Yue Zhang

The clinical assessment of spinocerebellar ataxia type 3 (SCA3) is hindered by subjective factors and inter‐rater variability. This study utilizes a previously validated wearable plantar pressure insole system established in our prior work to build a multiscenario digital balance detection framework, and assesses its efficacy in differentiating SCA3 patients from healthy individuals and in evaluating disease severity. The study involved 74 SCA3 patients and 45 healthy controls, who were equipped with custom‐developed plantar force sensors to perform standing tasks with eyes open (EO) and eyes closed (EC). Center of pressure (COP) variables were analyzed to determine their correlation with clinical characteristics and their discriminative capability. An XGBoost model was employed to classify disease severity, with SHAP analysis providing enhanced interpretability. COP variables derived from EC tasks demonstrated superior performance in terms of area under the curve, accuracy, sensitivity, and specificity compared to those from EO tasks. Significant correlations were identified between COP variables, such as the eyes closed, left side sway velocity (EC‐L velocity) SD, and clinical scores ( r > 0.4). The model achieved an accuracy of 87% in classifying disease severity, with SHAP analysis highlighting key variables and interactions. COP variables offer a robust, multidimensional measure of motor dysfunction.

Purpose Traditional hypertonia diagnosis relies on the Modified Ashworth Scale (MAS), which is subjective and dependent on doctors’ experience. Although previous studies have explored the use of force sensors and surface electromyography (sEMG), finding a reliable and valid detection method remains a challenge. This study aims to develop a simple yet effective platform that integrates biomechanical and sEMG data for upper-limb muscle tone assessment, providing a more objective and quantitative evaluation approach. Methods A detection platform was developed to collect biomechanical and sEMG data from 59 subjects, including 49 patients (MAS Ⅰ = 21, MAS Ⅰ + = 16, MAS Ⅱ = 12) and 10 healthy individuals, at different movement speeds (15°/s, 20°/s, and 25°/s). The acquired data underwent feature extraction, including signal processing and statistical analysis. Dimensionality reduction was applied to optimize the extracted features, and these features were then integrated into a classification algorithm for further analysis. Results The extracted features effectively distinguished patients from healthy individuals, with statistically significant differences (p < 0.01). Furthermore, the strong correlation between the extracted features and MAS scores (p < 0.01) confirmed the reliability of the proposed method. Finally, the classification algorithm demonstrated high consistency with clinical evaluations, validating its potential for clinical application in muscle tone assessment. Conclusion This study introduces an objective and quantitative method for assessing muscle tone, shifting away from the traditional subjective MAS evaluation. By enhancing diagnostic accuracy, the proposed approach provides a more reliable basis for hypertonia diagnosis and treatment. The findings hold significant promise for optimizing clinical decision-making, ultimately improving patient management and therapeutic strategies.

Spinocerebellar ataxia type 3 (SCA3) and the cerebellar subtype of multiple system atrophy (MSA-C) both manifest with cerebellar ataxia and postural instab

The advancement of hydrogel-based epidermal sensors that integrate multifunctionality, high transparency, rapid processing, and heightened sensitivity is of significant interest. Herein, we present an efficient approach for the fabrication of flexible dual-mode epidermal sensors through the ultraviolet (UV)-curing 3D printing of polyacrylamide (PAM)-based ionic hydrogels. The hydrogel precursor incorporates sodium dodecyl sulfate (SDS) monomers to augment the water dispersibility of the 2,4,6-trimethylbenzoyl-diphenylphosphine oxide (TPO) photoinitiator, thereby substantially increasing the photocuring efficiency of the ionic hydrogel. As a result, distinctive surface microstructures of PAM-based ionic hydrogels can be engineered for sensors with varying sensing modalities to improve detection performance. The piezoelectric tactile sensor, incorporating a concentric ring microstructure, demonstrates a sensitivity coefficient of 1.39 mV·kPa-1. Conversely, the resistive strain sensor, characterized by a high-density reticular hollow structure, exhibits the highest gauge factor of 24.87. Furthermore, each sensor modality demonstrates excellent temporal response and stability, confirming its applicability in motion monitoring and Morse code transmission.

The development of diverse microstructures has substantially contributed to recent progress in high‐performance electromagnetic wave (EMW) absorption materials, providing a versatile platform for the modulation of absorption properties. Exploring multidimensional microstructures and developing tailored and gentle strategies for their precise optimization can substantially address the current challenges posed by relatively unclear underlying mechanisms. Here, a series of 2D/1D heterogeneous NiO@PPy composites featuring hollow hierarchical microstructures are successfully synthesized using a straightforward strategy combining sacrificial templating with chemical oxidative polymerization. This strategy offers a facile and effective approach to fine‐tune the microstructure by adjusting the thickness of the polypyrrole (PPy) coating. This enables the continuous optimization of the dielectric properties and specific microstructures to maximize EMW absorption. Remarkably, the optimized 2D/1D hierarchical hollow NiO@PPy composite demonstrates an ultrathin thickness of 2.3 mm, a wide effective absorption band spanning 5.89 GHz, and a strong absorption intensity of −71.65 dB at a minimal loading of only 10 wt.%. The proposed mild and controllable preparation strategy not only provides insights for further tailoring the optimal dielectric properties of specific structures to enhance the absorption capacity, but also enriches the exploration of the underlying absorption mechanisms from the perspective of microstructure regulation.

In the field of intelligent prosthetics (IPs), the establishment of a natural interaction between prosthetic hands and amputees holds significant importance in restoring hand functionality, enhancing quality of life, and facilitating daily activities and social engagement. Prior investigations on surface electromyographic (sEMG) signals-controlled IPs have predominantly concentrated on gesture recognition, frequently neglecting the equally significant dimension of force level. This study proposes a control strategy integrating a multi-task learning (MTL) model to achieve synchronized recognition of gestures and force levels. The MTL model, incorporating shared convolutional blocks, self-attention, and multi-head attention layers, enhances prosthetic hand control for seamless user-device interaction. This study consistently showcases exceptional proficiency in recognizing gestures and force levels by conducting meticulous experimentation and validating the findings using datasets from diverse participants. Comparative assessments endorse the superiority of the MTL approach, particularly in real-time testing scenarios. The findings highlight the potential of this innovative myoelectric control strategy, empowering prosthetic users for prompt, precise, and intuitive responses, significantly augmenting their autonomy and quality of life.

Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model’s capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions.

A smart insole based on pressure sensing arrays is a simple and effective means of gait analysis assist in the assessment of human movement and neurological health. However, these smart insoles usually fail to combine high sensitivity with a wide detection range, making them only suitable for people within a certain body weight range. Here, based on the synergy of porous and air-gap structures, we develop a high-performance and high stability smart insole, which has a sensitivity of up to 16.064 kPa⁻¹ in a wide pressure range of 0.170 Pa to 248 kPa. After combined with Decision Tree machine learning model, gait classification and recognition can be as high as 99.96%. Based on these, a tap dance game was designed, which proves its ability to identify individual activities, and demonstrates its potential of application in the field of human-computer interaction and medical engineering.