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Biomimetics
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Biomimetics in Intelligent Sensor
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Biomimetics in Intelligent Sensor

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 5732

Special Issue Editors

Dr. Qian Wang

E-Mail Website
Guest Editor
Jiangsu Provincial Key Laboratory of Advanced Robotics, Soochow University, Suzhou 215021, China
Interests: bionic intelligent sensing; bio-inspired sensors; mechanical bionic
Dr. Daobing Chen

E-Mail Website
Guest Editor
The Institute of Technological Science, Wuhan University, South Donghu Road 8, Wuhan 430072, China
Interests: bio-inspired sensor-actuator; biomimetic self-sensing; bionic intelligent device; bionic sensor manufacturing

Special Issue Information

Dear Colleagues,

With the rapid advancement of technology, the integration of biomimetics and intelligent sensors is opening up rich possibilities for innovative research and technological applications. This Special Issue aims to delve into the application of biomimetics in the field of intelligent sensors, providing an exchange platform for researchers in academia and industry to collaboratively drive progress in this domain.

Bio-sensing technology, as a crucial component of biomimetics, offers new perspectives for the design and enhancement of intelligent sensors via simulation and the application of sensing mechanisms found in biological systems. One point of emphasis for this Special Issue is biomimetic sensor design, involving the creation of various sensor structures and principles inspired by nature to enhance adaptability and sensitivity to environmental changes.

In the realm of smart materials application, this SI seeks inspiration from natural materials to achieve more efficient and flexible sensor performance. The introduction of bio-inspired algorithms provides a means of optimizing the design of intelligent sensors by simulating mechanisms such as evolution and genetics from biological systems, enabling adaptive performance optimization.

Biosignal processing stands out as a key technology in this field, involving the conversion of biological signals into information with use for sensor systems. Simultaneously, research on sensor networks and biosensing is a focal point, aiming to enable the collaborative intelligent sensor research across various domains and broader applications.

This Issue will not only emphasize fundamental theoretical research but also intends to promote the practical application of intelligent sensors. In fields such as agriculture, healthcare, and environmental monitoring, the introduction of intelligent sensors offers novel approaches to the monitoring, analysis, and investigation problems. By exploring the application of intelligent sensors in different domains, this Issue aims to provide valuable  insights for researchers in related fields.

This Special Issue invites researchers and thinkers to actively participate in this field by submitting original research papers, sharing the latest achievements and breakthroughs related to biomimetics in intelligent sensors. We hope to stimulate greater reflection and innovation in the intersection of biomimetics and intelligent sensor research, driving technological advancements and contributing to solving societal and environmental issues.

Dr. Qian Wang
Dr. Daobing Chen
Guest Editors

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. Biomimetics 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 2200 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

  • intelligent sensors
  • bio-sensing technology
  • biomimetic sensor design
  • smart materials, bio-inspired algorithms
  • biosignal processing
  • sensor networks
  • biosensing
  • applications of intelligent sensors
  • bio-sensing manufacturing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

27 pages, 13890 KiB  
Article
A Fast Multi-Scale of Distributed Batch-Learning Growing Neural Gas for Multi-Camera 3D Environmental Map Building
by Chyan Zheng Siow, Azhar Aulia Saputra, Takenori Obo and Naoyuki Kubota
Biomimetics 2024, 9(9), 560; https://doi.org/10.3390/biomimetics9090560 - 16 Sep 2024
Viewed by 709
Abstract
Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but [...] Read more.
Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single RGB-D camera is not enough to perform the above tasks. Therefore, this study propose a 3D environmental map-building method integrating point cloud data measured via multiple RGB-D cameras. First, a fast multi-scale of distributed batch-learning growing neural gas (Fast MS-DBL-GNG) is proposed as a topological feature extraction method in order to reduce computational costs because a single RGB-D camera may output 1 million data. Next, random sample consensus (RANSAC) is applied to integrate two sets of point cloud data using topological features. In order to show the effectiveness of the proposed method, Fast MS-DBL-GNG is applied to perform topological mapping from several point cloud data sets measured in different directions with some overlapping areas included in two images. The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method. Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor)
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18 pages, 4321 KiB  
Article
Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber–Kalman and Autocorrelation Algorithms
by Yunlong Luo, Yang Yang, Yanbo Ma, Runhe Huang, Alex Qi, Muxin Ma and Yihong Qi
Biomimetics 2024, 9(8), 481; https://doi.org/10.3390/biomimetics9080481 - 9 Aug 2024
Viewed by 723
Abstract
Enhancing road safety by monitoring a driver’s physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, [...] Read more.
Enhancing road safety by monitoring a driver’s physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver’s seat. This article mainly describes how advanced signal processing methods, including the Huber–Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system’s effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments. Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor)
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12 pages, 2132 KiB  
Article
Effect of Various Carbon Electrodes on MIP-Based Sensing Proteins Using Poly(Scopoletin): A Case Study of Ferritin
by Aysu Yarman
Biomimetics 2024, 9(7), 426; https://doi.org/10.3390/biomimetics9070426 - 13 Jul 2024
Viewed by 926
Abstract
Sensitivity in the sub-nanomolar concentration region is required to determine important protein biomarkers, e.g., ferritin. As a prerequisite for high sensitivity, in this paper, the affinity of the functional monomer to the macromolecular target ferritin in solution was compared with the value for [...] Read more.
Sensitivity in the sub-nanomolar concentration region is required to determine important protein biomarkers, e.g., ferritin. As a prerequisite for high sensitivity, in this paper, the affinity of the functional monomer to the macromolecular target ferritin in solution was compared with the value for the respective molecularly imprinted polymer (MIP)-based electrodes, and the influence of various surface modifications of the electrode was investigated. The analytical performance of ferritin sensing was investigated using three different carbon electrodes (screen-printed carbon electrodes, single-walled-carbon-nanotube-modified screen-printed carbon electrodes, and glassy carbon electrodes) covered with a scopoletin-based MIP layer. Regardless of the electrode type, the template molecule ferritin was mixed with the functional monomer scopoletin, and electropolymerization was conducted using multistep amperometry. All stages of MIP preparation were followed by evaluating the diffusional permeability of the redox marker ferricyanide/ferrocyanide through the polymer layer by differential pulse voltammetry. The best results were obtained with glassy carbon electrodes. The MIP sensor responded up to 0.5 µM linearly with a Kd of 0.30 µM. Similar results were also obtained in solution upon the interaction of scopoletin and ferritin using fluorescence spectroscopy, resulting in the quenching of the scopoletin signal, with a calculated Kd of 0.81 µM. Moreover, the binding of 1 µM ferritin led to 49.6% suppression, whereas human serum albumin caused 8.6% suppression. Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor)
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22 pages, 7381 KiB  
Article
Can Plants Perceive Human Gestures? Using AI to Track Eurythmic Human–Plant Interaction
by Alvaro Francisco Gil, Moritz Weinbeer and Peter A. Gloor
Biomimetics 2024, 9(5), 290; https://doi.org/10.3390/biomimetics9050290 - 12 May 2024
Viewed by 1239
Abstract
This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in [...] Read more.
This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves. Using machine learning, we found that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems while also paving the way to use plants as biosensors for analyzing human movement. Full article
(This article belongs to the Special Issue Biomimetics in Intelligent Sensor)
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