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Article ; Online: Skin-Interfaced Wearable Sweat Sensors for Precision Medicine.

Min, Jihong / Tu, Jiaobing / Xu, Changhao / Lukas, Heather / Shin, Soyoung / Yang, Yiran / Solomon, Samuel A / Mukasa, Daniel / Gao, Wei

Chemical reviews

2023 Volume 123, Issue 8, Page(s) 5049–5138

Abstract: Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the ... ...

Abstract	Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.
MeSH term(s)	Biosensing Techniques ; Electronics ; Monitoring, Physiologic ; Precision Medicine ; Sweat ; Wearable Electronic Devices ; Skin
Language	English
Publishing date	2023-03-27
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	207949-5
ISSN	1520-6890 ; 0009-2665
ISSN (online)	1520-6890
ISSN	0009-2665
DOI	10.1021/acs.chemrev.2c00823
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Article ; Online: A wearable aptamer nanobiosensor for non-invasive female hormone monitoring.

Ye, Cui / Wang, Minqiang / Min, Jihong / Tay, Roland Yingjie / Lukas, Heather / Sempionatto, Juliane R / Li, Jiahong / Xu, Changhao / Gao, Wei

Nature nanotechnology

2023 Volume 19, Issue 3, Page(s) 330–337

Abstract: Personalized monitoring of female hormones (for example, oestradiol) is of great interest in fertility and women's health. However, existing approaches usually require invasive blood draws and/or bulky analytical laboratory equipment, making them hard to ...

Abstract	Personalized monitoring of female hormones (for example, oestradiol) is of great interest in fertility and women's health. However, existing approaches usually require invasive blood draws and/or bulky analytical laboratory equipment, making them hard to implement at home. Here we report a skin-interfaced wearable aptamer nanobiosensor based on target-induced strand displacement for automatic and non-invasive monitoring of oestradiol via in situ sweat analysis. The reagentless, amplification-free and 'signal-on' detection approach coupled with a gold nanoparticle-MXene-based detection electrode offers extraordinary sensitivity with an ultra-low limit of detection of 0.14 pM. This fully integrated system is capable of autonomous sweat induction at rest via iontophoresis, precise microfluidic sweat sampling controlled via capillary bursting valves, real-time oestradiol analysis and calibration with simultaneously collected multivariate information (that is, temperature, pH and ionic strength), as well as signal processing and wireless communication with a user interface (for example, smartphone). We validated the technology in human participants. Our data indicate a cyclical fluctuation in sweat oestradiol during menstrual cycles, and a high correlation between sweat and blood oestradiol was identified. Our study opens up the potential for wearable sensors for non-invasive, personalized reproductive hormone monitoring.
MeSH term(s)	Humans ; Female ; Wearable Electronic Devices ; Gold ; Metal Nanoparticles ; Skin ; Estradiol ; Biosensing Techniques
Chemical Substances	Gold (7440-57-5) ; Estradiol (4TI98Z838E)
Language	English
Publishing date	2023-09-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2254964-X
ISSN	1748-3395 ; 1748-3387
ISSN (online)	1748-3395
ISSN	1748-3387
DOI	10.1038/s41565-023-01513-0
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Article ; Online: A Computationally Assisted Approach for Designing Wearable Biosensors toward Non-Invasive Personalized Molecular Analysis.

Mukasa, Daniel / Wang, Minqiang / Min, Jihong / Yang, Yiran / Solomon, Samuel A / Han, Hong / Ye, Cui / Gao, Wei

Advanced materials (Deerfield Beach, Fla.)

2023 Volume 35, Issue 35, Page(s) e2212161

Abstract: Wearable sweat sensors have the potential to revolutionize precision medicine as they can non-invasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot ... ...

Abstract	Wearable sweat sensors have the potential to revolutionize precision medicine as they can non-invasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven't yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Here, QuantumDock is introduced, an automated computational framework for universal MIP development toward wearable applications. QuantumDock utilizes density functional theory to probe molecular interactions between monomers and the target/interferent molecules to optimize selectivity, a fundamentally limiting factor for MIP development toward wearable sensing. A molecular docking approach is employed to explore a wide range of known and unknown monomers, and to identify the optimal monomer/cross-linker choice for subsequent MIP fabrication. Using an essential amino acid phenylalanine as the exemplar, experimental validation of QuantumDock is performed successfully using solution-synthesized MIP nanoparticles coupled with ultraviolet-visible spectroscopy. Moreover, a QuantumDock-optimized graphene-based wearable device is designed that can perform autonomous sweat induction, sampling, and sensing. For the first time, wearable non-invasive phenylalanine monitoring is demonstrated in human subjects toward personalized healthcare applications.
MeSH term(s)	Humans ; Molecular Docking Simulation ; Biosensing Techniques/methods ; Wearable Electronic Devices ; Sweat/chemistry ; Graphite/chemistry
Chemical Substances	Graphite (7782-42-5)
Language	English
Publishing date	2023-07-01
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1474949-X
ISSN	1521-4095 ; 0935-9648
ISSN (online)	1521-4095
ISSN	0935-9648
DOI	10.1002/adma.202212161
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Book ; Online: ScaTE

Seo, Junwon / Kim, Taekyung / Kwak, Kiho / Min, Jihong / Shim, Inwook

A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments

2022

Abstract: For the safe and successful navigation of autonomous vehicles in unstructured environments, the traversability of terrain should vary based on the driving capabilities of the vehicles. Actual driving experience can be utilized in a self-supervised ... ...

Abstract	For the safe and successful navigation of autonomous vehicles in unstructured environments, the traversability of terrain should vary based on the driving capabilities of the vehicles. Actual driving experience can be utilized in a self-supervised fashion to learn vehicle-specific traversability. However, existing methods for learning self-supervised traversability are not highly scalable for learning the traversability of various vehicles. In this work, we introduce a scalable framework for learning self-supervised traversability, which can learn the traversability directly from vehicle-terrain interaction without any human supervision. We train a neural network that predicts the proprioceptive experience that a vehicle would undergo from 3D point clouds. Using a novel PU learning method, the network simultaneously identifies non-traversable regions where estimations can be overconfident. With driving data of various vehicles gathered from simulation and the real world, we show that our framework is capable of learning the self-supervised traversability of various vehicles. By integrating our framework with a model predictive controller, we demonstrate that estimated traversability results in effective navigation that enables distinct maneuvers based on the driving characteristics of the vehicles. In addition, experimental results validate the ability of our method to identify and avoid non-traversable regions. Comment: Accepted to IEEE Robotics and Automation Letters (and IROS 2023). Our video can be found at https://youtu.be/ZZHfD-8OpBg
Keywords	Computer Science - Robotics ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
Subject code	629
Publishing date	2022-09-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: SLiDE

Bae, Gwangtak / Kim, Byungjun / Ahn, Seongyong / Min, Jihong / Shim, Inwook

Self-supervised LiDAR De-snowing through Reconstruction Difficulty

2022

Abstract: LiDAR is widely used to capture accurate 3D outdoor scene structures. However, LiDAR produces many undesirable noise points in snowy weather, which hamper analyzing meaningful 3D scene structures. Semantic segmentation with snow labels would be a ... ...

Abstract	LiDAR is widely used to capture accurate 3D outdoor scene structures. However, LiDAR produces many undesirable noise points in snowy weather, which hamper analyzing meaningful 3D scene structures. Semantic segmentation with snow labels would be a straightforward solution for removing them, but it requires laborious point-wise annotation. To address this problem, we propose a novel self-supervised learning framework for snow points removal in LiDAR point clouds. Our method exploits the structural characteristic of the noise points: low spatial correlation with their neighbors. Our method consists of two deep neural networks: Point Reconstruction Network (PR-Net) reconstructs each point from its neighbors; Reconstruction Difficulty Network (RD-Net) predicts point-wise difficulty of the reconstruction by PR-Net, which we call reconstruction difficulty. With simple post-processing, our method effectively detects snow points without any label. Our method achieves the state-of-the-art performance among label-free approaches and is comparable to the fully-supervised method. Moreover, we demonstrate that our method can be exploited as a pretext task to improve label-efficiency of supervised training of de-snowing. Comment: ECCV 2022
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006 ; 004
Publishing date	2022-08-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Wearable and Implantable Electronics: Moving toward Precision Therapy.

Song, Yu / Min, Jihong / Gao, Wei

ACS nano

2019 Volume 13, Issue 11, Page(s) 12280–12286

Abstract: Soft wearable and implantable electronic systems have attracted tremendous attention due to their flexibility, conformability, and biocompatibility. Such favorable features are critical for reliably monitoring key biomedical and physiological information ...

Abstract	Soft wearable and implantable electronic systems have attracted tremendous attention due to their flexibility, conformability, and biocompatibility. Such favorable features are critical for reliably monitoring key biomedical and physiological information (including both biophysical and biochemical signals) and effective treatment and management of specific chronic diseases. Miniaturized, fully integrated self-powered bioelectronic devices that can harvest energy from the human body represent promising and emerging solutions for long-term, intimate, and personalized therapies. In this Perspective, we offer a brief overview of recent advances in wearable/implantable soft electronic devices and their therapeutic applications ranging from drug delivery to tissue regeneration. We also discuss the key opportunities, challenges, and future directions in this important area needed to fulfill the vision of personalized medicine.
MeSH term(s)	Equipment Design ; Humans ; Monitoring, Physiologic/instrumentation ; Precision Medicine/instrumentation ; Prostheses and Implants ; Wearable Electronic Devices
Language	English
Publishing date	2019-11-14
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ISSN	1936-086X
ISSN (online)	1936-086X
DOI	10.1021/acsnano.9b08323
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Article ; Online: A physicochemical-sensing electronic skin for stress response monitoring.

Xu, Changhao / Song, Yu / Sempionatto, Juliane R / Solomon, Samuel A / Yu, You / Nyein, Hnin Y Y / Tay, Roland Yingjie / Li, Jiahong / Heng, Wenzheng / Min, Jihong / Lao, Alison / Hsiai, Tzung K / Sumner, Jennifer A / Gao, Wei

Nature electronics

2024 Volume 7, Issue 2, Page(s) 168–179

Abstract: Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the ... ...

Abstract	Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the nervous, endocrine, and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of over 100 hours with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0%, and quantify psychological stress responses with a confidence level of 98.7%.
Language	English
Publishing date	2024-01-19
Publishing country	England
Document type	Journal Article
ISSN	2520-1131
ISSN (online)	2520-1131
DOI	10.1038/s41928-023-01116-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: All-printed soft human-machine interface for robotic physicochemical sensing.

Yu, You / Li, Jiahong / Solomon, Samuel A / Min, Jihong / Tu, Jiaobing / Guo, Wei / Xu, Changhao / Song, Yu / Gao, Wei

Science robotics

2022 Volume 7, Issue 67, Page(s) eabn0495

Abstract: Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily ... ...

Abstract	Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.
MeSH term(s)	Artificial Intelligence ; COVID-19 ; Humans ; Robotic Surgical Procedures ; SARS-CoV-2 ; Wearable Electronic Devices
Language	English
Publishing date	2022-06-01
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2470-9476
ISSN (online)	2470-9476
DOI	10.1126/scirobotics.abn0495
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Article: The market valuation of pre-registration for firms in the online gaming industry

Min, Jihong / Oh, Yun Kyung

The journal of applied business research Vol. 31, No. 5 , p. 1789-1798

2015 Volume 31, Issue 5, Page(s) 1789–1798

Author's details	Jihong Min, (Myongji University, South Korea), Yun Kyung Oh, (Dongduk Women’s University, South Korea)
Keywords	Marketing-Finance Interface ; Marketing Campaign ; Pre-registration ; Online Game ; Event Study
Language	English
Publisher	CIBER Research Inst.
Publishing place	Littleton, Colo.
Document type	Article
ZDB-ID	1107555-7
ISSN	0892-7626
Database	ECONomics Information System

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Article: The mediating role of popularity rank on the relationship between advertising and in-app purchase sales in mobile application market

Oh, Yun Kyung / Min, Jihong

The journal of applied business research Vol. 31, No. 4 , p. 1311-1322

2015 Volume 31, Issue 4, Page(s) 1311–1322

Author's details	Yun Kyung Oh; Jihong Min
Keywords	Mobile Application ; Mobile Advertising ; Popularity Rank ; In-app Purchase
Language	English
Size	graph. Darst.
Publisher	CIBER Research Inst.
Publishing place	Littleton, Colo.
Document type	Article
ZDB-ID	1107555-7
ISSN	0892-7626
Database	ECONomics Information System

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