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Koelewijn AD, Audu M, del-Ama AJ, Colucci A, Font-Llagunes JM, Gogeascoechea A, Hnat SK, Makowski N, Moreno JC, Nandor M, Quinn R, Reichenbach M, Reyes RD, Sartori M, Soekadar S, Triolo RJ, Vermehren M, Wenger C, Yavuz US, Fey D, Beckerle P. Adaptation Strategies for Personalized Gait Neuroprosthetics. Front Neurorobot 2021; 15:750519. [PMID: 34975445 PMCID: PMC8716811 DOI: 10.3389/fnbot.2021.750519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.
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Affiliation(s)
- Anne D. Koelewijn
- Biomechanical Data Analysis and Creation (BIOMAC) Group, Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Musa Audu
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Antonio J. del-Ama
- Applied Mathematics, Materials Science and Technology and Electronic Technology Department, Rey Juan Carlos University, Mostoles, Spain
| | - Annalisa Colucci
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Antonio Gogeascoechea
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Sandra K. Hnat
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Nathan Makowski
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center, Cleveland, OH, United States
| | - Juan C. Moreno
- Neural Rehabilitation Group, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
| | - Mark Nandor
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Roger Quinn
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Marc Reichenbach
- Chair of Computer Engineering, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ryan-David Reyes
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Massimo Sartori
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Surjo Soekadar
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Ronald J. Triolo
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mareike Vermehren
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Christian Wenger
- IHP-Leibniz Institut Fuer Innovative Mikroelektronik, Frankfurt (Oder), Germany
| | - Utku S. Yavuz
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Dietmar Fey
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Beckerle
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Naeem J, Hamzaid NA, Azman AW, Bijak M. Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study. ACTA ACUST UNITED AC 2021; 65:461-468. [PMID: 32304295 DOI: 10.1515/bmt-2019-0191] [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: 07/25/2019] [Accepted: 01/07/2020] [Indexed: 11/15/2022]
Abstract
Functional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.
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Affiliation(s)
- Jannatul Naeem
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Nur Azah Hamzaid
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Amelia Wong Azman
- Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
| | - Manfred Bijak
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
- Medical University Vienna, Center for Medical Physics and Biomedical Engineering, Vienna, Austria
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Uwamahoro R, Sundaraj K, Subramaniam ID. Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review. Biomed Eng Online 2021; 20:1. [PMID: 33390158 PMCID: PMC7780389 DOI: 10.1186/s12938-020-00840-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 12/11/2020] [Indexed: 11/10/2022] Open
Abstract
This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.
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Affiliation(s)
- Raphael Uwamahoro
- Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Tunggal, Malaysia
- Regional Centre of Excellence in Biomedical Engineering and E-Health, University of Rwanda, PO BOX 4285, Kigali, Rwanda
| | - Kenneth Sundaraj
- Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Tunggal, Malaysia.
| | - Indra Devi Subramaniam
- Pusat Bahasa & Pembangunan Insan, Universiti Teknikal Malaysia Melaka, Tunggal, Malaysia
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Rinaldin CDP, Cabral LPA, Krueger E, Nogueira-Neto GN, Nohama P, Scheeren EM. Fatigue in complete spinal cord injury and implications on total delay. Artif Organs 2019; 44:305-313. [PMID: 31553061 DOI: 10.1111/aor.13573] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 09/13/2019] [Accepted: 09/16/2019] [Indexed: 12/11/2022]
Abstract
The use of neuromuscular electrical stimulation (NMES) to artificially restore movement in people with complete spinal cord injury (SCI) induces an accelerated process of muscle fatigue. Fatigue increases the time between the beginning of NMES and the onset of muscle force (DelayTOT ). Understanding how much muscle fatigue affects the DelayTOT in people with SCI could help in the design of closed-loop neuroprostheses that compensate for this delay, thus making the control system more stable. The aim of this study was to evaluate the impact of the extent of fatigue on DelayTOT and peak force of the lower limbs in people with complete SCI. Fifteen men-young adults with complete SCI (paraplegia and tetraplegia) and stable health-participated in the experiment. DelayTOT was defined as the time interval between the beginning of NMES application until the onset of muscle force. The electrical intensity of NMES applied was adjusted individually and consisted of the amplitude required to obtain a full extension of the knee (0°), considering the maximum electrically stimulated extension (MESE). Subsequently, 70% of the MESE was applied during the fatigue induction protocol. Significant differences were identified between the moments before and after the fatigue protocol, both for peak force (P ≤ .026) and DelayTOT (P ≤ .001). The medians and interquartile range of the DelayTOT were higher in postfatigue (199.0 ms) when compared to the moment before fatigue (146.5 ms). The medians and interquartile range of the peak force were higher in unfatigued lower limbs (0.43 kgf) when compared to the moment postfatigue (0.27 kgf). The results support the hypothesis that muscle fatigue influences the increase in DelayTOT and decrease in force production in people with SCI. For future applications, the combined evaluation of the delay and force in SCI patients provides valuable feedback for NMES paradigms. The study will provide potentially critical muscle mechanical evidence for the investigation of the evolution of atrophy.
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Affiliation(s)
| | - Luciane Patrícia Adreani Cabral
- Human Motricity Laboratory/PPGTS, Pontifical Catholic University of Paraná (PUCPR), Curitiba, Brazil.,Regional University Hospital of Campos Gerais, Campus of Ponta Grossa State University, Ponta Grossa, Brazil
| | - Eddy Krueger
- Neural Engineering and Rehabilitation Laboratory, Master and Doctoral Program in Rehabilitation Sciences UEL-UNOPAR, Anatomy Department, State University of Londrina, Londrina, Brazil.,Rehabilitation Engineering Laboratory/CPGEI/PPGEB, Federal Technological University of Paraná (UTFPR), Curitiba, Brazil
| | - Guilherme N Nogueira-Neto
- Rehabilitation Engineering Laboratory/PPGTS, Pontifical Catholic University of Paraná (PUCPR), Curitiba, Brazil
| | - Percy Nohama
- Rehabilitation Engineering Laboratory/CPGEI/PPGEB, Federal Technological University of Paraná (UTFPR), Curitiba, Brazil.,Rehabilitation Engineering Laboratory/PPGTS, Pontifical Catholic University of Paraná (PUCPR), Curitiba, Brazil
| | - Eduardo M Scheeren
- Human Motricity Laboratory/PPGTS, Pontifical Catholic University of Paraná (PUCPR), Curitiba, Brazil
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Meglič A, Uršič M, Škorjanc A, Đorđević S, Belušič G. The Piezo-resistive MC Sensor is a Fast and Accurate Sensor for the Measurement of Mechanical Muscle Activity. SENSORS 2019; 19:s19092108. [PMID: 31067754 PMCID: PMC6539344 DOI: 10.3390/s19092108] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 04/19/2019] [Accepted: 04/25/2019] [Indexed: 11/16/2022]
Abstract
A piezo-resistive muscle contraction (MC) sensor was used to assess the contractile properties of seven human skeletal muscles (vastus medialis, rectus femoris, vastus lateralis, gastrocnemius medialis, biceps femoris, erector spinae) during electrically stimulated isometric contraction. The sensor was affixed to the skin directly above the muscle centre. The length of the adjustable sensor tip (3, 4.5 and 6 mm) determined the depth of the tip in the tissue and thus the initial pressure on the skin, fatty and muscle tissue. The depth of the tip increased the signal amplitude and slightly sped up the time course of the signal by shortening the delay time. The MC sensor readings were compared to tensiomyographic (TMG) measurements. The signals obtained by MC only partially matched the TMG measurements, largely due to the faster response time of the MC sensor.
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Affiliation(s)
- Andrej Meglič
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia.
| | - Mojca Uršič
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia.
| | - Aleš Škorjanc
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia.
| | - Srđan Đorđević
- TMG-BMC Ltd., Štihova ulica 24, 1000 Ljubljana, Slovenia.
| | - Gregor Belušič
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia.
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