1
|
Xue X, Wu H, Cai Q, Chen M, Moon S, Huang Z, Kim T, Peng C, Feng W, Sharma N, Jiang X. Flexible Ultrasonic Transducers for Wearable Biomedical Applications: A Review on Advanced Materials, Structural Designs, and Future Prospects. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2024; 71:786-810. [PMID: 37971905 PMCID: PMC11292608 DOI: 10.1109/tuffc.2023.3333318] [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] [Indexed: 11/19/2023]
Abstract
Due to the rapid developments in materials science and fabrication techniques, wearable devices have recently received increased attention for biomedical applications, particularly in medical ultrasound (US) imaging, sensing, and therapy. US is ubiquitous in biomedical applications because of its noninvasive nature, nonionic radiating, high precision, and real-time capabilities. While conventional US transducers are rigid and bulky, flexible transducers can be conformed to curved body areas for continuous sensing without restricting tissue movement or transducer shifting. This article comprehensively reviews the application of flexible US transducers in the field of biomedical imaging, sensing, and therapy. First, we review the background of flexible US transducers. Following that, we discuss advanced materials and fabrication techniques for flexible US transducers and their enabling technology status. Finally, we highlight and summarize some promising preliminary data with recent applications of flexible US transducers in biomedical imaging, sensing, and therapy. We also provide technical barriers, challenges, and future perspectives for further research and development.
Collapse
|
2
|
Wang H, Zuo S, Cerezo-Sánchez M, Arekhloo NG, Nazarpour K, Heidari H. Wearable super-resolution muscle-machine interfacing. Front Neurosci 2022; 16:1020546. [PMID: 36466163 PMCID: PMC9714306 DOI: 10.3389/fnins.2022.1020546] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/21/2022] [Indexed: 09/19/2023] Open
Abstract
Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.
Collapse
Affiliation(s)
- Huxi Wang
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Siming Zuo
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - María Cerezo-Sánchez
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Negin Ghahremani Arekhloo
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Kianoush Nazarpour
- Neuranics Ltd., Glasgow, United Kingdom
- School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| |
Collapse
|
3
|
Zhang Q, Iyer A, Lambeth K, Kim K, Sharma N. Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation. SENSORS 2022; 22:s22010335. [PMID: 35009875 PMCID: PMC8749646 DOI: 10.3390/s22010335] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/10/2021] [Accepted: 12/31/2021] [Indexed: 12/02/2022]
Abstract
Functional electrical stimulation (FES) is a potential neurorehabilitative intervention to enable functional movements in persons with neurological conditions that cause mobility impairments. However, the quick onset of muscle fatigue during FES is a significant challenge for sustaining the desired functional movements for more extended periods. Therefore, a considerable interest still exists in the development of sensing techniques that reliably measure FES-induced muscle fatigue. This study proposes to use ultrasound (US) imaging-derived echogenicity signal as an indicator of FES-induced muscle fatigue. We hypothesized that the US-derived echogenicity signal is sensitive to FES-induced muscle fatigue under isometric and dynamic muscle contraction conditions. Eight non-disabled participants participated in the experiments, where FES electrodes were applied on their tibialis anterior (TA) muscles. During a fatigue protocol under either isometric and dynamic ankle dorsiflexion conditions, we synchronously collected the isometric dorsiflexion torque or dynamic dorsiflexion angle on the ankle joint, US echogenicity signals from TA muscle, and the applied stimulation intensity. The experimental results showed an exponential reduction in the US echogenicity relative change (ERC) as the fatigue progressed under the isometric (R2=0.891±0.081) and dynamic (R2=0.858±0.065) conditions. The experimental results also implied a strong linear relationship between US ERC and TA muscle fatigue benchmark (dorsiflexion torque or angle amplitude), with R2 values of 0.840±0.054 and 0.794±0.065 under isometric and dynamic conditions, respectively. The findings in this study indicate that the US echogenicity signal is a computationally efficient signal that strongly represents FES-induced muscle fatigue. Its potential real-time implementation to detect fatigue can facilitate an FES closed-loop controller design that considers the FES-induced muscle fatigue.
Collapse
Affiliation(s)
- Qiang Zhang
- UNC/NCSU Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, USA; (Q.Z.); (A.I.); (K.L.)
- UNC/NCSU Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Ashwin Iyer
- UNC/NCSU Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, USA; (Q.Z.); (A.I.); (K.L.)
- UNC/NCSU Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Krysten Lambeth
- UNC/NCSU Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, USA; (Q.Z.); (A.I.); (K.L.)
- UNC/NCSU Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kang Kim
- The Department of Bioengineering, School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- The Center for Ultrasound Molecular Imaging and Therapeutics, Department of Medicine and Heart and Vascular Institute, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- The Department of Mechanical Engineering and Materials Science, School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- The McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Nitin Sharma
- UNC/NCSU Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, USA; (Q.Z.); (A.I.); (K.L.)
- UNC/NCSU Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Correspondence: ; Tel.: +1-919-513-0787
| |
Collapse
|
4
|
Current Status and Advancement of Ultrasound Imaging Technologies in Musculoskeletal Studies. CURRENT PHYSICAL MEDICINE AND REHABILITATION REPORTS 2021. [DOI: 10.1007/s40141-021-00337-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
5
|
Zhang Q, Iyer A, Lambeth K, Kim K, Sharma N. Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5948-5952. [PMID: 34892473 DOI: 10.1109/embc46164.2021.9630325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The rapid onset of muscle fatigue during functional electrical stimulation (FES) is a major challenge when attempting to perform long-term periodic tasks such as walking. Surface electromyography (sEMG) is frequently used to detect muscle fatigue for both volitional and FES-evoked muscle contraction. However, sEMG contamination from both FES stimulation artifacts and residual M-wave signals requires sophisticated processing to get clean signals and evaluate the muscle fatigue level. The objective of this paper is to investigate the feasibility of computationally efficient ultrasound (US) echogenicity as a candidate indicator of FES-induced muscle fatigue. We conducted isometric and dynamic ankle dorsiflexion experiments with electrically stimulated tibialis anterior (TA) muscle on three human participants. During a fatigue protocol, we synchronously recorded isometric dorsiflexion force, dynamic dorsiflexion angle, US images, and stimulation intensity. The temporal US echogenicity from US images was calculated based on a gray-scaled analysis to assess the decrease in dorsiflexion force or motion range due to FES-induced TA muscle fatigue. The results showed a monotonic reduction in US echogenicity change along with the fatigue progression for both isometric (R2 =0.870±0.026) and dynamic (R2 =0.803±0.048) ankle dorsiflexion. These results implied a strong linear relationship between US echogenicity and TA muscle fatigue level. The findings indicate that US echogenicity may be a promising computationally efficient indicator for assessing FES-induced muscle fatigue and may aid in the design of muscle-in-the-loop FES controllers that consider the onset of muscle fatigue.
Collapse
|
6
|
Zhang Q, Iyer A, Sun Z, Kim K, Sharma N. A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1944-1954. [PMID: 34428143 DOI: 10.1109/tnsre.2021.3106900] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.
Collapse
|
7
|
Banse M, Chagnaud BP, Huby A, Parmentier E, Kéver L. Sound production in piranhas is associated with modifications of the spinal locomotor pattern. J Exp Biol 2021; 224:260574. [PMID: 33942099 PMCID: PMC8126449 DOI: 10.1242/jeb.242336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/10/2021] [Indexed: 11/20/2022]
Abstract
In piranhas, sounds are produced through the vibration of the swim bladder wall caused by the contraction of bilateral sonic muscles. Because they are solely innervated by spinal nerves, these muscles likely evolved from the locomotor hypaxial musculature. The transition from a neuromuscular system initially shaped for slow movements (locomotion) to a system that requires a high contraction rate (sound production) was accompanied with major peripheral structural modifications, yet the associated neural adjustments remain to this date unclear. To close this gap, we investigated the activity of both the locomotor and the sonic musculature using electromyography. The comparison between the activation patterns of both systems highlighted modifications of the neural motor pathway: (1) a transition from a bilateral alternating pattern to a synchronous activation pattern, (2) a switch from a slow- to a high-frequency regime, and (3) an increase in the synchrony of motor neuron activation. Furthermore, our results demonstrate that sound features correspond to the activity of the sonic muscles, as both the variation patterns of periods and amplitudes of sounds highly correspond to those seen in the sonic muscle electromyograms (EMGsonic). Assuming that the premotor network for sound production in piranhas is of spinal origin, our results show that the neural circuit associated with spinal motor neurons transitioned from the slow alternating pattern originally used for locomotion to a much faster simultaneous activation pattern to generate vocal signals.
Collapse
Affiliation(s)
- Marine Banse
- Laboratoire de Morphologie Fonctionnelle et Evolutive, Université de Liège, 4000 Liège, Belgium
| | - Boris P Chagnaud
- Department Biology II, Ludwig-Maximilians-University Munich, 82152 Planegg, Germany.,Institute for Biology, Karl-Franzens-University Graz, 8010 Graz, Austria
| | - Alessia Huby
- Laboratoire de Morphologie Fonctionnelle et Evolutive, Université de Liège, 4000 Liège, Belgium
| | - Eric Parmentier
- Laboratoire de Morphologie Fonctionnelle et Evolutive, Université de Liège, 4000 Liège, Belgium
| | - Loïc Kéver
- Laboratoire de Morphologie Fonctionnelle et Evolutive, Université de Liège, 4000 Liège, Belgium.,Department Biology II, Ludwig-Maximilians-University Munich, 82152 Planegg, Germany
| |
Collapse
|