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Figueroa-Taiba P, Álvarez-Ruf J, Ulloa P, Bruna-Melo T, Espinoza-Maraboli L, Burgos PI, Mariman JJ. Potentiation of Motor Adaptation Via Cerebellar tACS: Characterization of the Stimulation Frequency. CEREBELLUM (LONDON, ENGLAND) 2024:10.1007/s12311-024-01748-0. [PMID: 39433720 DOI: 10.1007/s12311-024-01748-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/23/2024] [Indexed: 10/23/2024]
Abstract
Motor adaptation is critical to update motor tasks in new or modified environmental conditions. While the cerebellum supports error-based adaptations, its neural implementation is partially known. By controlling the frequency of cerebellar transcranial alternating current stimulation (c-tACS), we can test the influence of neural oscillation from the cerebellum for motor adaptation. Two independent experiments were conducted. In Experiment 1, 16 participants received four c-tACS protocols (45 Hz, 50 Hz, 55 Hz, and sham) on four different days while they practiced a visuomotor adaptation task (30 degrees CCW) with variable intensity (within-subject design). In Experiment 2, 45 participants separated into three groups received the effect of 45 Hz, 55 Hz c-tACS, and sham, respectively (between-subject design), performing the same visuomotor task with a fixed intensity (0.9 mA). In Experiment 1, 45 Hz and 50 Hz of c-tACS accelerated motor adaptation when participants performed the task only for the first time, independent of the time interval between sessions or the stimulation intensity. The effect of active c-tACS was ratified in Experiment 2, where 45 Hz c-tACS benefits motor adaptation during the complete practice period. Reaction time, velocity, or duration of reaching are not affected by c-tACS. Cerebellar alternating current stimulation is an effective strategy to potentiate visuomotor adaptations. Frequency-dependent effects on the gamma band, especially for 45 Hz c-tACS, ratify the oscillatory profile of cerebellar processes behind the motor adaptation. This can be exploited in future interventions to enhance motor learning.
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Affiliation(s)
- Paulo Figueroa-Taiba
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
- Laboratorio de Biomecánica Clínica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Carrera de Kinesiología, Santiago, Chile
| | - Joel Álvarez-Ruf
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
- Laboratorio de Biomecánica Clínica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Carrera de Kinesiología, Santiago, Chile
| | - Paulette Ulloa
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
| | - Trinidad Bruna-Melo
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
- Fisioterapia en Movimiento, Grupo de investigación Multiespecialidad (PTinMOTION), Departamento de Fisioterapia, Facultad de Fisioterapia, Universidad de Valencia, Valencia, 46010, España
- Departamento de Kinesiología, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Liam Espinoza-Maraboli
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
| | - Pablo Ignacio Burgos
- Departamento de Kinesiología, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Laboratorio de Neurorrehabilitación y control motor, Departamento de Neurociencias, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Balance Disorder Lab, Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, OP-32, Portland, OR, 97239, USA
| | - Juan J Mariman
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile.
- Departamento de Kinesiología, Facultad de Medicina, Universidad de Chile, Santiago, Chile.
- Núcleo de Bienestar y Desarrollo Humano, Centro de Investigación en Educación (CIE- UMCE), Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile.
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Caillet AH, Phillips ATM, Modenese L, Farina D. NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo. J Electromyogr Kinesiol 2024; 76:102873. [PMID: 38518426 DOI: 10.1016/j.jelekin.2024.102873] [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] [Indexed: 03/24/2024] Open
Abstract
The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.
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Affiliation(s)
| | - Andrew T M Phillips
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.
| | - Dario Farina
- Department of Bioengineering, Imperial College London, UK.
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3
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Caillet AH, Phillips ATM, Farina D, Modenese L. Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy. PLoS Comput Biol 2023; 19:e1011606. [PMID: 38060619 PMCID: PMC10729998 DOI: 10.1371/journal.pcbi.1011606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/19/2023] [Accepted: 10/16/2023] [Indexed: 12/20/2023] Open
Abstract
The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The motoneuronal control provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decodes the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.
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Affiliation(s)
- Arnault H. Caillet
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Andrew T. M. Phillips
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Luca Modenese
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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Babcock CD, Volk VL, Zeng W, Hamilton LD, Shelburne KB, Fitzpatrick CK. Neural-driven activation of 3D muscle within a finite element framework: exploring applications in healthy and neurodegenerative simulations. Comput Methods Biomech Biomed Engin 2023:1-11. [PMID: 37966863 PMCID: PMC11093887 DOI: 10.1080/10255842.2023.2280772] [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: 09/14/2023] [Accepted: 11/02/2023] [Indexed: 11/16/2023]
Abstract
This paper presents a novel computational framework for neural-driven finite element muscle models, with an application to amyotrophic lateral sclerosis (ALS). The multiscale neuromusculoskeletal (NMS) model incorporates physiologically accurate motor neurons, 3D muscle geometry, and muscle fiber recruitment. It successfully predicts healthy muscle force and tendon elongation and demonstrates a progressive decline in muscle force due to ALS, dropping from 203 N (healthy) to 155 N (120 days after ALS onset). This approach represents a preliminary step towards developing integrated neural and musculoskeletal simulations to enhance our understanding of neurodegenerative and neurodevelopmental conditions through predictive NMS models.
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Affiliation(s)
- Colton D. Babcock
- Mechanical and Biomedical Engineering, Boise State University, Boise, ID
| | - Victoria L. Volk
- Mechanical and Biomedical Engineering, Boise State University, Boise, ID
| | - Wei Zeng
- Department of Mechanical Engineering, New York Institute of Technology, New York, NY
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Gogeascoechea A, Ornelas-Kobayashi R, Yavuz US, Sartori M. Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4040-4050. [PMID: 37756177 DOI: 10.1109/tnsre.2023.3319959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (HD-EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion. We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.
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Bersani A, Davico G, Viceconti M. Modeling Human Suboptimal Control: A Review. J Appl Biomech 2023; 39:294-303. [PMID: 37586711 DOI: 10.1123/jab.2023-0015] [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: 01/16/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 08/18/2023]
Abstract
This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.
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Affiliation(s)
- Alex Bersani
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy
| | - Giorgio Davico
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy
| | - Marco Viceconti
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna,Italy
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7
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Lloyd DG, Jonkers I, Delp SL, Modenese L. The History and Future of Neuromusculoskeletal Biomechanics. J Appl Biomech 2023; 39:273-283. [PMID: 37751904 DOI: 10.1123/jab.2023-0165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 09/28/2023]
Abstract
The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.
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Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, School of Health Science and Social Work, Griffith University, Gold Coast, QLD, Australia
| | - Ilse Jonkers
- Institute of Physics-Based Modeling for in Silico Health, Human Movement Science Department, KU Leuven, Leuven, Belgium
| | - Scott L Delp
- Bioengineering, Mechanical Engineering and Orthopedic Surgery, and Wu Tsai Human Performance Alliance at Stanford, Stanford University, Stanford, CA, USA
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia
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8
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Mahdian ZS, Wang H, Refai MIM, Durandau G, Sartori M, MacLean MK. Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review. J Appl Biomech 2023; 39:318-333. [PMID: 37751903 DOI: 10.1123/jab.2023-0046] [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: 02/28/2023] [Revised: 08/11/2023] [Accepted: 08/18/2023] [Indexed: 09/28/2023]
Abstract
Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.
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Affiliation(s)
- Zahra S Mahdian
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Huawei Wang
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | | | - Guillaume Durandau
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Mhairi K MacLean
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
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Zabaleta-Korta A, Fernández-Peña E, Torres-Unda J, Francés M, Zubillaga A, Santos-Concejero J. Regional Hypertrophy: The Effect of Exercises at Long and Short Muscle Lengths in Recreationally Trained Women. J Hum Kinet 2023; 87:259-270. [PMID: 37559762 PMCID: PMC10407320 DOI: 10.5114/jhk/163561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/03/2023] [Indexed: 08/11/2023] Open
Abstract
The aim of the present study was to analyse the role of exercises' resistance profile in regional hypertrophy. Thirty-eight healthy women completed a 9-week resistance training program consisting of either 4 sets of 12 repetitions to volitional failure of inclined bicep curls (INC group) or preacher curls (PREA group), three times per week. Pre- and post-intervention muscle thickness was measured using B-mode ultrasound imaging with a linear-array transducer. Scan acquisition sites were determined by measuring 50%, 60% and 70% of the distance between the posterior crest of the acromion and the olecranon. Statistical significance was set at p < 0.05. No region of the INC group grew when comparing pre- to post-intervention. The 70% region of the PREA group grew significantly (muscle thickness increased from 2.7 ± 0.43 cm to 2.94 ± 0.44 cm). We found no growth differences between regions when analysing per group (p = 0.274), region (p = 0.571) or group*region (p = 0.367). Our results show that the distal region of the arm grows in response to the preacher curl that places the highest amount of strain in the range of motion in which the arm muscles are more elongated.
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Affiliation(s)
- Aitor Zabaleta-Korta
- Sports and Education Department, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Eneko Fernández-Peña
- Sports and Education Department, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Jon Torres-Unda
- Physiotherapy Department, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Maider Francés
- Physiotherapy Department, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Asier Zubillaga
- Sports and Education Department, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Jordan Santos-Concejero
- Sports and Education Department, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
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Haggie L, Schmid L, Röhrle O, Besier T, McMorland A, Saini H. Linking cortex and contraction-Integrating models along the corticomuscular pathway. Front Physiol 2023; 14:1095260. [PMID: 37234419 PMCID: PMC10206006 DOI: 10.3389/fphys.2023.1095260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.
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Affiliation(s)
- Lysea Haggie
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Laura Schmid
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
| | - Oliver Röhrle
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
- Stuttgart Center for Simulation Sciences (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Thor Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Angus McMorland
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Exercise Sciences, University of Auckland, Auckland, New Zealand
| | - Harnoor Saini
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Distal overactivation of gastrocnemius medialis in persistent plantarflexion weakness following Achilles tendon repair. J Biomech 2023; 148:111459. [PMID: 36738627 DOI: 10.1016/j.jbiomech.2023.111459] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 01/09/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
Structural alterations of the triceps surae and Achilles tendon (AT) can promote plantarflexion weakness one-year following an AT repair, influencing the activation strategies of the Gastrocnemius Medialis (GM) muscle. However, this is yet to be demonstrated. We aimed to determine whether patients with plantar flexion weakness one-year after AT repair show altered GM spatial activation. In this cross-sectional and case-control study, ten middle-aged men (age 34 ± 7 years old, and 12.9 ± 1.1 months post-surgery) with a high AT total rupture score who attended conventional physiotherapy for six months after surgery, and ten healthy control men (age 28 ± 9 years old), performed maximal and submaximal (40, 60 and 90%) voluntary isometric plantarflexion contractions on a dynamometer. The peak plantar flexor torque was determined by isokinetic dynamometry and the GM neuromuscular activation was measured with a linear surface-electromyography (EMG) array. Overall EMG activation (averaged channels) increased when the muscle contraction levels increased for both groups. EMG spatial analysis in AT repaired group showed an increased activation located distally at 85-99%, 75-97%, and 79-97% of the electrode array length for 40%, 60%, and 90% of the maximal voluntary isometric contractions, respectively. In conclusion, patients with persistent plantar flexion weakness after AT rupture showed higher distal overactivation in GM.
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Chen C, Yu Y, Sheng X, Meng J, Zhu X. Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control. IEEE Trans Neural Syst Rehabil Eng 2023; 31:1807-1815. [PMID: 37030732 DOI: 10.1109/tnsre.2023.3260209] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average [Formula: see text] of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.
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13
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Saini H, Klotz T, Röhrle O. Modelling motor units in 3D: influence on muscle contraction and joint force via a proof of concept simulation. Biomech Model Mechanobiol 2022; 22:593-610. [PMID: 36572787 PMCID: PMC10097764 DOI: 10.1007/s10237-022-01666-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022]
Abstract
AbstractFunctional heterogeneity is a skeletal muscle’s ability to generate diverse force vectors through localised motor unit (MU) recruitment. Existing 3D macroscopic continuum-mechanical finite element (FE) muscle models neglect MU anatomy and recruit muscle volume simultaneously, making them unsuitable for studying functional heterogeneity. Here, we develop a method to incorporate MU anatomy and information in 3D models. Virtual fibres in the muscle are grouped into MUs via a novel “virtual innervation” technique, which can control the units’ size, shape, position, and overlap. The discrete MU anatomy is then mapped to the FE mesh via statistical averaging, resulting in a volumetric MU distribution. Mesh dependency is investigated using a 2D idealised model and revealed that the amount of MU overlap is inversely proportional to mesh dependency. Simultaneous recruitment of a MU’s volume implies that action potentials (AP) propagate instantaneously. A 3D idealised model is used to verify this assumption, revealing that neglecting AP propagation results in a slightly less-steady force, advanced in time by approximately 20 ms, at the tendons. Lastly, the method is applied to a 3D, anatomically realistic model of the masticatory system to demonstrate the functional heterogeneity of masseter muscles in producing bite force. We found that the MU anatomy significantly affected bite force direction compared to bite force magnitude. MU position was much more efficacious in bringing about bite force changes than MU overlap. These results highlight the relevance of MU anatomy to muscle function and joint force, particularly for muscles with complex neuromuscular architecture.
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Affiliation(s)
- Harnoor Saini
- Institute of Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, BW Germany
| | - Thomas Klotz
- Institute of Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, BW Germany
| | - Oliver Röhrle
- Institute of Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, BW Germany
- Stuttgart Center for Simulation Technology (SC SimTech), University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, BW Germany
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14
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Caillet AH, Phillips ATM, Farina D, Modenese L. Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling. PLoS Comput Biol 2022; 18:e1010556. [PMID: 36174126 PMCID: PMC9553065 DOI: 10.1371/journal.pcbi.1010556] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 10/11/2022] [Accepted: 09/08/2022] [Indexed: 11/18/2022] Open
Abstract
Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire-and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.
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Affiliation(s)
- Arnault H. Caillet
- Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
| | - Andrew T. M. Phillips
- Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
| | - Dario Farina
- Department of Bioengineering, Imperial College London, United Kingdom
| | - Luca Modenese
- Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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15
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Kobayashi RO, Gogeascoechea A, Tomy LJ, Asseldonk EV, Sartori M. Neural data-driven model of spinal excitability changes induced by transcutaneous electrical stimulation in spinal cord injury subjects. IEEE Int Conf Rehabil Robot 2022; 2022:1-6. [PMID: 36176142 DOI: 10.1109/icorr55369.2022.9896517] [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: 06/16/2023]
Abstract
The efficacy of trans-spinal direct current stimulation (tsDCS) as neurorehabilitation technology remains sub-optimal, partly due to the variability introduced by subject-specific neurophysiological features and stimulation conditions (e.g. electrode placement, stimulating amplitude, polarity, etc.) Hence, current therapies apply tsDCS in an open-loop fashion, resulting in a lack of standardized protocols for controlling elicited neuronal adaptations in closed-loop. Through the combination of high-density electromyogram (HD-EMG) decomposition, biophysical neuronal modelling and metaheuristic optimization, this work presents a novel neural data-driven framework for estimating subject-specific features and quantifying acute neuronal adaptations elicited by tsDCS on incomplete spinal cord injury subjects. This approach consists of calibrating the anatomical parameters (e.g. soma diameter) of in silico $\alpha-$motoneuron (MN) models for firing similarly to in vivo MNs decoded from HD-EMG. Assuming that cathodal-tsDCS elicits excitability changes in the MN pool, while preserving their anatomical parameters, optimization of an excitability gain common to the entire pool was performed to minimize discrepancies in firing rate and recruitment time between in vivo and in silico MNs after cathodal-tsDCS. This quantification of excitability changes on MN models calibrated in a person specific way enables closing the loop with neuro-modulation devices to tailor neurorehabilitation therapies. Clinical Relevance - This framework addresses a key limitation in non-invasive neuro-modulative technologies via a novel model-assisted framework that enables quantifying acute excitability changes induced on a person-specific in silico MN pool calibrated using in vivo neural data. This will enable the development of advanced controllers for modulating targeted neuronal adaptations in closed-loop.
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16
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Durandau G, Rampeltshammer WF, Kooij HVD, Sartori M. Neuromechanical Model-Based Adaptive Control of Bilateral Ankle Exoskeletons: Biological Joint Torque and Electromyogram Reduction Across Walking Conditions. IEEE T ROBOT 2022. [DOI: 10.1109/tro.2022.3170239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Guillaume Durandau
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, NB, The Netherlands
| | - Wolfgang F. Rampeltshammer
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, NB, The Netherlands
| | - Herman van der Kooij
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, NB, The Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, NB, The Netherlands
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17
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Alix-Fages C, Del Vecchio A, Baz-Valle E, Santos-Concejero J, Balsalobre-Fernández C. The role of the neural stimulus in regulating skeletal muscle hypertrophy. Eur J Appl Physiol 2022; 122:1111-1128. [PMID: 35138447 DOI: 10.1007/s00421-022-04906-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/28/2022] [Indexed: 02/06/2023]
Abstract
Resistance training is frequently performed with the goal of stimulating muscle hypertrophy. Due to the key roles motor unit recruitment and mechanical tension play to induce muscle growth, when programming, the manipulation of the training variables is oriented to provoke the correct stimulus. Although it is known that the nervous system is responsible for the control of motor units and active muscle force, muscle hypertrophy researchers and trainers tend to only focus on the adaptations of the musculotendinous unit and not in the nervous system behaviour. To better guide resistance exercise prescription for muscle hypertrophy and aiming to delve into the mechanisms that maximize this goal, this review provides evidence-based considerations for possible effects of neural behaviour on muscle growth when programming resistance training, and future neurophysiological measurement that should be tested when training to increase muscle mass. Combined information from the neural and muscular structures will allow to understand the exact adaptations of the muscle in response to a given input (neural drive to the muscle). Changes at different levels of the nervous system will affect the control of motor units and mechanical forces during resistance training, thus impacting the potential hypertrophic adaptations. Additionally, this article addresses how neural adaptations and fatigue accumulation that occur when resistance training may influence the hypertrophic response and propose neurophysiological assessments that may improve our understanding of resistance training variables that impact on muscular adaptations.
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Affiliation(s)
- Carlos Alix-Fages
- Applied Biomechanics and Sport Technology Research Group, Autonomous University of Madrid, C/ Fco Tomas y Valiente 3, Cantoblanco, 28049, Madrid, Spain.
| | - Alessandro Del Vecchio
- Neuromuscular Physiology and Neural Interfacing Group, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University, Erlangen-Nürnberg, Germany
| | - Eneko Baz-Valle
- Department of Physical Education and Sport, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Jordan Santos-Concejero
- Department of Physical Education and Sport, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Carlos Balsalobre-Fernández
- Applied Biomechanics and Sport Technology Research Group, Autonomous University of Madrid, C/ Fco Tomas y Valiente 3, Cantoblanco, 28049, Madrid, Spain
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18
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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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19
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Chen C, Yu Y, Sheng X, Zhu X. Non-invasive analysis of motor unit activation during simultaneous and continuous wrist movements. IEEE J Biomed Health Inform 2021; 26:2106-2115. [PMID: 34910644 DOI: 10.1109/jbhi.2021.3135575] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Surface electromyography (EMG) signals have shown promising applications in human-machine interfacing (HMI) systems such as orthotics, prosthetics, and exoskeletons. Nevertheless, existing myoelectric control methods, generally based on time-domain or frequency-domain features, could not directly interpret neural commands. EMG decomposition techniques have become a prevailing solution to decode the motor neuron discharges from the spinal cord, whereas only single degree-of-freedom (DoF) movements are primarily involved in the current neural-based interfaces, resulting in limited intuitiveness and functionality. Here, we propose a non-invasive framework to analyze motor unit activities and estimate wrist torques during simultaneous contractions of multiple DoFs. Motor unit discharges were decoded from surface EMG signals and pooled into groups during sequential wrist movements. Then three neural features were extracted and linearly projected to the torques of multi-DoF tasks. On average, there were 4413 motor units identified for each motion with a PNR value of 25.82.9 dB. The neural features outperformed the classic EMG feature on the estimation accuracy with higher correlation coefficients and smoothness. These results demonstrate the feasibility and superiority of the proposed framework in kinetics estimation of simultaneous movements, extending the potential applications of surface EMG decomposition in human-machine interfaces.
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20
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Volk VL, Hamilton LD, Hume DR, Shelburne KB, Fitzpatrick CK. Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling. Sci Rep 2021; 11:22983. [PMID: 34836986 PMCID: PMC8626416 DOI: 10.1038/s41598-021-02298-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/10/2021] [Indexed: 11/12/2022] Open
Abstract
Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.
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Affiliation(s)
- Victoria L Volk
- Micron School of Materials Science and Engineering, Boise State University, Boise, ID, USA.,Mechanical and Biomedical Engineering, Boise State University, 1910 University Drive, MS-2085, Boise, ID, 83725-2085, USA
| | - Landon D Hamilton
- Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, USA
| | - Donald R Hume
- Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, USA
| | - Kevin B Shelburne
- Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, USA
| | - Clare K Fitzpatrick
- Mechanical and Biomedical Engineering, Boise State University, 1910 University Drive, MS-2085, Boise, ID, 83725-2085, USA.
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21
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Hernandez AG, Kobayashi RO, Yavuz US, Sartori M. Identification of Motor Unit Twitch Properties in the Intact Human In Vivo. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6310-6313. [PMID: 34892556 DOI: 10.1109/embc46164.2021.9630328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual's physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R2>0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.Clinical Relevance- This work has multiple implications for understanding the underlying mechanism of motor impairment and for developing closed-loop strategies for modulating alpha motor circuitries in neurologically injured individuals.
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22
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Fleming A, Stafford N, Huang S, Hu X, Ferris DP, Huang H(H. Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions. J Neural Eng 2021; 18:10.1088/1741-2552/ac1176. [PMID: 34229307 PMCID: PMC8694273 DOI: 10.1088/1741-2552/ac1176] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/06/2021] [Indexed: 11/16/2022]
Abstract
Objective.Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations.Approach.We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses.Main results.This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives.Significance.This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.
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Affiliation(s)
- Aaron Fleming
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States of America
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America
- Equal contribution as the first author
| | - Nicole Stafford
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, United States of America
- Equal contribution as the first author
| | - Stephanie Huang
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States of America
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States of America
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, United States of America
| | - He (Helen) Huang
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States of America
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America
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23
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Gizzi L, Vujaklija I, Sartori M, Röhrle O, Severini G. Editorial: Somatosensory Integration in Human Movement: Perspectives for Neuromechanics, Modelling and Rehabilitation. Front Bioeng Biotechnol 2021; 9:725603. [PMID: 34336813 PMCID: PMC8317207 DOI: 10.3389/fbioe.2021.725603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Leonardo Gizzi
- Institute for Modelling and Simulation of Biomechanical Systems, Chair for Continuum Biomechanics and Mechanobiology, University of Stuttgart, Stuttgart, Germany.,SimTech Cluster of Excellence "Data Integrated Simulation Science" EXT 2075, University of Stuttgart, Stuttgart, Germany
| | - Ivan Vujaklija
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, AE Enschede, Netherlands
| | - Oliver Röhrle
- Institute for Modelling and Simulation of Biomechanical Systems, Chair for Continuum Biomechanics and Mechanobiology, University of Stuttgart, Stuttgart, Germany.,SimTech Cluster of Excellence "Data Integrated Simulation Science" EXT 2075, University of Stuttgart, Stuttgart, Germany
| | - Giacomo Severini
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
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24
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Pizzolato C, Gunduz MA, Palipana D, Wu J, Grant G, Hall S, Dennison R, Zafonte RD, Lloyd DG, Teng YD. Non-invasive approaches to functional recovery after spinal cord injury: Therapeutic targets and multimodal device interventions. Exp Neurol 2021; 339:113612. [DOI: 10.1016/j.expneurol.2021.113612] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/24/2020] [Accepted: 01/11/2021] [Indexed: 12/16/2022]
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25
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Chen C, Yu Y, Sheng X, Farina D, Zhu X. Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time. J Neural Eng 2021; 18. [PMID: 33764315 DOI: 10.1088/1741-2552/abf186] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 03/24/2021] [Indexed: 11/12/2022]
Abstract
Objective.Surface electromyography (EMG) decomposition techniques can be used to establish human-machine interfacing (HMI), but most investigations are implemented offline due to the computational load of the approach. Here, we generalize the offline decomposition algorithm to identify the motor unit (MU) activities in real time, and we propose a MU-based approach for online simultaneous and proportional control of multiple motor tasks.Approach.High-density surface EMG signals recorded from forearm muscles were decomposed into motor unit spike trains (MUST) with the proposed decomposition method. The MUSTs were first pooled into clusters in the calibration phase and the cumulative discharges of active MUs in each group were extracted as the control signal for each motor task. Then the subjects were instructed to control a virtual cursor with multiple motor tasks involving grasp and wrist movements. Fifteen able-bodied subjects and two patients with limb deficiency participated in the experiments to validate the proposed control scheme.Main results.On average, over 20 MUSTs were identified in real time with an estimated decomposition accuracy > 85%. The cumulative discharge in each pool was highly correlated with the activation of the specific motion (R=0.93±0.05). Moreover, the proposed MU-based method had superior performance in online tests than conventional myo-control methods based on global EMG features.Significance.These results indicate the feasibility of real-time neural decoding in a non-invasive way. Moreover, the superior performance in online tests proves the potential of the MU-based approach for the simultaneous and proportional control, promoting the application of EMG decomposition for HMI systems.
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Affiliation(s)
- Chen Chen
- Department of Mechanical Engineering, Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, No. 800, Dongchuan Road, Shanghai, 200240, CHINA
| | - Yang Yu
- Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, 800 Dongchuan RD. Minhang District, Shanghai, 200240, CHINA
| | - Xinjun Sheng
- Department of Mechanical Engineering, Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, No. 800, Dongchuan Road, Shanghai, 200240, CHINA
| | - Dario Farina
- Department of Bioengineering, Imperial College London, Royal School of Mines South Kensington Campus, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Xiangyang Zhu
- Department of Mechanical Engineering, Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, No. 800, Dongchuan Road, Shanghai, 200240, CHINA
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Wen Y, Avrillon S, Hernandez-Pavon JC, Kim SJ, Hug F, Pons JL. A convolutional neural network to identify motor units from high-density surface electromyography signals in real time. J Neural Eng 2021; 18. [PMID: 33721852 DOI: 10.1088/1741-2552/abeead] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 03/15/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. APPROACH The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: 1) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and 2) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. MAIN RESULTS The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2 to 21.4 s/epoch vs. 6.5 to 47.8 s/epoch, respectively) and prediction time (0.04 vs. 0.27 s/sample, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88-0.95). SIGNIFICANCE We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.
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Affiliation(s)
- Yue Wen
- Legs and Walking Lab, Shirley Ryan AbilityLab, 355 East Erie Street, Chicago, Illinois, 60611-2654, UNITED STATES
| | - Simon Avrillon
- Shirley Ryan AbilityLab, 355 E Erie St, Chicago, Illinois, 60601, UNITED STATES
| | - Julio Cesar Hernandez-Pavon
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, 251 E Huron St, Chicago, Illinois, 60611, UNITED STATES
| | - Sangjoon Jonathan Kim
- Shirley Ryan AbilityLab, 355 E Erie St, Chicago, Illinois, 60611-2654, UNITED STATES
| | - Francois Hug
- Laboratoire 'Motricite, Interactions, Performance', Universite de Nantes, JE 2438 UFRSTAPS,, 25 bis Guy Mollet BP 72206, Nantes, F-44000 France, Nantes, 72206, FRANCE
| | - Jose Luis Pons
- Bioengineering Group, Spanish Research Council, Serrano 117, Arganda del Rey (Madrid), 28006, SPAIN
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Ranavolo A, Ajoudani A, Cherubini A, Bianchi M, Fritzsche L, Iavicoli S, Sartori M, Silvetti A, Vanderborght B, Varrecchia T, Draicchio F. The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5750. [PMID: 33050438 PMCID: PMC7599507 DOI: 10.3390/s20205750] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/24/2020] [Accepted: 10/03/2020] [Indexed: 02/06/2023]
Abstract
Due to the epochal changes introduced by "Industry 4.0", it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human-robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative "direct instrumental evaluations" and "rating of standard methods", allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers' awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.
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Affiliation(s)
- Alberto Ranavolo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040 Rome, Italy; (S.I.); (A.S.); (T.V.); (F.D.)
| | - Arash Ajoudani
- HRI2 Laboratory, Istituto Italiano di Tecnologia, 16163 Genova, Italy;
| | | | - Matteo Bianchi
- Centro di Ricerca “Enrico Piaggio” and Department of Information Engineering, Università di Pisa, 56126 Pisa, Italy;
| | - Lars Fritzsche
- Ergonomics Division, IMK Automotive GmbH, 09128 Chemnitz, Germany;
| | - Sergio Iavicoli
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040 Rome, Italy; (S.I.); (A.S.); (T.V.); (F.D.)
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, 7522 NB Enschede, The Netherlands;
| | - Alessio Silvetti
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040 Rome, Italy; (S.I.); (A.S.); (T.V.); (F.D.)
| | - Bram Vanderborght
- Brubotics, Vrije Universiteit Brussel, 1050 Brussels, Belgium;
- Flanders Make, Oude Diestersebaan 133, 3920 Lommel, Belgium
| | - Tiwana Varrecchia
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040 Rome, Italy; (S.I.); (A.S.); (T.V.); (F.D.)
| | - Francesco Draicchio
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040 Rome, Italy; (S.I.); (A.S.); (T.V.); (F.D.)
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Gogeascoechea A, Kuck A, van Asseldonk E, Negro F, Buitenweg JR, Yavuz US, Sartori M. Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients Receiving Trans-spinal Electrical Stimulation. Front Neurol 2020; 11:493. [PMID: 32582012 PMCID: PMC7296155 DOI: 10.3389/fneur.2020.00493] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/05/2020] [Indexed: 12/22/2022] Open
Abstract
Trans-spinal direct current stimulation (tsDCS) provides a non-invasive, clinically viable approach to potentially restore physiological neuromuscular function after neurological impairment, e.g., spinal cord injury (SCI). Use of tsDCS has been hampered by the inability of delivering stimulation patterns based on the activity of neural targets responsible to motor function, i.e., α-motor neurons (α-MNs). State of the art modeling and experimental techniques do not provide information about how individual α-MNs respond to electrical fields. This is a major element hindering the development of neuro-modulative technologies highly tailored to an individual patient. For the first time, we propose the use of a signal-based approach to infer tsDCS effects on large α-MNs pools in four incomplete SCI individuals. We employ leg muscles spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-MNs discharges across multiple lumbosacral segments during isometric plantar flexion sub-maximal contractions. This is done before, immediately after and 30 min after sub-threshold cathodal stimulation. We deliver sham tsDCS as a control measure. First, we propose a new algorithm for removing compromised information from decomposed α-MNs spike trains, thereby enabling robust decomposition and frequency-domain analysis. Second, we propose the analysis of α-MNs spike trains coherence (i.e., frequency-domain) as an indicator of spinal response to tsDCS. Results showed that α-MNs spike trains coherence analysis sensibly varied across stimulation phases. Coherence analyses results suggested that the common synaptic input to α-MNs pools decreased immediately after cathodal tsDCS with a persistent effect after 30 min. Our proposed non-invasive decoding of individual α-MNs behavior may open up new avenues for the design of real-time closed-loop control applications including both transcutaneous and epidural spinal electrical stimulation where stimulation parameters are adjusted on-the-fly.
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Affiliation(s)
- Antonio Gogeascoechea
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
| | - Alexander Kuck
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
| | - Edwin van Asseldonk
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
| | - Francesco Negro
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Jan R Buitenweg
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Utku S Yavuz
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
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29
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Nizamis K, Rijken NHM, van Middelaar R, Neto J, Koopman BFJM, Sartori M. Characterization of Forearm Muscle Activation in Duchenne Muscular Dystrophy via High-Density Electromyography: A Case Study on the Implications for Myoelectric Control. Front Neurol 2020; 11:231. [PMID: 32351441 PMCID: PMC7174775 DOI: 10.3389/fneur.2020.00231] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/11/2020] [Indexed: 12/26/2022] Open
Abstract
Duchenne muscular dystrophy (DMD) is a genetic disorder that results in progressive muscular degeneration. Although medical advances increased their life expectancy, DMD individuals are still highly dependent on caregivers. Hand/wrist function is central for providing independence, and robotic exoskeletons are good candidates for effectively compensating for deteriorating functionality. Robotic hand exoskeletons require the accurate decoding of motor intention typically via surface electromyography (sEMG). Traditional low-density sEMG was used in the past to explore the muscular activations of individuals with DMD; however, it cannot provide high spatial resolution. This study characterized, for the first time, the forearm high-density (HD) electromyograms of three individuals with DMD while performing seven hand/wrist-related tasks and compared them to eight healthy individuals (all data available online). We looked into the spatial distribution of HD-sEMG patterns by using principal component analysis (PCA) and also assessed the repeatability and the amplitude distributions of muscle activity. Additionally, we used a machine learning approach to assess DMD individuals' potentials for myocontrol. Our analysis showed that although participants with DMD were able to repeat similar HD-sEMG patterns across gestures (similarly to healthy participants), a fewer number of electrodes was activated during their gestures compared to the healthy participants. Additionally, participants with DMD activated their muscles close to maximal contraction level (0.63 ± 0.23), whereas healthy participants had lower normalized activations (0.26 ± 0.2). Lastly, participants with DMD showed on average fewer PCs (3), explaining 90% of the complete gesture space than the healthy (5). However, the ability of the DMD participants to produce repeatable HD-sEMG patterns was unexpectedly comparable to that of healthy participants, and the same holds true for their offline myocontrol performance, disproving our hypothesis and suggesting a clear potential for the myocontrol of wearable exoskeletons. Our findings present evidence for the first time on how DMD leads to progressive alterations in hand/wrist motor control in DMD individuals compared to healthy. The better understanding of these alterations can lead to further developments for the intuitive and robust myoelectric control of active hand exoskeletons for individuals with DMD.
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Affiliation(s)
- Kostas Nizamis
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Noortje H M Rijken
- Faculty Physical Activity and Health, Saxion University of Applied Sciences, Enschede, Netherlands
| | - Robbert van Middelaar
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - João Neto
- Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Bart F J M Koopman
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
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30
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Kapelner T, Sartori M, Negro F, Farina D. Neuro-Musculoskeletal Mapping for Man-Machine Interfacing. Sci Rep 2020; 10:5834. [PMID: 32242142 PMCID: PMC7118097 DOI: 10.1038/s41598-020-62773-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 03/09/2020] [Indexed: 12/31/2022] Open
Abstract
We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function.
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Affiliation(s)
- Tamas Kapelner
- Orthopaedic Surgery and Plastic Surgery - Research Department of Neurorehabilitation Systems, Clinic for Trauma Surgery, University Medical Center Göttingen, Göttingen, 37075, Germany
| | - Massimo Sartori
- Department of Biomechanical Engineering, TechMed Centre, University of Twente, Enschede, Netherlands
| | - Francesco Negro
- Department of Clinical and Experimental Sciences, Research Centre for Neuromuscular Function and Adapted Physical Activity "Teresa Camplani", Università degli Studi di Brescia, Brescia, Italy
| | - Dario Farina
- Department of Bioengineering, Imperial College London, SW7 2AZ, London, UK.
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31
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Nuckols RW, Dick TJM, Beck ON, Sawicki GS. Ultrasound imaging links soleus muscle neuromechanics and energetics during human walking with elastic ankle exoskeletons. Sci Rep 2020; 10:3604. [PMID: 32109239 PMCID: PMC7046782 DOI: 10.1038/s41598-020-60360-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 02/07/2020] [Indexed: 11/16/2022] Open
Abstract
Unpowered exoskeletons with springs in parallel to human plantar flexor muscle-tendons can reduce the metabolic cost of walking. We used ultrasound imaging to look 'under the skin' and measure how exoskeleton stiffness alters soleus muscle contractile dynamics and shapes the user's metabolic rate during walking. Eleven participants (4F, 7M; age: 27.7 ± 3.3 years) walked on a treadmill at 1.25 m s-1 and 0% grade with elastic ankle exoskeletons (rotational stiffness: 0-250 Nm rad-1) in one training and two testing days. Metabolic savings were maximized (4.2%) at a stiffness of 50 Nm rad-1. As exoskeleton stiffness increased, the soleus muscle operated at longer lengths and improved economy (force/activation) during early stance, but this benefit was offset by faster shortening velocity and poorer economy in late stance. Changes in soleus activation rate correlated with changes in users' metabolic rate (p = 0.038, R2 = 0.44), highlighting a crucial link between muscle neuromechanics and exoskeleton performance; perhaps informing future 'muscle-in-the loop' exoskeleton controllers designed to steer contractile dynamics toward more economical force production.
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Affiliation(s)
- R W Nuckols
- Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Raleigh, NC, 27607, USA.
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, 02138, USA.
| | - T J M Dick
- Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Raleigh, NC, 27607, USA
- School of Biomedical Sciences, University of Queensland, St Lucia, Queensland, Australia
| | - O N Beck
- George W. Woodruff School of Mechanical Engineering and School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - G S Sawicki
- Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Raleigh, NC, 27607, USA.
- George W. Woodruff School of Mechanical Engineering and School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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32
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Cop CP, Durandau G, Esteban AM, van 't Veld RC, Schouten AC, Sartori M. Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks: a Validation-Based Approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4104-4107. [PMID: 31946773 DOI: 10.1109/embc.2019.8857391] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.
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Durandau G, Farina D, Asín-Prieto G, Dimbwadyo-Terrer I, Lerma-Lara S, Pons JL, Moreno JC, Sartori M. Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling. J Neuroeng Rehabil 2019; 16:91. [PMID: 31315633 PMCID: PMC6637518 DOI: 10.1186/s12984-019-0559-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 06/26/2019] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. METHODS We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. RESULTS We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. CONCLUSIONS Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
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Affiliation(s)
- Guillaume Durandau
- Faculty of Engineering Technology, Department of Biomechanical Engineering, University of Twente, Technical Medical Centre, Building: Horsting. Room: W106, P.O. Box: 217, 7500 AE Enschede, The Netherlands
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, UK
| | - Guillermo Asín-Prieto
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain
| | - Iris Dimbwadyo-Terrer
- Occupational Thinks Research Group, Centro Superior de Estudios Universitarios La Salle, Universidad Autónoma de Madrid, Madrid, Spain
| | - Sergio Lerma-Lara
- Occupational Thinks Research Group, Centro Superior de Estudios Universitarios La Salle, Universidad Autónoma de Madrid, Madrid, Spain
| | - Jose L. Pons
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain
| | - Juan C. Moreno
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain
| | - Massimo Sartori
- Faculty of Engineering Technology, Department of Biomechanical Engineering, University of Twente, Technical Medical Centre, Building: Horsting. Room: W106, P.O. Box: 217, 7500 AE Enschede, The Netherlands
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34
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Röhrle O, Yavuz UŞ, Klotz T, Negro F, Heidlauf T. Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 11:e1457. [PMID: 31237041 DOI: 10.1002/wsbm.1457] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 05/13/2019] [Accepted: 05/14/2019] [Indexed: 01/10/2023]
Abstract
Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state-of-the-art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub-models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.
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Affiliation(s)
- Oliver Röhrle
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany.,Stuttgart Center for Simulation Sciences (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Utku Ş Yavuz
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany.,Biomedical Signals and Systems, Universiteit Twente, Enschede, The Netherlands
| | - Thomas Klotz
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany.,Stuttgart Center for Simulation Sciences (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Francesco Negro
- Department of Clinical and Experimental Sciences, Universià degli Studi di Brescia, Brescia, Italy
| | - Thomas Heidlauf
- EPS5 - Simulation and System Analysis, Hofer pdc GmbH, Stuttgart, Germany
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35
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Nizamis K, Stienen AHA, Kamper DG, Keller T, Plettenburg DH, Rouse EJ, Farina D, Koopman BFJM, Sartori M. Transferrable Expertise From Bionic Arms to Robotic Exoskeletons: Perspectives for Stroke and Duchenne Muscular Dystrophy. ACTA ACUST UNITED AC 2019. [DOI: 10.1109/tmrb.2019.2912453] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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36
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Sreenivasa M, Valero-Cuevas FJ, Tresch M, Nakamura Y, Schouten AC, Sartori M. Editorial: Neuromechanics and Control of Physical Behavior: From Experimental and Computational Formulations to Bio-inspired Technologies. Front Comput Neurosci 2019; 13:13. [PMID: 30941027 PMCID: PMC6434995 DOI: 10.3389/fncom.2019.00013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 02/15/2019] [Indexed: 11/16/2022] Open
Affiliation(s)
- Manish Sreenivasa
- Department of Mechanical, Material, Mechatronics and Biomedical Engineering, University of Wollongong, Wollongong, NSW, Australia
| | - Francisco J Valero-Cuevas
- Division of Biokinesiology and Physical Therapy, Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Matthew Tresch
- Shirley Ryan AbilityLab, Chicago, IL, United States.,Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - Yoshihiko Nakamura
- Department of Mechano-Informatics, School of Information Science and Technology, University of Tokyo, Tokyo, Japan
| | - Alfred C Schouten
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands.,Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
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Savc M, Glaser V, Kranjec J, Cikajlo I, Matjacic Z, Holobar A. Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1935-1944. [PMID: 30281464 DOI: 10.1109/tnsre.2018.2869426] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We compared non-negative matrix factorization (NMF) and convolution kernel compensation techniques for high-density electromyogram decomposition. The experimental data were recorded from nine healthy persons during controlled single degree of freedom (DOF) wrist flexion-extension, supination-pronation, and ulnar-radial deviation movements. We assembled the identified motor units and NMF components into three groups. Those active mostly during the first and the second movement direction per DOF were placed in the G1 and G3 groups, respectively. The remaining components were nonspecific for movement direction and were placed in the G2 group. In ulnar and radial deviation, the relative energies of identified cumulative motor unit spike trains (CSTs) and NMF components were similarly distributed among the groups. In other two movement types, the energy of NMF components in the G2 group was significantly larger than the energy of CSTs. We further performed a coherence analysis between CSTs and sums of NMF components in each group. Both decompositions demonstrated a solid match, but only at frequencies <3 Hz. At higher frequencies, the coherence hardly exceeded the value of 0.5. Potential reasons for these discrepancies include the negative impact of motor unit action potential shapes and noise on NMF decomposition.
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Sartori M, Durandau G, Došen S, Farina D. Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling. J Neural Eng 2018; 15:066026. [PMID: 30229745 DOI: 10.1088/1741-2552/aae26b] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces. APPROACH This work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system. MAIN RESULTS Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks. SIGNIFICANCE The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies.
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Affiliation(s)
- Massimo Sartori
- Department of Biomechanical Engineering, TechMed Centre, University of Twente, Enschede, Netherlands
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Úbeda A, Azorín JM, Farina D, Sartori M. Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces. Front Comput Neurosci 2018; 12:3. [PMID: 29422842 PMCID: PMC5788900 DOI: 10.3389/fncom.2018.00003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 01/04/2018] [Indexed: 11/13/2022] Open
Abstract
One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.
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Affiliation(s)
- Andrés Úbeda
- AUROVA Group, Department of Physics, Systems Engineering and Signal Theory, University of Alicante, San Vicente del Raspeig, Spain.,Brain-Machine Interface Systems Lab, Miguel Hernández University, Elche, Spain
| | - José M Azorín
- Brain-Machine Interface Systems Lab, Miguel Hernández University, Elche, Spain
| | - Dario Farina
- Chair in Neurorehabilitation Engineering, Department of Bioengineering, Imperial College, London, United Kingdom
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
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