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Ershad F, Patel S, Yu C. Wearable bioelectronics fabricated in situ on skins. NPJ FLEXIBLE ELECTRONICS 2023; 7:32. [PMID: 38665149 PMCID: PMC11041641 DOI: 10.1038/s41528-023-00265-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 07/04/2023] [Indexed: 04/28/2024]
Abstract
In recent years, wearable bioelectronics has rapidly expanded for diagnosing, monitoring, and treating various pathological conditions from the skin surface. Although the devices are typically prefabricated as soft patches for general usage, there is a growing need for devices that are customized in situ to provide accurate data and precise treatment. In this perspective, the state-of-the-art in situ fabricated wearable bioelectronics are summarized, focusing primarily on Drawn-on-Skin (DoS) bioelectronics and other in situ fabrication methods. The advantages and limitations of these technologies are evaluated and potential future directions are suggested for the widespread adoption of these technologies in everyday life.
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Affiliation(s)
- Faheem Ershad
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16801 USA
| | - Shubham Patel
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16801 USA
| | - Cunjiang Yu
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16801 USA
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16801 USA
- Department of Materials Science and Engineering, Materials Research Institute, Pennsylvania State University, University Park, PA 16801 USA
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A Proposal of Bioinspired Soft Active Hand Prosthesis. Biomimetics (Basel) 2023; 8:biomimetics8010029. [PMID: 36648815 PMCID: PMC9844288 DOI: 10.3390/biomimetics8010029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023] Open
Abstract
Soft robotics have broken the rigid wall of interaction between humans and robots due to their own definition and manufacturing principles, allowing robotic systems to adapt to humans and enhance or restore their capabilities. In this research we propose a dexterous bioinspired soft active hand prosthesis based in the skeletal architecture of the human hand. The design includes the imitation of the musculoskeletal components and morphology of the human hand, allowing the prosthesis to emulate the biomechanical properties of the hand, which results in better grips and a natural design. CAD models for each of the bones were developed and 3D printing was used to manufacture the skeletal structure of the prosthesis, also soft materials were used for the musculoskeletal components. A myoelectric control system was developed using a recurrent neural network (RNN) to classify the hand gestures using electromyography signals; the RNN model achieved an accuracy of 87% during real time testing. Objects with different size, texture and shape were tested to validate the grasping performance of the prosthesis, showing good adaptability, soft grasping and mechanical compliance to object of the daily life.
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Ershad F, Houston M, Patel S, Contreras L, Koirala B, Lu Y, Rao Z, Liu Y, Dias N, Haces-Garcia A, Zhu W, Zhang Y, Yu C. Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays. PNAS NEXUS 2023; 2:pgac291. [PMID: 36712933 PMCID: PMC9837666 DOI: 10.1093/pnasnexus/pgac291] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/09/2022] [Indexed: 06/18/2023]
Abstract
Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.
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Affiliation(s)
- Faheem Ershad
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16801, USA
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Michael Houston
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Shubham Patel
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Luis Contreras
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Bikram Koirala
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
| | - Yuntao Lu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Materials Science and Engineering Program, University of Houston, Houston, TX, 77204, USA
| | - Zhoulyu Rao
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Materials Science and Engineering Program, University of Houston, Houston, TX, 77204, USA
| | - Yang Liu
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Nicholas Dias
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Arturo Haces-Garcia
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204, USA
| | - Weihang Zhu
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
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Rapid Detection of Cardiac Pathologies by Neural Networks Using ECG Signals (1D) and sECG Images (3D). COMPUTATION 2022. [DOI: 10.3390/computation10070112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Usually, cardiac pathologies are detected using one-dimensional electrocardiogram signals or two-dimensional images. When working with electrocardiogram signals, they can be represented in the time and frequency domains (one-dimensional signals). However, this technique can present difficulties, such as the high cost of private health services or the time the public health system takes to refer the patient to a cardiologist. In addition, the variety of cardiac pathologies (more than 20 types) is a problem in diagnosing the disease. On the other hand, surface electrocardiography (sECG) is a little-explored technique for this diagnosis. sECGs are three-dimensional images (two dimensions in space and one in time). In this way, the signals were taken in one-dimensional format and analyzed using neural networks. Following the transformation of the one-dimensional signals to three-dimensional signals, they were analyzed in the same sense. For this research, two models based on LSTM and ResNet34 neural networks were developed, which showed high accuracy, 98.71% and 93.64%, respectively. This study aims to propose the basis for developing Decision Support Software (DSS) based on machine learning models.
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Kwon YT, Kim YS, Kwon S, Mahmood M, Lim HR, Park SW, Kang SO, Choi JJ, Herbert R, Jang YC, Choa YH, Yeo WH. All-printed nanomembrane wireless bioelectronics using a biocompatible solderable graphene for multimodal human-machine interfaces. Nat Commun 2020; 11:3450. [PMID: 32651424 PMCID: PMC7351733 DOI: 10.1038/s41467-020-17288-0] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 06/19/2020] [Indexed: 12/22/2022] Open
Abstract
Recent advances in nanomaterials and nano-microfabrication have enabled the development of flexible wearable electronics. However, existing manufacturing methods still rely on a multi-step, error-prone complex process that requires a costly cleanroom facility. Here, we report a new class of additive nanomanufacturing of functional materials that enables a wireless, multilayered, seamlessly interconnected, and flexible hybrid electronic system. All-printed electronics, incorporating machine learning, offers multi-class and versatile human-machine interfaces. One of the key technological advancements is the use of a functionalized conductive graphene with enhanced biocompatibility, anti-oxidation, and solderability, which allows a wireless flexible circuit. The high-aspect ratio graphene offers gel-free, high-fidelity recording of muscle activities. The performance of the printed electronics is demonstrated by using real-time control of external systems via electromyograms. Anatomical study with deep learning-embedded electrophysiology mapping allows for an optimal selection of three channels to capture all finger motions with an accuracy of about 99% for seven classes. Though wearable electronics remain an attractive technology for bioelectronics, fabrication methods that precisely print biocompatible materials for electronics are needed. Here, the authors report an additive manufacturing process that yields all-printed nanomaterial-based wireless electronics.
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Affiliation(s)
- Young-Tae Kwon
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Yun-Soung Kim
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Shinjae Kwon
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Musa Mahmood
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hyo-Ryoung Lim
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Si-Woo Park
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan, 15588, South Korea
| | - Sung-Oong Kang
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan, 15588, South Korea
| | - Jeongmoon J Choi
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Robert Herbert
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Young C Jang
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.,Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Yong-Ho Choa
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan, 15588, South Korea
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA. .,Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA. .,Neural Engineering Center, Flexible and Wearable Electronics Advanced Research, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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