reduce needle deflection. Moreover, cooperative pull-up, insertion, release and retraction by the microneedle-forceps are validated to achieve multiple probe implantations within a single insertion duration of 10 s and a density of 0.2 mm.

Multi-thread flexible microelectrodes hold the promise of high-quality and long-term neural signal recording. To implant fragile microelectrodes into brain, a microneedle is usually used as a shuttle to increase stiffness. However, hooking microelectrodes with a microneedle is error-prone and time-consuming, due to the difficulty in accurately obtaining micro-object coordinates through low-quality microscope images. To solve this problem, this paper proposes an intelligent microscope vision system based on few-shot segmentation method. Firstly, a stereo vision system is designed to enable cameras to focus on all microelectrode threads simultaneously. Secondly, a few-shot instance segmentation method is proposed to obtain keypoint coordinates for vision guidance, which consists of data augmentation, improved Mask-RCNN and anchor-free clustering loss. The data augmentation method synthesizes low-quality images to strengthen model’s generalization ability. To improve the proposal quality, a residual region proposal module is introduced into Mask-RCNN. By clustering instance features in metric space, the anchor-free clustering loss enhances the model capability of predicting hard instances and avoids the intra-class bias of fixed anchor. Experimental result shows that the proposed method achieves 99.72% and 90% correct rate for microelectrode and microneedle segmentation with 5-shot training. Code is available at https://github.com/NamingIsEasy/FSS_implantation.