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Dong Y, Guo W, Yang Y, Li Q. The Effect of Phonomyography Prototype for Intraoperative Neuromuscular Monitoring: A Preliminary Study. Bioengineering (Basel) 2024; 11:486. [PMID: 38790354 PMCID: PMC11117765 DOI: 10.3390/bioengineering11050486] [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: 04/07/2024] [Revised: 04/29/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Quantitative neuromuscular monitoring, as extolled by clinical guidelines, is advocated to circumvent the complications associated with neuromuscular blockers (NMBs), such as residual neuromuscular block (rNMB). Nonetheless, the worldwide utilization of such methods remains undesirable. Phonomyography (PMG) boasts the advantages of convenience, stability, and multi-muscle recording which may be a promising monitoring method. The purpose of this preliminary study is conducting a feasibility analysis and an effectiveness evaluation of a PMG prototype under general anesthesia. A prospective observational preliminary study was conducted. Twenty-five adults who had undergone none-cardiac elective surgery were enrolled. The PMG prototype and TOF-Watch SX simultaneously recorded the pharmacodynamic properties of single bolus rocuronium at the ipsilateral adductor pollicis for each patient. For the primary outcome, the time duration to 0.9 TOF ratio of the two devices reached no statistical significance (p > 0.05). For secondary outcomes, the multi-temporal neuromuscular-monitoring measurements between the two devices also reached no statistical significance (p > 0.05). What is more, both the Spearman's and Pearson's correlation tests revealed a strong correlation across all monitoring periods between the PMG prototype and TOF-Watch SX. Additionally, Bland-Altman plots demonstrated a good agreement between the two devices. Thus, the PMG prototype was a feasible, secure, and effective neuromuscular-monitoring technique during general anesthesia and was interchangeable with TOF-Watch SX.
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Affiliation(s)
- Yanjie Dong
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, China;
| | - Weichao Guo
- Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Yi Yang
- Department of Anesthesiology, Sichuan Provincial People’s Hospital, Chengdu 610072, China;
| | - Qian Li
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, China;
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2
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Jarque-Bou NJ, Vergara M, Sancho-Bru JL. Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1505-1514. [PMID: 38551830 DOI: 10.1109/tnsre.2024.3383156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone's intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices.
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3
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Skoraczynski DJ, Chen C. Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:1379. [PMID: 38474915 DOI: 10.3390/s24051379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 03/14/2024]
Abstract
This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.
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Affiliation(s)
- Dariusz J Skoraczynski
- Laboratory of Motion Generation and Analysis (LMGA), Monash University, Clayton, VIC 3800, Australia
| | - Chao Chen
- Laboratory of Motion Generation and Analysis (LMGA), Monash University, Clayton, VIC 3800, Australia
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4
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Yang Y, Wang Q, Wei Q. Muscle redistribution technique for expressing motion intention in patients with wrist-level amputation. J Hand Surg Eur Vol 2024; 49:100-102. [PMID: 37684019 DOI: 10.1177/17531934231196438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
This study reports the preliminary results of a technique for redistributing muscles at the wrist in the stump of hand amputees by suturing the tendons to the dermis. The technique has the potential to improve control of hand prostheses by detecting movement intentions.
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Affiliation(s)
- Yong Yang
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Qining Wang
- Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, China
| | - Qipei Wei
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
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Papaleo ED, D'Alonzo M, Fiori F, Piombino V, Falato E, Pilato F, De Liso A, Di Lazzaro V, Di Pino G. Integration of proprioception in upper limb prostheses through non-invasive strategies: a review. J Neuroeng Rehabil 2023; 20:118. [PMID: 37689701 PMCID: PMC10493033 DOI: 10.1186/s12984-023-01242-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/24/2023] [Indexed: 09/11/2023] Open
Abstract
Proprioception plays a key role in moving our body dexterously and effortlessly. Nevertheless, the majority of investigations evaluating the benefits of providing supplemental feedback to prosthetics users focus on delivering touch restitution. These studies evaluate the influence of touch sensation in an attempt to improve the controllability of current robotic devices. Contrarily, investigations evaluating the capabilities of proprioceptive supplemental feedback have yet to be comprehensively analyzed to the same extent, marking a major gap in knowledge within the current research climate. The non-invasive strategies employed so far to restitute proprioception are reviewed in this work. In the absence of a clearly superior strategy, approaches employing vibrotactile, electrotactile and skin-stretch stimulation achieved better and more consistent results, considering both kinesthetic and grip force information, compared with other strategies or any incidental feedback. Although emulating the richness of the physiological sensory return through artificial feedback is the primary hurdle, measuring its effects to eventually support the integration of cumbersome and energy intensive hardware into commercial prosthetic devices could represent an even greater challenge. Thus, we analyze the strengths and limitations of previous studies and discuss the possible benefits of coupling objective measures, like neurophysiological parameters, as well as measures of prosthesis embodiment and cognitive load with behavioral measures of performance. Such insights aim to provide additional and collateral outcomes to be considered in the experimental design of future investigations of proprioception restitution that could, in the end, allow researchers to gain a more detailed understanding of possibly similar behavioral results and, thus, support one strategy over another.
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Affiliation(s)
- Ermanno Donato Papaleo
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Via Álvaro Del Portillo 21, 00128, Rome, Italy
| | - Marco D'Alonzo
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Via Álvaro Del Portillo 21, 00128, Rome, Italy
| | - Francesca Fiori
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Via Álvaro Del Portillo 21, 00128, Rome, Italy
| | - Valeria Piombino
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Via Álvaro Del Portillo 21, 00128, Rome, Italy
| | - Emma Falato
- Research Unit of Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Fabio Pilato
- Research Unit of Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Alfredo De Liso
- Research Unit of Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Giovanni Di Pino
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Via Álvaro Del Portillo 21, 00128, Rome, Italy.
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Jiang N, Chen C, He J, Meng J, Pan L, Su S, Zhu X. Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review. Natl Sci Rev 2023; 10:nwad048. [PMID: 37056442 PMCID: PMC10089583 DOI: 10.1093/nsr/nwad048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/01/2023] [Accepted: 02/07/2023] [Indexed: 04/05/2023] Open
Abstract
ABSTRACT
A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed that four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discuss three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review 10 years ago: deep learning methods, surface electromyogram decomposition and open-source databases. To conclude the review, we provide an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.
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Affiliation(s)
| | - Chen Chen
- State Key Laboratory of Mechanical System and Vibration, and Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiayuan He
- National Clinical Research Center for Geriatrics, West China Hospital, and Med-X Center for Manufacturing, Sichuan University, Chengdu 610041, China
| | - Jianjun Meng
- State Key Laboratory of Mechanical System and Vibration, and Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lizhi Pan
- Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China
| | - Shiyong Su
- Institute of Neuroscience, Université Catholique Louvain, Brussel B-1348, Belgium
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, and Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
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7
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Han Y, Lu Y, Zuo Y, Song H, Chou CH, Wang X, Li X, Li L, Niu CM, Hou W. Substitutive proprioception feedback of a prosthetic wrist by electrotactile stimulation. Front Neurosci 2023; 17:1135687. [PMID: 36895418 PMCID: PMC9989268 DOI: 10.3389/fnins.2023.1135687] [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: 01/01/2023] [Accepted: 02/01/2023] [Indexed: 02/25/2023] Open
Abstract
Objective Sensory feedback of upper-limb prostheses is widely desired and studied. As important components of proprioception, position, and movement feedback help users to control prostheses better. Among various feedback methods, electrotactile stimulation is a potential method for coding proprioceptive information of a prosthesis. This study was motivated by the need for proprioception information for a prosthetic wrist. The flexion-extension (FE) position and movement information of the prosthetic wrist are transmitted back to the human body through multichannel electrotactile stimulation. Approach We developed an electrotactile scheme to encode the FE position and movement of the prosthetic wrist and designed an integrated experimental platform. A preliminary experiment on the sensory threshold and discomfort threshold was performed. Then, two proprioceptive feedback experiments were performed: a position sense experiment (Exp 1) and a movement sense experiment (Exp 2). Each experiment included a learning session and a test session. The success rate (SR) and discrimination reaction time (DRT) were analyzed to evaluate the recognition effect. The acceptance of the electrotactile scheme was evaluated by a questionnaire. Main results Our results showed that the average position SRs of five able-bodied subjects, amputee 1, and amputee 2 were 83.78, 97.78, and 84.44%, respectively. The average movement SR, and the direction and range SR of wrist movement in five able-bodied subjects were 76.25, 96.67%, respectively. Amputee 1 and amputee 2 had movement SRs of 87.78 and 90.00% and direction and range SRs of 64.58 and 77.08%, respectively. The average DRT of five able-bodied subjects was less than 1.5 s and that of amputees was less than 3.5 s. Conclusion The results indicate that after a short period of learning, the subjects can sense the position and movement of wrist FE. The proposed substitutive scheme has the potential for amputees to sense a prosthetic wrist, thus enhancing the human-machine interaction.
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Affiliation(s)
- Yichen Han
- Biomedical Engineering Department, Bioengineering College, Chongqing University, Chongqing, China
| | - Yinping Lu
- Biomedical Engineering Department, Bioengineering College, Chongqing University, Chongqing, China
| | - Yufeng Zuo
- Biomedical Engineering Department, Bioengineering College, Chongqing University, Chongqing, China
| | - Hongliang Song
- Biomedical Engineering Department, Bioengineering College, Chongqing University, Chongqing, China
| | - Chih-Hong Chou
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xing Wang
- Biomedical Engineering Department, Bioengineering College, Chongqing University, Chongqing, China
| | - Xiangxin Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, Guangdong, China
| | - Lei Li
- Department of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, China
| | - Chuanxin M Niu
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wensheng Hou
- Biomedical Engineering Department, Bioengineering College, Chongqing University, Chongqing, China
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8
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de Oliveira J, de Souza MA, Assef AA, Maia JM. Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:9232. [PMID: 36501933 PMCID: PMC9740760 DOI: 10.3390/s22239232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
The study of muscle contractions generated by the muscle-tendon unit (MTU) plays a critical role in medical diagnoses, monitoring, rehabilitation, and functional assessments, including the potential for movement prediction modeling used for prosthetic control. Over the last decade, the use of combined traditional techniques to quantify information about the muscle condition that is correlated to neuromuscular electrical activation and the generation of muscle force and vibration has grown. The purpose of this review is to guide the reader to relevant works in different applications of ultrasound imaging in combination with other techniques for the characterization of biological signals. Several research groups have been using multi-sensing systems to carry out specific studies in the health area. We can divide these studies into two categories: human-machine interface (HMI), in which sensors are used to capture critical information to control computerized prostheses and/or robotic actuators, and physiological study, where sensors are used to investigate a hypothesis and/or a clinical diagnosis. In addition, the relevance, challenges, and expectations for future work are discussed.
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Affiliation(s)
- Jonathan de Oliveira
- Graduate Program in Health Technology (PPGTS), Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil
| | - Mauren Abreu de Souza
- Graduate Program in Health Technology (PPGTS), Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil
| | - Amauri Amorin Assef
- Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology of Paraná (UTFPR), Curitiba 80230-901, Brazil
| | - Joaquim Miguel Maia
- Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology of Paraná (UTFPR), Curitiba 80230-901, Brazil
- Electronics Engineering Department (DAELN), Federal University of Technology of Paraná (UTFPR), Curitiba 80230-901, Brazil
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9
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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.
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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
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10
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Moradi A, Rafiei H, Daliri M, Akbarzadeh-T MR, Akbarzadeh A, Naddaf-Sh AM, Naddaf-Sh S. Clinical implementation of a bionic hand controlled with kineticomyographic signals. Sci Rep 2022; 12:14805. [PMID: 36045214 PMCID: PMC9433417 DOI: 10.1038/s41598-022-19128-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/24/2022] [Indexed: 11/23/2022] Open
Abstract
Sensing the proper signal could be a vital piece of the solution to the much evading attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive movement. Towards this end, magnetics tags have been recently suggested as an alternative sensing mechanism to the more common EMG signals. Such sensing technology, however, is inherently invasive and hence only in simulation stages of magnet localization to date. Here, for the first time, we report on the clinical implementation of implanted magnetic tags for an amputee's prosthetic hand from both the medical and engineering perspectives. Specifically, the proposed approach introduces a flexor-extensor tendon transfer surgical procedure to implant the tags, artificial neural networks to extract human intention directly from the implanted magnet's magnetic fields -in short KineticoMyoGraphy (KMG) signals- rather than localizing them, and a game strategy to examine the proposed algorithms and rehabilitate the patient with his new prosthetic hand. The bionic hand's ability is then tested following the patient's intended gesture type and grade. The statistical results confirm the possible utility of surgically implanted magnetic tags as an accurate sensing interface for recognizing the intended gesture and degree of movement between an amputee and his bionic hand.
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Affiliation(s)
- Ali Moradi
- Orthopedic Research Center, Ghaem Hospital, Mashhad University of Medical Sciences, Azadi Sq., Mashhad, 91388-13944, Iran
| | - Hamed Rafiei
- Department of Electrical Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Azadi Sq., Mashhad, 9177948974, Iran
| | - Mahla Daliri
- Orthopedic Research Center, Ghaem Hospital, Mashhad University of Medical Sciences, Azadi Sq., Mashhad, 91388-13944, Iran
| | - Mohammad-R Akbarzadeh-T
- Department of Electrical Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Azadi Sq., Mashhad, 9177948974, Iran.
| | - Alireza Akbarzadeh
- Department of Mechanical Engineering, FUM Center of Advanced Rehabilitation and Robotics Research (FUM CARE) and Center of Excllence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Azadi Sq., Mashhad, 9177948974, Iran
| | - Amir-M Naddaf-Sh
- Department of Electrical Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Azadi Sq., Mashhad, 9177948974, Iran
| | - Sadra Naddaf-Sh
- Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Azadi Sq., Mashhad, 9177948974, Iran
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11
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Bao T, Xie SQ, Yang P, Zhou P, Zhang ZQ. Towards Robust, Adaptive and Reliable Upper-limb Motion Estimation Using Machine Learning and Deep Learning--A Survey in Myoelectric Control. IEEE J Biomed Health Inform 2022; 26:3822-3835. [PMID: 35294368 DOI: 10.1109/jbhi.2022.3159792] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
To develop multi-functional human-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. \textcolor{red}{Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.
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12
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Esposito D, Centracchio J, Andreozzi E, Gargiulo GD, Naik GR, Bifulco P. Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey. SENSORS 2021; 21:s21206863. [PMID: 34696076 PMCID: PMC8540117 DOI: 10.3390/s21206863] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/30/2021] [Accepted: 10/12/2021] [Indexed: 12/03/2022]
Abstract
As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.
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Affiliation(s)
- Daniele Esposito
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia;
- The MARCS Institute, Western Sydney University, Penrith, NSW 2751, Australia
| | - Ganesh R. Naik
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia;
- The Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA 5042, Australia
- Correspondence:
| | - Paolo Bifulco
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
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Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration. SENSORS 2021; 21:s21041081. [PMID: 33557373 PMCID: PMC7914874 DOI: 10.3390/s21041081] [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: 12/05/2020] [Revised: 01/25/2021] [Accepted: 02/01/2021] [Indexed: 01/14/2023]
Abstract
Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.
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