In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.

Accurately extracting the Region of Interest (ROI) of a palm print was crucial for subsequent palm print recognition. However, under unconstrained environmental conditions, the user\'s palm posture and angle, as well as the background and lighting of the environment, were not controlled, making the extraction of the ROI of palm print a major challenge. In existing research methods, traditional ROI extraction methods relied on image segmentation and were difficult to apply to multiple datasets simultaneously under the aforementioned interference. However, deep learning-based methods typically did not consider the computational cost of the model and were difficult to apply to embedded devices. This article proposed a palm print ROI extraction method based on lightweight networks. Firstly, the YOLOv5-lite network was used to detect and preliminarily locate the palm, in order to eliminate most of the interference from complex backgrounds. Then, an improved UNet was used for keypoints detection. This network model reduced the number of parameters compared to the original UNet model, improved network performance, and accelerated network convergence. The output of this model combined Gaussian heatmap regression and direct regression and proposed a joint loss function based on JS loss and L2 loss for supervision. During the experiment, a mixed database consisting of 5 databases was used to meet the needs of practical applications. The results showed that the proposed method achieved an accuracy of 98.3% on the database, with an average detection time of only 28ms on the GPU, which was superior to other mainstream lightweight networks, and the model size was only 831k. In the open-set test, with a success rate of 93.4%, an average detection time of 5.95ms on the GPU, it was far ahead of the latest palm print ROI extraction algorithm and could be applied in practice.

Intravascular papillary endothelial hyperplasia (IPEH), also called masson tumor which is a benign vascular tumor named after Pierre Masson, the French pathologist who originally described it in 1923, terming it \"hémangioendothéliome végétant intravasculaire.\" It is characterized by a reactive proliferation of endothelial cells associated with thrombosis. Ultrasound and MRI are the main imaging examinations, but the diagnosis of IPEH was confirmed by biopsy pathology and immunohistochemistry. It is generally accepted that surgical excision is the first choice of treatment. In this report, we report a case of Masson tumor arising in the right wrist.

BACKGROUND: Macrodactyly is a rare congenital malformation characterized by an increase in the size of all structures of a digit, accounting for less than 1% of all congenital upper extremity conditions.
METHODS: We report a case involving a 49-year-old woman who presented for the first time with untreated, radial-sided hand macrodactyly. We performed soft tissue debulking, amputation, median nerve neurotomy and coaptation, and carpal tunnel release. At the 6-year follow-up, no significant growth was observed in the bone or soft tissue of the affected area.
CONCLUSIONS: Tissue overgrowth in patients with progressive macrodactyly can continue and progress excessively with age. Median nerve neurotomy and coaptation play a crucial role in preventing recurrence of the deformity.

BACKGROUND: During a robot-assisted minimally invasive surgery, hand tremors in a surgeon\'s manipulation of the master manipulator can cause vibrations of the slave surgical instruments.
METHODS: This letter addresses this problem by proposing an improved Enhanced Band-Limited Multiple Linear Fourier Combiner (E-BMFLC) algorithm for filtering the physiological tremor signals of a surgeon\'s hand. The proposed method uses the amplitude of the input signal to adapt the learning rate and a dense division of the combiner bands for the higher amplitude bands of the tremor signals.
RESULTS: By using the proposed improved E-BMFLC algorithm, the compensation accuracy can be improved by 4.5%-8.9%, as well as a spatial position error of less than 1 mm.
CONCLUSIONS: The results show that among all filtering methods, the improved E-BMFLC filtering method has the highest number of successful experiments and the lowest experimental time.

This research proposes an innovative, intelligent hand-assisted diagnostic system aiming to achieve a comprehensive assessment of hand function through information fusion technology. Based on the single-vision algorithm we designed, the system can perceive and analyze the morphology and motion posture of the patient\'s hands in real time. This visual perception can provide an objective data foundation and capture the continuous changes in the patient\'s hand movement, thereby providing more detailed information for the assessment and providing a scientific basis for subsequent treatment plans. By introducing medical knowledge graph technology, the system integrates and analyzes medical knowledge information and combines it with a voice question-answering system, allowing patients to communicate and obtain information effectively even with limited hand function. Voice question-answering, as a subjective and convenient interaction method, greatly improves the interactivity and communication efficiency between patients and the system. In conclusion, this system holds immense potential as a highly efficient and accurate hand-assisted assessment tool, delivering enhanced diagnostic services and rehabilitation support for patients.

In order to better design handling-assisted exoskeletons, it is necessary to analyze the biomechanics of human hand movements. In this study, Anybody Modeling System (AMS) simulation was used to analyze the movement state of muscles during human handling. Combined with surface electromyography (sEMG) experiments, specific analysis and verification were carried out to obtain the position of muscles that the human body needs to assist during handling. In this study, the simulation and experiment were carried out for the manual handling process. A treatment group and an experimental group were set up. This study found that the vastus medialis muscle, vastus lateralis muscle, latissimus dorsi muscle, trapezius muscle, deltoid muscle and triceps brachii muscle require more energy in the process of handling, and it is reasonable and effective to combine sEMG signals with the simulation of the musculoskeletal model to analyze the muscle condition of human movement.

In current smart classroom research, numerous studies focus on recognizing hand-raising, but few analyze the movements to interpret students\' intentions. This limitation hinders teachers from utilizing this information to enhance the effectiveness of smart classroom teaching. Assistive teaching methods, including robotic and artificial intelligence teaching, require smart classroom systems to both recognize and thoroughly analyze hand-raising movements. This detailed analysis enables systems to provide targeted guidance based on students\' hand-raising behavior. This study proposes a morphology-based analysis method to innovatively convert students\' skeleton key point data into several one-dimensional time series. By analyzing these time series, this method offers a more detailed analysis of student hand-raising behavior, addressing the limitations of deep learning methods that cannot compare classroom hand-raising enthusiasm or establish a detailed database of such behavior. This method primarily utilizes a neural network to obtain students\' skeleton estimation results, which are then converted into time series of several variables using the morphology-based analysis method. The YOLOX and HrNet models were employed to obtain the skeleton estimation results; YOLOX is an object detection model, while HrNet is a skeleton estimation model. This method successfully recognizes hand-raising actions and provides a detailed analysis of their speed and amplitude, effectively supplementing the coarse recognition capabilities of neural networks. The effectiveness of this method has been validated through experiments.

OBJECTIVE: Rheumatoid arthritis (RA) is a severe and common autoimmune disease. Conventional diagnostic methods are often subjective, error-prone, and repetitive works. There is an urgent need for a method to detect RA accurately. Therefore, this study aims to develop an automatic diagnostic system based on deep learning for recognizing and staging RA from radiographs to assist physicians in diagnosing RA quickly and accurately.
METHODS: We develop a CNN-based fully automated RA diagnostic model, exploring five popular CNN architectures on two clinical applications. The model is trained on a radiograph dataset containing 240 hand radiographs, of which 39 are normal and 201 are RA with five stages. For evaluation, we use 104 hand radiographs, of which 13 are normal and 91 RA with five stages.
RESULTS: The CNN model achieves good performance in RA diagnosis based on hand radiographs. For the RA recognition, all models achieve an AUC above 90% with a sensitivity over 98%. In particular, the AUC of the GoogLeNet-based model is 97.80%, and the sensitivity is 100.0%. For the RA staging, all models achieve over 77% AUC with a sensitivity over 80%. Specifically, the VGG16-based model achieves 83.36% AUC with 92.67% sensitivity.
CONCLUSIONS: The presented GoogLeNet-based model and VGG16-based model have the best AUC and sensitivity for RA recognition and staging, respectively. The experimental results demonstrate the feasibility and applicability of CNN in radiograph-based RA diagnosis. Therefore, this model has important clinical significance, especially for resource-limited areas and inexperienced physicians.

Tactile sensors play an important role when robots perform contact tasks, such as physical information collection, force or displacement control to avoid collision. For these manipulations, excessive contact may cause damage while poor contact cause information loss between the robotic end-effector and the objects. Inspired by skin structure and signal transmission method, this paper proposes a tactile sensing system based on the self-sensing soft pneumatic actuator (S-SPA) capable of providing tactile sensing capability for robots. Based on the adjustable height and compliance characteristics of the S-SPA, the contact process is safe and more tactile information can be collected. And to demonstrate the feasibility and advantage of this system, a robotic hand with S-SPAs could recognize different textures and stiffness of the objects by touching and pinching behaviours to collect physical information of the various objects under the positive work states of the S-SPA. The result shows the recognition accuracy of the fifteen texture plates reaches 99.4%, and the recognition accuracy of the four stiffness cuboids reaches 100%by training a KNN model. This safe and simple tactile sensing system with high recognition accuracies based on S-SPA shows great potential in robotic manipulations and is beneficial to applications in domestic and industrial fields.