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Olyanasab A, Annabestani M. Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review. J Pers Med 2024; 14:203. [PMID: 38392636 PMCID: PMC10890129 DOI: 10.3390/jpm14020203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/29/2024] [Accepted: 02/11/2024] [Indexed: 02/24/2024] Open
Abstract
This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer's physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual's physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.
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Affiliation(s)
- Ali Olyanasab
- Institute for Integrated Circuits, Johannes Kepler University Linz, 4040 Linz, Austria
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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Parashar A, Shekhawat RS, Ding W, Rida I. Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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Gait Analysis Accuracy Difference with Different Dimensions of Flexible Capacitance Sensors. SENSORS 2021; 21:s21165299. [PMID: 34450739 PMCID: PMC8401030 DOI: 10.3390/s21165299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/29/2021] [Accepted: 08/04/2021] [Indexed: 11/17/2022]
Abstract
Stroke causes neurological pathologies, including gait pathologies, which are diagnosed by gait analysis. However, existing gait analysis devices are difficult to use in situ or are disrupted by external conditions. To overcome these drawbacks, a flexible capacitance sensor was developed in this study. To date, a performance comparison of flexible sensors with different dimensions has not been carried out. The aim of this study was to provide optimized sensor dimension information for gait analysis. To accomplish this, sensors with seven different dimensions were fabricated. The dimensions of the sensors were based on the average body size and movement range of 20- to 59-year-old adults. The sensors were characterized by 100 oscillations. The minimum hysteresis error was 8%. After that, four subjects were equipped with the sensor and walked on a treadmill at a speed of 3.6 km/h. All walking processes were filmed at 50 fps and analyzed in Kinovea. The RMS error was calculated using the same frame rate of the video and the sampling rate of the signal from the sensor. The smallest RMS error between the sensor data and the ankle angle was 3.13° using the 49 × 8 mm sensor. In this study, we confirm the dimensions of the sensor with the highest gait analysis accuracy; therefore, the results can be used to make decisions regarding sensor dimensions.
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Kim M, Choi H, Cho KJ, Jo S. Single to Multi: Data-Driven High Resolution Calibration Method for Piezoresistive Sensor Array. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3070823] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Kim D, Kim SH, Kim T, Kang BB, Lee M, Park W, Ku S, Kim D, Kwon J, Lee H, Bae J, Park YL, Cho KJ, Jo S. Review of machine learning methods in soft robotics. PLoS One 2021; 16:e0246102. [PMID: 33600496 PMCID: PMC7891779 DOI: 10.1371/journal.pone.0246102] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.
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Affiliation(s)
- Daekyum Kim
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Neuro-Machine Augmented Intelligence Laboratory, School of Computing, KAIST, Daejeon, Korea
| | - Sang-Hun Kim
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Biorobotics Laboratory, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
| | - Taekyoung Kim
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
- Soft Robotics & Bionics Lab, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
| | - Brian Byunghyun Kang
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Biorobotics Laboratory, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
| | - Minhyuk Lee
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Bio-Robotics and Control Laboratory, Department of Mechanical Engineering, UNIST, Ulsan, Korea
| | - Wookeun Park
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Bio-Robotics and Control Laboratory, Department of Mechanical Engineering, UNIST, Ulsan, Korea
| | - Subyeong Ku
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
- Soft Robotics & Bionics Lab, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
| | - DongWook Kim
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
- Soft Robotics & Bionics Lab, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
| | - Junghan Kwon
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
- Soft Robotics & Bionics Lab, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
| | - Hochang Lee
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Neuro-Machine Augmented Intelligence Laboratory, School of Computing, KAIST, Daejeon, Korea
| | - Joonbum Bae
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Bio-Robotics and Control Laboratory, Department of Mechanical Engineering, UNIST, Ulsan, Korea
| | - Yong-Lae Park
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
- Soft Robotics & Bionics Lab, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
| | - Kyu-Jin Cho
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Biorobotics Laboratory, Department of Mechanical Engineering, Seoul National University, Seoul, Korea
- Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea
| | - Sungho Jo
- Soft Robotics Research Center, Seoul National University, Seoul, Korea
- Neuro-Machine Augmented Intelligence Laboratory, School of Computing, KAIST, Daejeon, Korea
- KAIST Institute for Artificial Intelligence, KAIST, Daejeon, Korea
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Eguchi R, Michael B, Howard M, Takahashi M. Shift-Adaptive Estimation of Joint Angle Using Instrumented Brace With Two Stretch Sensors Based on Gaussian Mixture Models. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3010486] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Chen Z, Wu R, Guo S, Liu X, Fu H, Jin X, Liao M. 3D Upper Body Reconstruction with Sparse Soft Sensors. Soft Robot 2020; 8:226-239. [PMID: 32668188 DOI: 10.1089/soro.2019.0187] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Three-dimensional (3D) reconstruction of human body has wide applications, for example, for customized design of clothes and digital avatar production. Existing vision-based systems for 3D body reconstruction require users to wear minimal or extreme-tight clothes in front of cameras, and thus suffer from privacy problems. In this work, we explore a novel solution based on a sparse number of soft sensors on a standard garment, and use it for capturing 3D upper body shape. We utilize the maximal stretching range by modeling the nonlinear performance profile for individual sensors. The body shape can be dynamically reconstructed by analyzing the relationship between mesh deformation and sensor reading, with a learning-based approach. The wearability and flexibility of our prototype allow its use in indoor/outdoor environments and for long-term breath monitoring. Our prototype has been extensively evaluated by multiple users with different body sizes and the same user for multiple days. The results show that our garment prototype is comfortable to wear, and achieves the state-of-the-art reconstruction performance with the advantages in privacy projection and application scenarios.
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Affiliation(s)
- Zhiyong Chen
- School of Informatics, Xiamen University, Xiamen, China
| | - Ronghui Wu
- Research Institution for Biomimetics and Soft Matter, College of Materials, College of Physical Science and Technology, Xiamen University, Xiamen, China
| | - Shihui Guo
- School of Informatics, Xiamen University, Xiamen, China
| | - Xiangyang Liu
- Research Institution for Biomimetics and Soft Matter, College of Materials, College of Physical Science and Technology, Xiamen University, Xiamen, China
| | - Hongbo Fu
- School of Creative Media, City University of Hong Kong, Kowloon, Hong Kong
| | - Xiaogang Jin
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China.,ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab, Hangzhou, China
| | - Minghong Liao
- School of Informatics, Xiamen University, Xiamen, China
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Chander H, Burch RF, Talegaonkar P, Saucier D, Luczak T, Ball JE, Turner A, Kodithuwakku Arachchige SNK, Carroll W, Smith BK, Knight A, Prabhu RK. Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17103554. [PMID: 32438649 PMCID: PMC7277680 DOI: 10.3390/ijerph17103554] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 05/15/2020] [Accepted: 05/16/2020] [Indexed: 11/16/2022]
Abstract
Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.
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Affiliation(s)
- Harish Chander
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
- Correspondence:
| | - Reuben F. Burch
- Department of Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA;
| | - Purva Talegaonkar
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (P.T.); (B.K.S.)
| | - David Saucier
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Tony Luczak
- National Strategic Planning and Analysis Research Center (NSPARC), Mississippi State University, Mississippi State, MS 39762, USA;
| | - John E. Ball
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Alana Turner
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
| | | | - Will Carroll
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Brian K. Smith
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (P.T.); (B.K.S.)
| | - Adam Knight
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
| | - Raj K. Prabhu
- Department of Agricultural and Biomedical Engineering, Mississippi State University, Mississippi State, MS 39762, USA;
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Park H, Cho J, Park J, Na Y, Kim J. Sim-To-Real Transfer Learning Approach for Tracking Multi-DOF Ankle Motions Using Soft Strain Sensors. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.2979631] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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