SENSORS
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2021
·Vol. 21
·期 17
·5964
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IF 3.576
Wei Liu,Yineng Xiao,Xiaoming Wang,Fangming Deng
This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N−1), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.Keywords:plantar pressure detection;gait recognition;flexible hydrogel sensor
研究领域:
生物医学工程
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康复医学
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF COLLOID AND INTERFACE SCIENCE
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2024
·
IF 9.4
Haifei Wang,Guanhua Lin,Yang Lin,Yang Cui,Gang Chen,Zhengchun Peng
Based on real-time detection of plantar pressure, gait recognition could provide important health information for rehabilitation administration, fatigue prevention, and sports training assessment. So far, such researches are extremely limited due to lacking of reliable, stable and comfortable plantar pressure sensors. Herein, a strategy for preparing high compression strength and resilience conductive iongels has been proposed by implanting physically entangled polymer chains with covalently cross-linked networks. The resulting iongels have excellent mechanical properties including nice compliance (young’s modulus < 300 kPa), high compression strength (>10 MPa at a strain of 90 %), and good resilience (self-recovery within seconds). And capacitive pressure sensor composed by them possesses excellent sensitivity, good linear response even under very small stress (∼kPa), and long-term durability (cycles > 100,000) under high-stress conditions (133 kPa). Then, capacitive pressure sensor arrays have been prepared for high-precision detection of plantar pressure spatial distribution, which also exhibit excellent sensing performances and long-term stability. Further, an extremely sensitive and fast response plantar pressure monitoring system has been designed for monitoring plantar pressure of foot at different postures including upright, forward and backward. The system achieves real-time tracking and monitoring of changes of plantar pressure during different static and dynamic posture processes. And the characteristics of plantar pressure information can be digitally and photography displayed. Finally, we propose an intelligent framework for real-time detection of plantar pressure by combining electronic insoles with data analysis system, which presents excellent applications in sport trainings and safety precautions.
研究领域:
生物医学工程
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材料科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Nano Energy
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2024
·
IF 16.8
Dedong Guo,Yuanlong Li,Qihui Zhou,Zhongxiang Yu,Xueqian Liu,Shuheng Dong,Shipeng Zhang,Ho-Kun Sung,Zhao Yao,Yang Li,Yuanyue Li
Gait recognition and rehabilitation training based on pressure sensing technology have an important role in the field of rehabilitation medicine. However, existing pressure sensors that are used in this process are often fabricated from non-degradable or non-biocompatible source materials, which limits their application in long-term sustainable use. In this study, we propose a high-performance, degradable, biocompatible and flexible capacitive pressure sensor. Its dielectric layer is prepared using a polyvinyl alcohol/sodium alginate (PVA/SA) electrospun nanofiber membrane (ENM), while its upper and lower electrodes are fabricated from PVA@graphene screen-printed ENMs. Experimental results demonstrate the sensor's exceptional pressure sensing performance, characterized by its high sensitivity (0.54 kPa −1 at 0–10 kPa), low detection limit (∼2.2 Pa), rapid response time (26.6/29.9 ms), and excellent stability (5000 cycles). Furthermore, it exhibits outstanding biocompatibility, achieving a cell viability assay survival rate exceeding 97%, and remarkable degradability, completely degrading within one hour. A gait recognition and rehabilitation training system has been developed by integrating the sensors with a deep learning algorithm and wireless transmission technology. The system can effectively monitor and record dynamic parameters of lower limb swing during walking, exhibiting an impressive gait recognition accuracy of 98% over five normal and abnormal gaits. Moreover, it can guide patients in performing diverse rehabilitation actions as well as monitor their progress through action count recording. This innovative solution offers a sustainable approach to gait recognition and rehabilitation training within the field of rehabilitation medicine, thereby opening up new avenues for patient recovery.
研究领域:
康复医学
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材料科学
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传感器技术
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产品关联
DOI
高影响因子
CHEMICAL ENGINEERING JOURNAL
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2025
·
IF 13.2
Xia Yin,Shunhang Zhang,Yanning Qu,Shijin Zhang,Xinyu Zhang,Jisheng Zhao,Xiaohang Li,HanXiao Zeng,Hang Wang,Hong Liu,Mingwei Tian
Real-time and accurate monitoring of abnormal gait is highly desired in preventive healthcare and medical diagnosis of neurodegenerative disorders. However, the development of wearable monitoring devices that are both comfortable to wear and capable of real-time sensing remains a significant challenge. Herein, a high-resolution gait recognition insole (GRI) composed of the sensitivity-tunable sensing fiber is proposed. The GRI is capable of both static and dynamic plantar pressure mapping with a 144-pixel sensor array pixel. We design a sensitivity-tunable sensing fiber with core-sheath structure through conductive particles concentration gradient directed migration. The sheath layer exhibits a hierarchical architecture with micro-scale pores formed by carbon black concentration gradient directed migration. This fabrication technique strategically enhances sensor sensitivity by magnifying the initial resistance values. Furthermore, the GRI system is designed to integrate with machine learning algorithms, thereby enabling the recognition of abnormal gait patterns associated with neurological disorders. The system can accurately recognize abnormal gait with an accuracy of 98.42%, showing great potential for reliable applications in healthcare monitoring, sports training, and rehabilitation engineering.
研究领域:
生物医学工程
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预防医学
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神经科学
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产品关联
DOI
高影响因子
Nano Energy
·
2020
·
IF 16.602
Tao Yang,Hong Pan,Guo Tian,Binbin Zhang,Da Xiong,Yuyu Gao,Cheng Yan,Xiang Chu,Ningjun Chen,Shen Zhong,Lei Zhang,Weili Deng,Weiqing Yang
Piezoelectric-based wearable electronics promise potential applications in human physiological monitoring for disease prediction, diagnose and rehabilitation. Still, it is a big challenge to fulfill excellent compatibility and precisely monitoring of the complexly subtle physiological signals simultaneously. Here, we prepared a three-dimensional hierarchically interlocked PVDF/ZnO nanofiber-based piezoelectric sensor through epitaxial growing ZnO nano rods (NRs) on the surface of electrospun PVDF nanofibers, which enables the fiber-based physiological monitoring electronics (PME) of well flexibility and high gas permeability . Due to synergistic piezoelectric effect of the effectively deformed interlocked ZnO NRs and the uniformly orientated PVDF nanofibers with high electroactive phase, the sensitivities of PME in both pressing and bending modes have been greatly improved 6 times and 41 times than that of pure PVDF nanofibers respectively. On this basis, the designed PME can precisely detect the complexly subtle physiological signals of respiration, wrist pulse and muscle behavior. Moreover, a sensitive gait recognition system was successfully developed based on PME arrays. Therefore, this proposed fiber-based device provides an alternative strategy to monitor the human subtle physiological signals and demonstrates promising potential in the expanded application of healthcare and clinical diagnosis.
研究领域:
材料科学
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生物医学工程
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可穿戴设备
✓ 已验证
产品关联
DOI
高影响因子
ADVANCED FUNCTIONAL MATERIALS
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2024
·
IF 18.5
Tao Sun,Meng Qi,Qing-Xiu Li,Hang-Fei Li,Zhi-Peng Feng,Run-Ze Xu,You Zhou,Yu Wen,Gui-Jun Li,Ye Zhou,Su-Ting Han
Gait is among the most dependable, accurate, and secure methods of biometric identification. However, high power consumption and low computing capability are two major obstacles on wearable sensors-based gait recognition system. In this work, an integrated system is reported combining a triboelectric nanogenerator (TENG), a memristor (Ag/HfO x /Pt), and perovskite-based multicolor LEDs (PMCLED) for the visualization and recognition of foot patterns through signal-on-none and multi-wavelength on-device preprocessing. The flexible TENG acts as a sensory receptor, generating voltage based on the duration and intensity of pressure, which in turn promotes voltage-triggered synaptic plasticity in the memristor. The PMCLED, with its threshold switching and multi-wavelength emission characteristics, enables nonlinear filtering and amplification of the synaptic signal from the memristor, resulting in a simplified system design and reduced background noise. Additionally, the effectiveness of on-device preprocessing is validated based on a 5 × 5 array of integrated devices and software-built neural network for foot pattern visualization and recognition. The proposed system is able to recognize the on-device preprocessed images with high accuracy, indicating great potentials in both healthcare monitoring and human-machine interaction.
研究领域:
生物识别
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可穿戴技术
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纳米技术
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神经网络
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人机交互
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）