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Boateng D, Li X, Zhu Y, Zhang H, Wu M, Liu J, Kang Y, Zeng H, Han L. Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception. Biosens Bioelectron 2024; 261:116499. [PMID: 38896981 DOI: 10.1016/j.bios.2024.116499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/21/2024]
Abstract
With the advent of flexible electronics and sensing technology, hydrogel-based flexible sensors have exhibited considerable potential across a diverse range of applications, including wearable electronics and soft robotics. Recently, advanced machine learning (ML) algorithms have been integrated into flexible hydrogel sensing technology to enhance their data processing capabilities and to achieve intelligent perception. However, there are no reviews specifically focusing on the data processing steps and analysis based on the raw sensing data obtained by flexible hydrogel sensors. Here we provide a comprehensive review of the latest advancements and breakthroughs in intelligent perception achieved through the fusion of ML algorithms with flexible hydrogel sensors, across various applications. Moreover, this review thoroughly examines the data processing techniques employed in flexible hydrogel sensors, offering valuable perspectives expected to drive future data-driven applications in this field.
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Affiliation(s)
- Derrick Boateng
- College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Xukai Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Yuhan Zhu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Hao Zhang
- School of Physics and Optoelectronic Engineering, Hainan University, Haikou, 570228, China.
| | - Meng Wu
- Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada
| | - Jifang Liu
- The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510700, China
| | - Yan Kang
- College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Hongbo Zeng
- Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada
| | - Linbo Han
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China.
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Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals. SENSORS 2021; 21:s21186064. [PMID: 34577270 PMCID: PMC8469046 DOI: 10.3390/s21186064] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/31/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).
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Wang X, Xiao Y, Deng F, Chen Y, Zhang H. Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM. BIOSENSORS 2021; 11:198. [PMID: 34208524 PMCID: PMC8234407 DOI: 10.3390/bios11060198] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 11/17/2022]
Abstract
To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user's forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young's modulus (286 KPa) and exceptional breathability (18 g m-2 h-1 of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.
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Affiliation(s)
| | | | - Fangming Deng
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China; (X.W.); (Y.X.); (Y.C.); (H.Z.)
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Davoudabadi MJ, Aminghafari M. A fuzzy-wavelet denoising technique with applications to noise reduction in audio signals. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-162329] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mohammad-Javad Davoudabadi
- Department of Statistics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Mina Aminghafari
- Department of Statistics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
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