Finger-Individuating Exoskeleton System with Non-Contact Leader-Follower Control Strategy.

This paper proposes a novel finger-individuating exoskeleton system with a non-contact leader-follower control strategy that effectively combines motion functionality and individual adaptability. Our solution comprises the following two interactive components: the leader side and the follower side. The leader side processes joint angle information from the healthy hand during motion via a Leap Motion Controller as the system input, providing more flexible and active operations owing to the non-contact manner. Then, as the follower side, the exoskeleton is driven to assist the user's hand for rehabilitation training according to the input. The exoskeleton mechanism is designed as a universal module that can adapt to various digit sizes and weighs only 40 g. Additionally, the current motion of the exoskeleton is fed back to the system in real time, forming a closed loop to ensure control accuracy. Finally, four experiments validate the design effectiveness and motion performance of the proposed exoskeleton system. The experimental results indicate that our prototype can provide an average force of about 16.5 N for the whole hand during flexing, and the success rate reaches 82.03% in grasping tasks. Importantly, the proposed prototype holds promise for improving rehabilitation outcomes, offering diverse options for different stroke stages or application scenarios.

ABR-Attention: An Attention-Based Model for Precisely Localizing Auditory Brainstem Response.

Auditory Brainstem Response (ABR) is an evoked potential in the brainstem's neural centers in response to sound stimuli. Clinically, characteristic waves, especially Wave V latency, extracted from ABR can objectively indicate auditory loss and diagnose diseases. Several methods have been developed for the extraction of characteristic waves. To ensure the effectiveness of the method, most of the methods are time-consuming and rely on the heavy workloads of clinicians. To reduce the workload of clinicians, automated extraction methods have been developed. However, the above methods also have limitations. This study introduces a novel deep learning network for automatic extraction of Wave V latency, named ABR-Attention. ABR-Attention model includes a self-attention module, first and second-derivative attention module, and regressor module. Experiments are conducted on the accuracy with 10-fold cross-validation, the effects on different sound pressure levels (SPLs), the effects of different error scales and the effects of ablation. ABR-Attention shows efficacy in extracting Wave V latency of ABR, with an overall accuracy of 96.76 ± 0.41 % and an error scale of 0.1ms, and provides a new solution for objective localization of ABR characteristic waves.

Highly Anthropomorphic Finger Design With a Novel Friction Clutch for Achieving Human-Like Reach-and-Grasp Movements.

In the design of prosthetic hand fingers, achieving human-like movement while meeting anthropomorphic demands such as appearance, size, and lightweight is quite challenging. Human finger movement involves two distinct motion characters during natural reach-and-grasp tasks: consistency in the reaching stage and adaptability in the grasping stage. The former one enhances grasp stability and reduces control complexity; the latter one promotes the adaptability of finger to various objects. However, conventional tendon-driven prosthetic finger designs typically incorporate bulky actuation modules or complex tendon routes to reconcile the consistency and adaptability. In contrast, we propose a novel friction clutch consisting of a single tendon and slider, which is simple and compact enough to be configurated within the metacarpal bone. Through tactfully exploiting the friction force to balance the gravity effect on each phalanx during finger motion, this design effectively combines both consistency and adaptability. As a result, the prosthetic finger can maintain consistent motion unaffected by any spatial posture during reaching, execute adaptive motion during grasping, and automatically switch between them, resulting in human-like reach-and-grasp movements. Additionally, the proposed finger achieves a highly anthropomorphic design, weighing only 18.9 g and possessing the same size as an adult's middle finger. Finally, a series of experiments validate the theoretical effectiveness and motion performance of the proposed design. Remarkably, the mechanical principle of the proposed friction clutch is beneficial to achieve highly anthropomorphic design, providing not only a new strategy to prosthetic hand design but also great potential in hand rehabilitation.

Towards Optimizing the Quality of Long-Term Physiological Signals Monitoring by Using Anhydrous Carbon Paste Electrode.

Long-term physiological signal monitoring is very important for the diagnosis of health conditions that occur randomly and cannot be easily detected by a short period of a hospital visit. However, the conventional wet electrodes suffered from the problem of signal quality degradation due to the gradual dehydration of the conductive gel. An anhydrous carbon paste electrode (CPE) constructed by a composite of carbon black and polydimethylsiloxane was proposed to enable long-term physiological signal monitoring without signal quality degradation as time elapses. The performance was systematically compared with conventional electrodes when measuring long-term physiological signals including electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and auditory brainstem response (ABR). The proposed CPE showed more stable skin-electrode impedance and higher signal qualities as the monitoring time increased up to 48 days, with signal-to-noise ratios (SNRs) of 16.43 ± 10.39 dB higher for ECG and 24.30 ± 7.79 dB higher for EMG when compared with wet electrodes. The CPE method could also obtain more consistent ABR waveform morphologies and could measure EEG under sweating conditions. It is believed that the proposed CPE could be a potential candidate for durable and robust wearable sensors systems on long-term physiological signal monitoring.

Towards high-accuracy classifying attention-deficit/hyperactivity disorders using CNN-LSTM model.

Objective. The neurocognitive attention functions involve the cooperation of multiple brain regions, and the defects in the cooperation will lead to attention-deficit/hyperactivity disorder (ADHD), which is one of the most common neuropsychiatric disorders for children. The current ADHD diagnosis is mainly based on a subjective evaluation that is easily biased by the experience of the clinicians and lacks the support of objective indicators. The purpose of this study is to propose a method that can effectively identify children with ADHD.Approach. In this study, we proposed a CNN-LSTM model to solve the three-class problems of classifying ADHD, attention deficit disorder (ADD) and healthy children, based on a public electroencephalogram (EEG) dataset that includes event-related potential (ERP) EEG signals of 144 children. The convolution visualization and saliency map methods were used to observe the features automatically extracted by the proposed model, which could intuitively explain how the model distinguished different groups.Main results. The results showed that our CNN-LSTM model could achieve an accuracy as high as 98.23% in a five-fold cross-validation method, which was significantly better than the current state-of-the-art CNN models. The features extracted by the proposed model were mainly located in the frontal and central areas, with significant differences in the time period mappings among the three different groups. The P300 and contingent negative variation (CNV) in the frontal lobe had the largest decrease in the healthy control (HC) group, and the ADD group had the smallest decrease. In the central area, only the HC group had a significant negative oscillation of CNV waves.Significance. The results of this study suggest that the CNN-LSTM model can effectively identify children with ADHD and its subtypes. The visualized features automatically extracted by this model could better explain the differences in the ERP response among different groups, which is more convincing than previous studies, and it could be used as more reliable neural biomarkers to help with more accurate diagnosis in the clinics.

Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand.

Although arch motions of the palm substantially contribute to frequent hand grasping, they are usually neglected in the development of prosthetic hands which focuses on digit movements. We designed the arch function for its implementation on an adaptive multi-finger prosthetic hand. The digits from the developed hand can perform adaptive grasping, and two carpometacarpal joints enable the palm of the prosthetic hand to form an arch with the thumb. Moreover, the arch posture can be passively released, mimicking the human hand switching between sphere and medium wrap grasps according to the situation. Other requirements such as weight, cost, and size limitations for hand prostheses were also considered. As a result, we only used three actuators fully embedded in the palm through a novel tendon-driven transmission. Although the prosthetic hand is almost the same size of an adult hand, it weighs only 146 g and can perform 70% of the 10 most frequent grasps.