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Flexible, breathable and hydrophobic iontronic tactile sensors for comfortable skin-attachable health monitoring

MATERIALS LETTERS 2025年 382卷

作者： Hilario, Jonas Wang, Peng Meng, Chuizhou Hebei Univ Technol Engn Res Ctr Minist Educ Intelligent Rehabil Devic State Key Lab Reliabil & Intelligence Elect Equipm Hebei Key Lab Smart Sensing & Human Robot Interact Tianjin 300401 Peoples R China Univ Jinan Sch Mech Engn Jinan 250022 Peoples R China

Conventional flexible sensors are built on impermeable substrates, which arises uncomfortable wearing issue during long-term skin-attachable usage. Herein, we develop a flexible, breathable and hydrophobic tactile sensor by utilizing permeable fabric and foam. The sensor adopts iontronic supercapacitive sensing mechanism, offering high sensitivity (376.19 kPa(-1) within 40 kPa), and shows air permeability (65.12 mms(-1)) and hydrophobicity (contact angle similar to 118.9 degrees). Various wearable sensing applications by attaching to skin for long time with comfort are demonstrated.

关键词： Flexible tactile sensor Iontronic pressure sensing Breathable Waterproof Health monitoring

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Wearable Electronic Glove and Multilayer Para-LSTM-CNN-Based Method for Sign Language Recognition

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IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 40787-40799页

作者： Wang, Dapeng Wang, Mingyuan Zhang, Ziqi Liu, Teng Meng, Chuizhou Guo, Shijie Hebei Univ Technol Engn Res Ctr Minist Educ Intelligent Rehabil Devic State Key Lab Reliabil & Intelligence Elect Equipm Hebei Key Lab Smart Sensing & Human Robot Interact Tianjin 300401 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China

Communication between normal people and hearing-impaired people is usually realized with the sign language. However, the communication quality is limited by the familiarity of the people with sign language. Wearable sign language recognition systems, especially those based on flexible electronic glove, have attracted much attention due to their efficiency for sign language recognition. However, there are still two problems need to be solved in current research: 1) the size or weight of wearable device is usually inversely proportional to the fineness of the hand movement information captured and 2) both spatial features and temporal features are important for sign language recognition, and current algorithms usually do not achieve sufficient extraction. To address the above problems, a wearable electronic glove with new multilayer parallel LSTM-CNN (Para-LSTM-CNN) network is proposed. The designed wearable electronic glove is only 126 g in weight, and utilizes inertial measurement units (IMUs) and Flex sensors to capture hand motion signals. 41-dimensions of hand motion signals can be captured and uploaded to computer via Wi-Fi. A Para-LSTM-CNN algorithm based on parallel features extraction strategy is proposed. The temporal and spatial features of the sensor signals are extracted through long short-term memory (LSTM) and convolutional neural network (CNN), respectively. The Para-LSTM-CNN can effectively improve the accuracy of sign language recognition by fusing spatial and temporal features synchronously. The proposed system has a high sign-language-recognition accuracy of 97.01% for 26 gestures. Result shows that the proposed system promises to provide effective solutions for patients with hearing-impaired. The available code of Para-LSTM-CNN can be found at https://***/1104162390-A/Para-LSTM-CNN/tree/main.

关键词： Feature extraction Sign language Sensors Wearable devices Accuracy Flexible printed circuits Classification algorithms Parallel features extraction sign language recognition temporal features wearable electronic glove

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Cu Single-Atom Decorated Three-Dimensional Nitrogen-Carbon Framework as an Oxygen Reduction Reaction Catalyst for High-Performance Zinc-Air Batteries

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ACS APPLIED NANO MATERIALS 2023年第1期7卷 1108-1119页

作者： Ma, Zhaolei Bai, Ruoning Yu, Wei Li, Guoxian Meng, Chuizhou Hebei Univ Technol Hebei Key Lab Smart Sensing & Human Robot Interact State Key Lab Reliabil & Intelligence Elect Equipm Sch Mech EngnEngn Res CtrMinist Educ Intelligent Tianjin 300401 Peoples R China

Transition metal single atoms have been considered as promising candidates for oxygen reduction reaction catalysts for developing high-performance zinc-air batteries. How to uniformly disperse metal single atoms onto an ideal porous carbon framework for full utilization of the catalyst with enriched active sites and fast mass diffusion still remains a challenge. Herein, by taking advantage of both the efficient single-atom Cu catalyst and the effective NaCl-template sacrificing method, we report the design and fabrication of a single-atom Cu catalyst decorated on a three-dimensional (3D) nitrogen-carbon (NC) framework. The developed catalyst exhibits excellent (oxygen reduction reaction) ORR catalytic activity with a high half-wave potential of 0.86 V, maximum current density of 5.53 mA cm(-2), and Tafel slope of 81.52 mV dec(-1), which are better than those of the commercial Pt/C catalyst. The comparison experiment with the 3D NC framework (no copper salt added) and the bulk Cu decorated NC framework (no NaCl added) verifies the synergistic effect between the Cu single atoms as the efficient ORR catalyst and the NaCl template to achieve the ideal porous structure. Aqueous zinc-air batteries assembled with the developed catalyst deliver good battery performance with a high open circuit voltage of 1.48 V, large specific discharge capacity of 760.47 mAh g(-1), high peak power density of 120 mW cm(-2), and stable charge-discharge cyclability for 600 h. Gel polymer electrolyte-based zinc-air batteries are also demonstrated to be workable under bending conditions for practical wearable electronic applications.

关键词： zinc-air battery oxygen reduction reaction (ORR) single-atom catalyst three-dimensional (3D) carbon framework

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Classification of Respiratory Sounds into Crackles and Noncrackles Categories via Convolutional Neural Networks 21

Classification of Respiratory Sounds into Crackles and Noncr...

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21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Qi Dexuan Ye, Yuan Zhao Haiwen Wu Wenjuan Guo Shijie Minist Educ Intelligent Rehabil Equipment & Detec Engn Res Ctr Tianjin Peoples R China Hebei Key Lab Robot Sensing & Human Robot Interac Tianjin Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China Wuhan Jinyintan Hosp Wuhan Peoples R China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084;9798350388077

When evaluating the efficacy of expectoration, it is essential not only to consider the subjective perception of patients but also to rely on objective assessment indicators. Crackles, as a crucial indicator for evaluating expectoration effectiveness, have rarely been independently classified in existing research. Additionally, approximately half of pulmonary sounds are incorrectly identified by hospital interns and medical students. To address these challenges, an automatic identification model for crackles is developed. A Convolutional Neural Network (CNN) is employed to classify 1864 instances of crackles and 2291 instances of noncrackles (including rhonchi, wheezes, and normal lung sounds). The model combines pre-trained image feature extractors, respiratory sound data, and a CNN classifier. After training and evaluation, a predictive accuracy of 0.875 is achieved, with an area under the receiver operating characteristic (ROC) curve of 0.89. This research aims to provide objective assessment indicators for expectoration treatment for patients through our research findings.

关键词： Crackles Expectoration Convolutional Neural Network

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A Wearable Mechanical Pressure-Electrophysiological Bimodal sensing System for Rehabilitation Electromechanical Device

A Wearable Mechanical Pressure-Electrophysiological Bimodal ...

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2024 International Conference on Intelligent robots and Systems

作者： Wang, Peng Liu, Jixiao Qi, Dianpeng Guo, Shijie Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China Hebei Univ Technol Hebei Key Lab Smart Sensing & Human Robot Interac Tianjin 300132 Peoples R China Harbin Inst Technol Sch Chem Engn Harbin 150006 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

With the aging of society, there has been an increase in the number of elderly individuals with limb movement disorders. Active rehabilitation training using limb rehabilitation electromechanical devices that incorporate multimodal sensing and monitoring functions can significantly contribute to the recovery of limb motor functions. This report introduces a wearable mechanical pressure-electrophysiological monitoring bimodal sensing system specifically designed for human limb rehabilitation devices. By utilizing just four electrodes (SE, CE/DE, GND, REF), this system enables simultaneous and co-located measurement of surface electromyographic (sEMG), pressure, and mechanomyography (MMG) signals. These signals can be utilized to analyze muscle tension, stiffness, and tremor information. At last, this sensing system was used to assess muscle contraction force and localized muscle fatigue. The time and frequency domain characteristics of physiological signals during exercise were thoroughly investigated. The wearable mechanical pressure-electrophysiological bimodal sensing system can provide valuable data references for rehabilitation robots or human limb rehabilitation device, which is of great significance in the diagnosis of muscular diseases and rehabilitation treatment.

关键词： Electromyography

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Modeling and Simulation of Microstructure Geometry and Material Type on Flexible Capacitive Pressure Sensors by Finite Element Analysis

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IEEE SENSORS JOURNAL 2025年第3期25卷 4811-4819页

作者： Hilario, Jonas Wang, Haihang Wang, Peng Yu, Wei Meng, Chuizhou Hebei Univ Technol Minist Educ Intelligent Rehabil Device & Detect Te Sch Mech Engn Hebei Key Lab Smart Sensing & Human Robot Interact Tianjin 300401 Peoples R China Univ Jinan Sch Mech Engn Jinan 250022 Peoples R China

This article presents the modeling and simulation of flexible capacitive pressure sensors based on the change of geometry, shape, and material for sensing performance improvement. For simplicity and time saving without significant loss of accuracy, the 2-D models in COMSOL Multiphysics are constructed based on different micropatterning structures. The Von Misses stress and displacement distributions and the absolute and relative capacitance changes are observed to investigate the sensing behaviors. The simulation findings indicate that the proposed model enables efficient information between the top electrode layer and the middle microstructured dielectric layer that enter into contact to create the geometry change, consequently enhancing the capacitive sensitivity. These findings provide valuable insights into the optimal sensor design with structural and material selection for flexible pressure sensors.

关键词： 2-D model capacitive pressure sensor COMSOL Multiphysics finite element analysis (FEA) microstructure 2-D model capacitive pressure sensor COMSOL Multiphysics finite element analysis (FEA) microstructure

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Effects of a lumbar exoskeleton that provides two traction forces on spinal loading and muscles

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2025年 13卷 1530034页

作者： Qi, Kaicheng Yin, Zhiyang Li, Chao Zhang, Jianjun Song, Jingke Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China Hebei Prov Key Lab Robot Sensing & Human Machine I Tianjin Peoples R China Minist Educ Intelligent Rehabil Devices & Testing Engn Res Ctr Tianjin Peoples R China

In workplaces with prolonged or repetitive stooping, disc herniation due to excessive pressure at the lumbar L5/S1 joints has been a difficult condition to prevent and treat in the field of lower back pain. Previous research on lumbar exoskeletons mainly focused on the impact of assistive torque on muscle activation, overlooking the fact that providing assistive torque is not the optimal approach when bending over with a low load. Instead, using traction force to reduce disc pressure is a more adaptable method to mitigate the risk of intervertebral disc herniation. In this paper, a novel lumbar exoskeleton mechanism is proposed. The exoskeleton principle is similar to a lever arranged on the torso, which provides two types of traction forces using a single compression spring with a lower support moment and higher traction performance. Subsequently, a kinetic simulation model covering passive physiologic tissues and spring stiffness was developed to determine the optimal range of traction forces for a given load, to predict the disc pressure and muscle activation at optimal traction forces. Eight subjects were invited to wear the exoskeleton for stooping and lifting tests under extreme loads, using myoelectric sensors to measure muscle activation. The results confirm that optimal traction force effectively reduces L5/S1 disc pressure without additionally increasing muscle activation. The exoskeleton in this study provides an alternative idea for the design of lumbar exoskeletons adapted to light load stooping.

关键词： lumbar exoskeleton traction forces intervertebral disc pressure stiffness optimization electromyography

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Two-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network

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GAIT & POSTURE 2025年 117卷 191-203页

作者： Liu, Qiming Wang, Shan Dai, Yuxing Wu, Xingfu Guo, Shijie Su, Weihua Hebei Univ Technol Engn Res Ctr Minist Educ Intelligent Rehabil Equipment & Detect Tianjin 300401 Peoples R China Hebei Univ Technol Hebei Key Lab Robot Sensing & Human Robot Interact Tianjin 300401 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Technol Vehicle Changsha 410082 Peoples R China

Background: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals. Therefore, this paper proposes a two-dimensional recognition method of lower limb gait features based on sEMG signal decomposition under multiple motion modes to improve the accuracy and robustness of gait recognition. Methods: First, in order to obtain gait information of human lower limbs, gait experiments in different motion modes are carried out using the sEMG acquisition system with 7 channels. Then, the gait dataset of human lower limbs is expanded and transformed using the variational modal decomposition (VMD) algorithm and Gramian Angular Field (GAF). The processing not only enhances the data, improves the learning ability of classifiers and avoid the overfitting during the training of the convolutional neural network (CNN), but also effectively utilizes the feature extraction capability of the CNN and preserves the temporal correlation of the EMG. Finally, the gait features in four motion modes are recognized using the processed sEMG data and trained ResNet network. Results: The recognition results show that the proposed method in this paper has the highest recognition rate under four motion modes compared to BP neural network and CNN network based on original sEMG signal. This research is helpful for the effective implementation of intelligent control strategies and the coordination of human-exoskeleton system.

关键词： Gait recognition Surface electromyographic signal Variational modal decomposition Convolutional neural network Exoskeletons

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Knowledge-driven eutectic electrolyte design for Zn-ion batteries

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CHEMICAL ENGINEERING JOURNAL 2025年 511卷

作者： Chang, Jieyu Liu, Qianqian Sun, Chun Hu, Jinhua Bai, Jiabao Li, Guoxian Meng, Chuizhou Wang, Liguang Hebei Univ Technol Sch Elect & Informat Engn Key Lab Elect Mat & Devices Tianjin Tianjin 300401 Peoples R China Hebei Univ Technol Sch Mech Engn State Key Lab Reliabil & Intelligence Elect Equipm Hebei Key Lab Smart Sensing & Human Robot Interact Tianjin 300401 Peoples R China Zhejiang Univ Coll Chem & Biol Engn Hangzhou 310058 Peoples R China

Eutectic electrolytes are considered as promising candidates for Zn-ion batteries (ZIBs) due to the wide liquid range, broad electrochemical window, high safety, high tunability, and low cost. However, current design of eutectic electrolytes primarily relies on trial-and-error methods, which are time-consuming and costly due to their complex structure and compositions. To address these challenges, we propose an ionic conductivity prediction method of eutectic electrolytes through data-driven machine learning. To improve model interpretability, we construct domain knowledge-driven features based on the physicochemical properties of electrolyte components. By integrating domain knowledge, the Categorical Boosting (CatBoost) model achieves high accurate predictions of ionic conductivity. Moreover, feature contribution quantified by the Shapley Additive exPlanations (SHAP) identifies donor number of anion and solvent as well as dipole moment of H2O as most significant descriptors affecting ionic conductivity. This understanding offers a fundamental design principle for developing high-ionic-conductivity eutectic electrolytes. The eutectic electrolytes designed with our machine learningassisted method are tested in Zn||Zn symmetrical cells, providing the experimental evidence for high ionic conductivity and stability. This work provides a new guidance for designing high-safety and high-performance eutectic electrolytes for ZIBs that can operate efficiently over a broad temperature range.

关键词： Machine learning Ionic conductivity Domain knowledge-based features Eutectic electrolytes

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Experimental and numerical research on the characteristics of heavy gas leakage and diffusion

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PROCESS SAFETY PROGRESS 2022年第3期41卷 567-580页

作者： Shi, Bao-Jun Wang, Hui Nie, Shi-Ming Cheng, Cheng Li, Tie-Jun Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Key Lab Robot Sensing & Human Robot Integra Tianjin Peoples R China

Once a hazardous gas leaks, it will pose a serious threat to the safety of people's lives and property. This paper aims to investigate the flow characteristics of heavy gas leakage and diffusion to deal with sudden accidents caused by hazardous gas leakage. First, an experimental platform for the leakage and diffusion of heavy gas is built, and the tracer technique is adopted to visualize the experimental phenomena during the process of gas leakage and diffusion. Second, a computational fluid dynamics (CFD) model is built based on the experimental platform to simulate and analyze the gas leakage and diffusion process. The CFD model is verified by comparing the numerical results with those of experiments using an authoritative evaluation standard procedure. The comparison shows that the related statistical performance measures are all reasonable. Third, a three-dimensional numerical model of a real tank farm is created in full scale and the heavy gas (liquefied petroleum gas) leakage process is simulated and analyzed considering also the effect of wind. Experimental and numerical results show that the heavy gas leakage and diffusion process can be divided into three stages: gas concentration rising stage, gravity sedimentation stage, and gas passive diffusion stage, in which different flow phenomena are observed with different flow characteristics for each of the stages. The phenomena observed in experiment are further analyzed and clarified using fluid dynamics by numerical simulation. The full-scale modeling and simulation of the real tank farm is helpful for taking possible measures once a gas leakage occurs.

关键词： computational fluid dynamics design and simulation evacuation route heavy gas leakage and diffusion

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