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Li C, Delgado-Gómez D, Sujar A, Wang P, Martin-Moratinos M, Bella-Fernández M, Masó-Besga AE, Peñuelas-Calvo I, Ardoy-Cuadros J, Hernández-Liebo P, Blasco-Fontecilla H. Assessment of ADHD Subtypes Using Motion Tracking Recognition Based on Stroop Color-Word Tests. SENSORS (BASEL, SWITZERLAND) 2024; 24:323. [PMID: 38257416 PMCID: PMC10818498 DOI: 10.3390/s24020323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/27/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder known for its significant heterogeneity and varied symptom presentation. Describing the different subtypes as predominantly inattentive (ADHD-I), combined (ADHD-C), and hyperactive-impulsive (ADHD-H) relies primarily on clinical observations, which can be subjective. To address the need for more objective diagnostic methods, this pilot study implemented a Microsoft Kinect-based Stroop Color-Word Test (KSWCT) with the objective of investigating the potential differences in executive function and motor control between different subtypes in a group of children and adolescents with ADHD. A series of linear mixture modeling were used to encompass the performance accuracy, reaction times, and extraneous movements during the tests. Our findings suggested that age plays a critical role, and older subjects showed improvements in KSWCT performance; however, no significant divergence in activity level between the subtypes (ADHD-I and ADHD-H/C) was established. Patients with ADHD-H/C showed tendencies toward deficits in motor planning and executive control, exhibited by shorter reaction times for incorrect responses and more difficulty suppressing erroneous responses. This study provides preliminary evidence of unique executive characteristics among ADHD subtypes, advances our understanding of the heterogeneity of the disorder, and lays the foundation for the development of refined and objective diagnostic tools for ADHD.
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Affiliation(s)
- Chao Li
- Faculty of Medicine, Autonomous University of Madrid, 28029 Madrid, Spain
- Department of Psychiatry, Puerta de Hierro University Hospital, 28222 Majadahonda, Spain
| | - David Delgado-Gómez
- Department of Statistics, University Carlos III of Madrid, 28903 Getafe, Spain
| | - Aaron Sujar
- School of Computer Engineering, University Rey Juan Carlos, 28933 Madrid, Spain
| | - Ping Wang
- Faculty of Medicine, Autonomous University of Madrid, 28029 Madrid, Spain
- Department of Psychiatry, Puerta de Hierro University Hospital, 28222 Majadahonda, Spain
| | - Marina Martin-Moratinos
- Faculty of Medicine, Autonomous University of Madrid, 28029 Madrid, Spain
- Department of Psychiatry, Puerta de Hierro University Hospital, 28222 Majadahonda, Spain
| | - Marcos Bella-Fernández
- Department of Psychiatry, Puerta de Hierro University Hospital, 28222 Majadahonda, Spain
- Department of Psychology, Comillas Pontifical University, 28015 Madrid, Spain
- Department of Psychology, Autonomous University of Madrid, 28029 Madrid, Spain
| | | | - Inmaculada Peñuelas-Calvo
- Department of Child and Adolescent Psychiatry, University Hospital 12 de Octubre, 28041 Madrid, Spain
| | - Juan Ardoy-Cuadros
- Health Sciences College, Rey Juan Carlos University, 28933 Madrid, Spain
| | - Paula Hernández-Liebo
- Department of Psychiatry, Marqués de Valdecilla University Hospital, University of Cantabria, 39008 Santander, Spain
| | - Hilario Blasco-Fontecilla
- Center of Biomedical Network Research on Mental Health (CIBERSAM), 28029 Madrid, Spain
- UNIR-ITEI & Health Sciences School, International University of La Rioja, 26006 Logroño, Spain
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Parupelli SK, Desai S. The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects. Bioengineering (Basel) 2023; 11:32. [PMID: 38247910 PMCID: PMC10813523 DOI: 10.3390/bioengineering11010032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain.
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Affiliation(s)
- Santosh Kumar Parupelli
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA;
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
| | - Salil Desai
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA;
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
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Mishra A, Agrawal M, Ali A, Garg P. Uninterrupted real-time cerebral stress level monitoring using wearable biosensors: A review. Biotechnol Appl Biochem 2023; 70:1895-1914. [PMID: 37455443 DOI: 10.1002/bab.2491] [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: 01/31/2023] [Accepted: 06/15/2023] [Indexed: 07/18/2023]
Abstract
Stress is the major unseen bug for the health of humans with the increasing workaholic era. Long periods of avoidance are the main precursor for chronic disorders that are quite tough to treat. As precaution is better than cure, stress detection and monitoring are vital. Although there are ways to measure stress clinically, there is still a constant need and demand for methods that measure stress personally and in an ex vitro manner for the convenience of the user. The concept of continuous stress monitoring has been introduced to tackle the issue of unseen stress accumulating in the body simultaneously with being user-friendly and reliable. Stress biosensors nowadays provide real-time, noninvasive, and continuous monitoring of stress. These biosensors are innovative anthropogenic creations that are a combination of biomarkers and indicators like heart rate variation, electrodermal activity, skin temperature, galvanic skin response, and electroencephalograph of stress in the body along with machine learning algorithms and techniques. The collaboration of biological markers, artificial intelligence techniques, and data science tools makes stress biosensors a hot topic for research. These attributes have made continuous stress detection a possibility with ease. The advancement in stress biosensing technologies has made a great impact on the lives of human beings so far. This article focuses on the comprehensive study of stress-indicating biomarkers and the techniques along with principles of the biosensors used for continuous stress detection. The precise overview of wearable stress monitoring systems is also sectioned to pave a pathway for possible future research studies.
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Affiliation(s)
- Anuja Mishra
- Department of Biotechnology, Institute of Applied Science & Humanities, GLA University, Mathura, Uttar Pradesh, India
| | - Mukti Agrawal
- Department of Biotechnology, Institute of Applied Science & Humanities, GLA University, Mathura, Uttar Pradesh, India
| | - Aaliya Ali
- School of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, India
- Center for Omics and Biodiversity Research, Shoolini University, Solan, Himachal Pradesh, India
| | - Prakrati Garg
- School of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, India
- Center for Omics and Biodiversity Research, Shoolini University, Solan, Himachal Pradesh, India
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Lin CT, Wang Y, Chen SF, Huang KC, Liao LD. Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission. Med Biol Eng Comput 2023; 61:3003-3019. [PMID: 37563528 DOI: 10.1007/s11517-023-02879-y] [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: 04/06/2023] [Accepted: 06/25/2023] [Indexed: 08/12/2023]
Abstract
Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.
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Affiliation(s)
- Chin-Teng Lin
- Human-centric AI Centre (HAI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
- Australia Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
- Brain Science and Technology Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
| | - Yuhling Wang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan
- Department of Electrical Engineering, National United University, Miaoli, Taiwan
| | - Sheng-Fu Chen
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan
| | - Kuan-Chih Huang
- Brain Science and Technology Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Institute of Electrical Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Lun-De Liao
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan.
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Fan YL, Hsu FR, Wang Y, Liao LD. Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization. Med Biol Eng Comput 2023; 61:2797-2814. [PMID: 37558927 DOI: 10.1007/s11517-023-02903-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.
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Affiliation(s)
- Yi-Ling Fan
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan
| | - Fang-Rong Hsu
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan
| | - Yuhling Wang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan
- Department of Electrical Engineering, National United University, 2, Lien-Da, Nan-Shih Li, Miaoli, 360302, Taiwan
| | - Lun-De Liao
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan.
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Li S, Li H, Lu Y, Zhou M, Jiang S, Du X, Guo C. Advanced Textile-Based Wearable Biosensors for Healthcare Monitoring. BIOSENSORS 2023; 13:909. [PMID: 37887102 PMCID: PMC10605256 DOI: 10.3390/bios13100909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 10/28/2023]
Abstract
With the innovation of wearable technology and the rapid development of biosensors, wearable biosensors based on flexible textile materials have become a hot topic. Such textile-based wearable biosensors promote the development of health monitoring, motion detection and medical management, and they have become an important support tool for human healthcare monitoring. Textile-based wearable biosensors not only non-invasively monitor various physiological indicators of the human body in real time, but they also provide accurate feedback of individual health information. This review examines the recent research progress of fabric-based wearable biosensors. Moreover, materials, detection principles and fabrication methods for textile-based wearable biosensors are introduced. In addition, the applications of biosensors in monitoring vital signs and detecting body fluids are also presented. Finally, we also discuss several challenges faced by textile-based wearable biosensors and the direction of future development.
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Affiliation(s)
- Sheng Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
- CCZU-ARK Institute of Carbon Materials, Nanjing 210012, China
| | - Huan Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Yongcai Lu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Minhao Zhou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Xiaosong Du
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Chang Guo
- CCZU-ARK Institute of Carbon Materials, Nanjing 210012, China
- School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
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