Intramuscular EMG-Driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients

Objective: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. Methods: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. Results: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. Conclusion: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. Significance: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation.The authors would like to thank Enrique Pérez Rizo, Natalia Comino Suárez and María Isabel Sinovas Alonso for their assistance on the experimental and data acquisition procedure

Adaptation Strategies for Personalized Gait Neuroprosthetics

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.AK is funded by a faculty endowment by adidas AG. MA, SKH, NM, MN, RJQ, R-DR, RJT are supported by NSF CPS grant 1739800, VA Merit Reviews A2275-R and 3056, and the NIH (5T32EB004314-15, R01 NS040547-13). MS and AG are funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 803035). AJd-A, JMF-L, and JCM are supported by coordinated grants RTI2018-097290-B-C31/C32/C33 (TAILOR project) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. MR is funded by the Lo3-ML project by the Federal Ministry for Education, Science and Technology (BMBF) (Funding No. 16ES1142K). AC, SS, and MV were supported by the European Research Council (ERC) under the project NGBMI (759370), the Einstein Stiftung Berlin, the ERA-NET NEURON project HYBRIDMIND (BMBF, 01GP2121A and -B) and the BMBF project NEO (13GW0483C)

EEG model stability and online decoding of attentional demand during gait using gamma band features

Rehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients’ involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is possible to modify, in real time, the parameters of the rehabilitation according to the patients’ cognitive state. In this paper, an online brain–machine interface to measure the attention level during gait is presented. The system is based on the measurement of selective attention mechanisms manifested as power synchronization and desynchronization in the gamma band. A Linear Discriminant Analysis classifier is used to provide an attention index between 0 and 1 in real time. Robust techniques for artifact rejection and signal standardization are used in order to deal with the problems associated to the measurement of cortical signals during walking. The final interface is validated with 4 incomplete Spinal Cord Injury patients and 4 healthy participants. The system shows an average success rate of 68.1% in the classification of 3 attention levels and a stable behavior of these results during timeThis research has been funded by the Commission of the European Union under the BioMot project – Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT- 2013-10-611695)and by the Spanish Ministry of Science, Innovation and Universities, the Spanish State Agency of Research, and the Commission of the European Union through the European Regional Development Fund. under the Walk project - Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (Grant Agreement number RTI2018-096677-B-I00)

Clinical application of the upper limb motion analysis during wheelchair propulsion

Manual wheelchair propulsion results in a physical demand on the upper extremities that, due to its repetitive nature, leads to chronic pain, specially at wrist and shoulder joints. These problems are increasing because life expectancy of patients with spinal cord injury has incremented during recent years. In fact, the consequence of the long-term use of the wheelchair presents a biomechanical challenge as the upper extremities are not designed to support propulsion repetitive movements. Analyzing the manual wheelchair propulsion gesture with a biomechanical approach provides objective data on the loads and movements that the upper limb supports. Therefore, transferring this biomechanical analysis towards the clinical field through an application will help the facultatives on taking decisions. In this paper, an application which provides flexibility, all the needed information and a report with the key data of a propulsion test has been developed

Derivation of the Gait Deviation Index for Spinal Cord Injury

The Gait Deviation Index (GDI) is a dimensionless multivariate measure of overall gait pathology represented as a single score that indicates the gait deviation from a normal gait average. It is calculated using kinematic data recorded during a three-dimensional gait analysis and an orthonormal vectorial basis with 15 gait features that was originally obtained using singular value decomposition and feature analysis on a dataset of children with cerebral palsy. Ever since, it has been used as an outcome measure to study gait in several conditions, including spinal cord injury (SCI). Nevertheless, the validity of implementing the GDI in a population with SCI has not been studied yet. We investigate the application of these mathematical methods to derive a similar metric but with a dataset of adults with SCI (SCI-GDI). The new SCI-GDI is compared with the original GDI to evaluate their differences and assess the need for a specific GDI for SCI and with the WISCI II to evaluate its sensibility. Our findings show that a 21-feature basis is necessary to account for most of the variance in gait patterns in the SCI population and to provide high-quality reconstructions of the gait curves included in the dataset and in foreign data. Furthermore, using only the first 15 features of our SCI basis, the fidelity of the reconstructions obtained in our population is higher than that when using the basis of the original GDI. The results showed that the SCI-GDI discriminates most levels of the WISCI II scale, except for levels 12 and 18. Statistically significant differences were found between both indexes within each WISCI II level except for 12, 20, and the control group (p < 0.05). In all levels, the average GDI value was greater than the average SCI-GDI value, but the difference between both indexes is larger in data with greater impairment and it reduces progressively toward a normal gait pattern. In conclusion, the implementation of the original GDI in SCI may lead to overestimation of gait function, and our new SCI-GDI is more sensitive to larger gait impairment than the GDI. Further validation of the SCI-GDI with other scales validated in SCI is needed.This research has been supported by the TAILOR project: “Modular robotic and neuroprosthetic customizable systems for the assistance of pathological gait,” funded by Agencia Estatal de Investigación on 2018 call (reference RTI 2018-097290-B-C31)

Feasibility of Transcutaneous Spinal Cord Stimulation Combined with Robotic-Assisted Gait Training (Lokomat) for Gait Rehabilitation of an Incomplete Spinal Cord Injury Subject

The aim of this study was to analyze the feasibility and safety of transcutaneous spinal cord stimulation (tSCS), applied over T11–T12 vertebral level combined with robotic-assisted gait training in a volunteer with incomplete spinal cord injury (SCI). We performed 20 sessions, with 30 min in Lokomat, which first 20 min tSCS was applied. The outcomes measured were the lower extremity motor score (LEMS), modified Ashworth scale (MAS), the lower limbs strength by a hand dynamometer, functional outcomes using 10 m walk test (10MWT), timed up and go test (TUG), Walking index of spinal cord injury (WISCI-II), the spinal cord independence measure (SCIM-III) and pain perceived during treatment. The assessment was done at baseline, after and follow-up at 4 weeks post-treatment. The preliminary results support that the tSCS combined with Lokomat seems to be a safety therapy and there was not a relationship between the level of stimulation intensity and pain perceived

Effect of posture and body weight loading on spinal posterior root reflex responses

Abstract. The posterior root muscle response (PRM) is a monosynaptic reflex that is evoked by single pulse transcutaneous spinal cord stimulation (tSCS). The main aim of this work was to analyse how body weight loading influences PRM reflex threshold measured from several lower limb muscles in healthy participants. PRM reflex responses were evoked with 1-ms rectangular monophasic pulses applied at an interval of 6 s via a self-adhesive electrode (9 × 5 cm) at the T11–T12 vertebral level. Surface electromyographic activity of lower limb muscles was recorded during four different conditions, one in decubitus supine (DS) and the other three involving standing at 100%, 50%, and 0% body weight loading (BW). PRM threshold intensity, peak-to-peak amplitude, and latency for each muscle were analysed in different conditions study. PRM reflex threshold increased with body weight unloading compared with DS, and the largest change was observed between DS and 0% BW for the proximal muscles and between DS and 50% BW for distal muscles. Peak-to-peak amplitude analysis showed only a significant mean decrease of 34.6% (SD 10.4, p = 0.028) in TA and 53.6% (SD 15.1, p = 0.019) in GM muscles between DS and 50% BW. No significant differences were observed for PRM latency. This study has shown that sensorimotor networks can be activated with tSCS in various conditions of body weight unloading. Higher stimulus intensities are necessary to evoke reflex response during standing at 50% body weight loading. These results have practical implications for gait rehabilitation training programmes that include body weight support.This research was funded by the“Junta de Comunidadesde Castilla-La Mancha”and“Fondos FEDER”(EXO-STIM project, grant number SBPLY/19/180501/000316)and by the EXPLORA 2017 program (Recode project,grant number: DPI2017-91117-EXP). DSM has receivedfunding by the European Regional Development Fund(2019/7375)

Transcranial direct current stimulation combined with robotic therapy for upper and lower limb function after stroke: a systematic review and meta-analysis of randomized control trials

Background: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method able to modulate neuronal activity after stroke. The aim of this systematic review was to determine if tDCS combined with robotic therapy (RT) improves limb function after stroke when compared to RT alone. Methods: A search for randomized controlled trials (RCTs) published prior to July 15, 2021 was performed. The main outcome was function assessed with the Fugl-Meyer motor assessment for upper extremities (FM/ue) and 10-m walking test (10MWT) for the lower limbs. As secondary outcomes, strength was assessed with the Motricity Index (MI) or Medical Research Council scale (MRC), spasticity with the modified Ashworth scale (MAS), functional independence with the Barthel Index (BI), and kinematic parameters. Results: Ten studies were included for analysis (n = 368 enrolled participants). The results showed a non-significant effect for tDCS combined with RT to improve upper limb function [standardized mean difference (SMD) = − 0.12; 95% confidence interval (CI): − 0.35–0.11)]. However, a positive effect of the combined therapy was observed in the lower limb function (SMD = 0.48; 95% CI: − 0.15–1.12). Significant results favouring tDCS combined with RT were not found in strength (SMD = − 0.15; 95% CI: − 0.4–0.1), spasticity [mean difference (MD) = − 0.15; 95% CI: − 0.8–0.5)], functional independence (MD = 2.5; 95% CI: − 1.9–6.9) or velocity of movement (SMD = 0.06; 95% CI: − 0.3–0.5) with a “moderate” or “low” recommendation level according to the GRADE guidelines. Conclusions: Current findings suggest that tDCS combined with RT does not improve upper limb function, strength, spasticity, functional independence or velocity of movement after stroke. However, tDCS may enhance the effects of RT alone for lower limb function. tDCS parameters and the stage or type of stroke injury could be crucial factors that determine the effectiveness of this therapy.This was supported by the Ministerio de Economía, Industria y Competitividad. RECODE project [Grant number: DPI2017‑91117‑EXP]; and by Junta de Comunidades de Castilla la Mancha and Fondo Europeo de Desarrollo Regional (Fondos FEDER). EXO‑STIM Project [Grant number: SBPLY/19/180501/000316]

A subject-specific kinematic model to predict human motion in exoskeleton-assisted gait

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5◦ globally, and around 1.5◦ at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton