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Micromachines
Special Issues
Wearable and Implantable Bio-MEMS Devices and Applications
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Wearable and Implantable Bio-MEMS Devices and Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "B：Biology and Biomedicine".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 13957

Special Issue Editor

Dr. Bowen Ji

E-Mail Website
Guest Editor
Unmanned System Research Institue, Northwestern Polytechnical University, Xi’an 710072, China
Interests: flexible electronics; implantable devices; bio- and medical MEMS devices

Special Issue Information

Dear Colleagues,

Wearable and implantable MEMS sensors and actuators have attracted tremendous attention in health monitoring, disease treatment, human–machine interaction, etc. due to their flexibility, minimization, low power consumption, and biocompatibility. A variety of devices have been developed in the last decade regarding the general aspects of advanced materials, device design, manufacturing and packaging methods, as well as system integration.

This Special Issue focuses on the recent development and solution of wearable and implantable Bio-MEMS devices from the aspects of optimized design methods, micromachining technology, and microsystem integration on any potential application. Specifically, the optimized design method includes the application of new biocompatible materials, theoretical calculation, and simulation; the micromachining technology focuses on the development of MEMS fabrication of devices based on either silicon or non-silicon materials to achieve better performance; microsystem integration should consider the packages, connection, miniaturization, failure modes, etc. In brief, wearable and implantable bio- and medical MEMS devices are expected to revolutionize personalized healthcare monitoring and precision therapy, and thus, continued efforts to develop wearable and implantable devices and systems hold great promise for the quality of people’s lives. Any other topics related to bio- and medical MEMS are also welcome.

Dr. Bowen Ji
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable devices and systems
  • implantable devices and systems
  • design and simulation for bio- and medical MEMS devices
  • manufacturing for bio- and medical MEMS devices
  • bio- and medical MEMS applications
  • other bio- and medical MEMS 

Published Papers (6 papers)

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Research

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14 pages, 3640 KiB  
Article
A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection
by Zekai Liang, Xuanqi Wang, Jun Guo, Yuanming Ye, Haoyang Zhang, Liang Xie, Kai Tao, Wen Zeng, Erwei Yin and Bowen Ji
Micromachines 2023, 14(5), 1085; https://doi.org/10.3390/mi14051085 - 21 May 2023
Cited by 1 | Viewed by 1709
Abstract
The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human–computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not [...] Read more.
The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human–computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not in line with daily wearing habits, such as the arms, legs, and face. In addition, some systems rely on wired connections, which impacts their flexibility and user-friendliness. This paper presents a novel wrist-worn system with four sEMG acquisition channels and a high common-mode rejection ratio (CMRR) greater than 120 dB. The circuit has an overall gain of 2492 V/V and a bandwidth of 15~500 Hz. It is fabricated using flexible circuit technologies and is encapsulated in a soft skin-friendly silicone gel. The system acquires sEMG signals at a sampling rate of over 2000 Hz with a 16-bit resolution and transmits data to a smart device via low-power Bluetooth. Muscle fatigue detection and four-class gesture recognition experiments (accuracy greater than 95%) were conducted to validate its practicality. The system has potential applications in natural and intuitive human–computer interaction and physiological state monitoring. Full article
(This article belongs to the Special Issue Wearable and Implantable Bio-MEMS Devices and Applications)
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16 pages, 6615 KiB  
Article
A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals
by Shengyu Wang, Bowen Ji, Dian Shao, Wanru Chen and Kunpeng Gao
Micromachines 2023, 14(5), 976; https://doi.org/10.3390/mi14050976 - 29 Apr 2023
Cited by 2 | Viewed by 1785
Abstract
In this paper, we propose a classification algorithm of EEG signal based on canonical correlation analysis (CCA) and integrated with adaptive filtering. It can enhance the detection of steady-state visual evoked potentials (SSVEPs) in a brain–computer interface (BCI) speller. An adaptive filter is [...] Read more.
In this paper, we propose a classification algorithm of EEG signal based on canonical correlation analysis (CCA) and integrated with adaptive filtering. It can enhance the detection of steady-state visual evoked potentials (SSVEPs) in a brain–computer interface (BCI) speller. An adaptive filter is employed in front of the CCA algorithm to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. The ensemble method is developed to integrate recursive least squares (RLS) adaptive filter corresponding to multiple stimulation frequencies. The method is tested by the SSVEP signal recorded from six targets by actual experiment and the EEG in a public SSVEP dataset of 40 targets from Tsinghua University. The accuracy rates of the CCA method and the CCA-based integrated RLS filter algorithm (RLS-CCA method) are compared. Experiment results show that the proposed RLS-CCA-based method significantly improves the classification accuracy compared with the pure CCA method. Especially when the number of EEG leads is low (three occipital electrodes and five non occipital electrodes), its advantage is more significant, and accuracy reaches 91.23%, which is more suitable for wearable environments where high-density EEG is not easy to collect. Full article
(This article belongs to the Special Issue Wearable and Implantable Bio-MEMS Devices and Applications)
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22 pages, 3039 KiB  
Article
MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
by Huihui Wang, Bo Ru, Xin Miao, Qin Gao, Masood Habib, Long Liu and Sen Qiu
Micromachines 2023, 14(5), 947; https://doi.org/10.3390/mi14050947 - 27 Apr 2023
Cited by 8 | Viewed by 2302
Abstract
Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and [...] Read more.
Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset. Full article
(This article belongs to the Special Issue Wearable and Implantable Bio-MEMS Devices and Applications)
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12 pages, 4365 KiB  
Article
Localized Surface Hydrophilicity Tailoring of Polyimide Film for Flexible Electronics Manufacturing Using an Atmospheric Pressure Ar/H2O Microplasma Jet
by Bowen Ji, Tao Wang, Meng Li, Liping Shi, Xiaoli You, Fanqi Sun and Haiwen Luan
Micromachines 2022, 13(11), 1853; https://doi.org/10.3390/mi13111853 - 29 Oct 2022
Cited by 1 | Viewed by 1617
Abstract
The poor hydrophilicity of polyimide (PI) films limits their applications in flexible electronics, such as in wearable and implantable bio-MEMS devices. In this paper, an atmospheric pressure Ar/H2O microplasma jet (μAPPJ) with a nozzle diameter of 100 μm was utilized to [...] Read more.
The poor hydrophilicity of polyimide (PI) films limits their applications in flexible electronics, such as in wearable and implantable bio-MEMS devices. In this paper, an atmospheric pressure Ar/H2O microplasma jet (μAPPJ) with a nozzle diameter of 100 μm was utilized to site-selectively tune the surface hydrophilicity of a PI film. The electrical and optical characteristics of the μAPPJ were firstly investigated, and the results showed that multi-spikes occurred during the plasma discharge and that diverse reactive species, such as O atoms and OH radicals, were generated in the plasma plume. The physical and chemical properties of pristine and microplasma-modified PI surfaces were characterized by the water contact angle (WCA), atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). The wettability of the PI surface was significantly enhanced after microplasma modification, and the WCA could be adjusted by varying the applied voltage, water vapor content, plasma treatment time and storage time. The AFM images indicated that the surface roughness increased after the plasma treatment, which partially contributed to an improvement in the surface hydrophilicity. The XPS results showed a reduction in the C content and an increase in the O content, and abundant hydrophilic polar oxygen-containing functional groups were also grafted onto the PI film surface. Finally, the interaction mechanism between the PI molecular chains and the microplasma is discussed. The breaking of C-N and C-O bonds and the grafting of OH radicals were the key pathways to dominate the reaction process. Full article
(This article belongs to the Special Issue Wearable and Implantable Bio-MEMS Devices and Applications)
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12 pages, 3798 KiB  
Article
A Low-Cost Flexible Perforated Respiratory Sensor Based on Platinum for Continuous Respiratory Monitoring
by Lu Cao, Zhitong Zhang, Junshi Li, Zhongyan Wang, Yingjie Ren, Qining Wang, Dong Huang and Zhihong Li
Micromachines 2022, 13(10), 1743; https://doi.org/10.3390/mi13101743 - 14 Oct 2022
Cited by 6 | Viewed by 1925
Abstract
Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing [...] Read more.
Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing system consists of two perforated temperature sensors, signal conditioning circuits, a transmission module, and a supporting analysis algorithm. The perforated structure effectively enhances the sensitivity of the system and shortens the response time. The sensor’s response time is 0.07 s in air and sensitivity is 1.4‰°C−1. The device can achieve a monitoring respiratory temperature range between normal room temperature and 40 °C. The simple and standard micromachining process ensures low cost and high reproducibility. We achieved the monitoring of different breathing patterns, such as normal breathing, panting, and apnea, which can be applied to sleep breath monitoring and exercise information recording. Full article
(This article belongs to the Special Issue Wearable and Implantable Bio-MEMS Devices and Applications)
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Review

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24 pages, 6471 KiB  
Review
Epidermal Wearable Biosensors for the Continuous Monitoring of Biomarkers of Chronic Disease in Interstitial Fluid
by Xichen Yuan, Oumaima Ouaskioud, Xu Yin, Chen Li, Pengyi Ma, Yang Yang, Peng-Fei Yang, Li Xie and Li Ren
Micromachines 2023, 14(7), 1452; https://doi.org/10.3390/mi14071452 - 20 Jul 2023
Cited by 5 | Viewed by 3680
Abstract
Healthcare technology has allowed individuals to monitor and track various physiological and biological parameters. With the growing trend of the use of the internet of things and big data, wearable biosensors have shown great potential in gaining access to the human body, and [...] Read more.
Healthcare technology has allowed individuals to monitor and track various physiological and biological parameters. With the growing trend of the use of the internet of things and big data, wearable biosensors have shown great potential in gaining access to the human body, and providing additional functionality to analyze physiological and biochemical information, which has led to a better personalized and more efficient healthcare. In this review, we summarize the biomarkers in interstitial fluid, introduce and explain the extraction methods for interstitial fluid, and discuss the application of epidermal wearable biosensors for the continuous monitoring of markers in clinical biology. In addition, the current needs, development prospects and challenges are briefly discussed. Full article
(This article belongs to the Special Issue Wearable and Implantable Bio-MEMS Devices and Applications)
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