Classification de mouvements fantômes du membre supérieur chez des amputés huméraux à l'aide de mesures électromyographiques et cinématiques

RÉSUMÉ La perte d’un membre supérieur engendre de nombreux déficits fonctionnels pour l’amputé dans sa vie de tous les jours. En effet, la plupart des activités de la vie quotidienne, telles qu’attacher ses souliers ou ouvrir une bouteille, sont complexes et difficiles à réaliser avec un seul bras fonctionnel. Les impacts de ces déficits augmentent à mesure que le niveau d’amputation est plus haut au niveau du bras. Pour toutes ces personnes, les nombreuses avancées dans le domaine des prothèses myoélectriques, c’est-à-dire commandées par l’activité musculaire des muscles restants après l’amputation, sont encourageantes parce qu’elles permettent d’entretenir l’espoir d’une prothèse à la commande intuitive. Un phénomène particulier, présent chez la majorité des amputés, est celui des sensations au membre fantôme. Ces sensations peuvent se manifester sous plusieurs formes : thermiques, douleurs, mobilités. Les mobilités du membre fantôme sont particulièrement intéressantes pour le développement des prothèses myoélectriques étant donné qu’il a été démontré que les mouvements fantômes produisent une activité électromyographique (EMG) au niveau du membre amputé. Cependant, les études s’intéressant à la détection des mouvements fantômes ont enregistré l’activité EMG provenant de muscles difficilement intégrables dans l’emboiture d’une prothèse myoélectriques, tels que ceux du dos, du torse et de l’épaule. La présente étude se concentre sur la classification des mouvements fantômes chez les amputés huméraux à l’aide de l’EMG dans l’optique de développer une prothèse myoélectrique commandée par reconnaissance de formes. Cinq adultes ayant subi une amputation unilatérale humérale suite à un trauma ont participé à cette étude. L’activité EMG des participants a été enregistrée exclusivement autour de leur moignon. Durant les enregistrements, il était demandé aux participants de réaliser l’un des principaux mouvements fantômes du membre supérieur : la flexion ou l’extension du coude, la pronation ou la supination de l’avant-bras, la flexion ou l’extension du poignet, l’ouverture ou la fermeture de la main et le repos. Chaque mouvement fantôme devait être réalisé symétriquement à l’aide du bras sain et la cinématique de ce dernier a été enregistrée à l’aide d’un système d’analyse du mouvement. Dix caractéristiques (ou « features » en anglais) temporels ont été extraites des signaux EMG et utilisées pour entraîner un réseau de neurones permettant de classifier les mouvements fantômes du membre supérieur.----------ABSTRACT Upper limb amputation creates substantial functional deficits for the amputee. Indeed, most activities of daily living, such as tying shoelaces or opening a bottle, are complex and hard to achieve with only one functional arm. These functional impairments increase as the level of amputation is higher up the arm. For these people, recent advances in the field of myoelectric prostheses, i.e. controlled by the activity of the remaining muscles after amputation, are encouraging because they help maintain the hope of an intuitive prosthesis. A particular phenomenon, occurring in the majority of amputees, is the presence of phantom limb sensations. Phantom limb sensations are of many types: thermal, pain, and mobility. Phantom limb mobilities are particularly interesting for the development of myoelectric prostheses since it has been shown that they produce an electromyographic (EMG) activity in the amputated limb. However, the studies focusing on the detection of phantom movements recorded EMG from muscles that are hard to integrate into the socket element of a myoelectric prosthesis, such as the back, chest and shoulder muscles. This study focuses on the classification of phantom movements in transhumeral amputees using EMG in the context of developing a myoelectric prosthesis controlled by pattern recognition. Five adults who underwent unilateral humeral amputation following a trauma participated in this study. The EMG activity of the participants was recorded exclusively around their stump. During the recordings, participants were asked to perform one of the main upper limb phantom movements: flexion or extension of the elbow, pronation or supination of the forearm, flexion or extension of the wrist, opening or closing the hand and rest. Each phantom movement was to be made symmetrical with the unaffected arm and the kinematics of the latter was recorded using a motion analysis system. Ten time-domain features were extracted from the EMG signals and used to train a neural network to classify the phantom limb movements. The performance of the classifier was evaluated based on the number of movements studied and an optimal set of four EMG features was determined. The impact of kinematic information on the classification performance was also evaluated. The accuracy of the classification varies from one amputee to another, but some trends are common: performance decreases if the number of degrees of freedom considered in the classification increases and/or if the phantom movements become more distal. Moreover, the optimal set of four EMG features provided a performance equivalent to that obtained with all ten EMG features. The addition of the kinematic information improved classification accuracy for all amputees

Current trends and challenges in pediatric access to sensorless and sensor-based upper limb exoskeletons

ABSTRACT: Sensorless and sensor-based upper limb exoskeletons that enhance or support daily motor function are limited for children. This review presents the different needs in pediatrics and the latest trends when developing an upper limb exoskeleton and discusses future prospects to improve accessibility. First, the principal diagnoses in pediatrics and their respective challenge are presented. A total of 14 upper limb exoskeletons aimed for pediatric use were identified in the literature. The exoskeletons were then classified as sensorless or sensor-based, and categorized with respect to the application domain, the motorization solution, the targeted population(s), and the supported movement(s). The relative absence of upper limb exoskeleton in pediatrics is mainly due to the additional complexity required in order to adapt to children’s growth and answer their specific needs and usage. This review highlights that research should focus on sensor-based exoskeletons, which would benefit the majority of children by allowing easier adjustment to the children’s needs. Sensor-based exoskeletons are often the best solution for children to improve their participation in activities of daily living and limit cognitive, social, and motor impairments during their development

Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths

Publisher Copyright: © 2021 The Authors, some rights reserved.Circulating autoantibodies (auto-Abs) neutralizing high concentrations (10 ng/ml; in plasma diluted 1:10) of IFN-alpha and/or IFN-omega are found in about 10% of patients with critical COVID-19 (coronavirus disease 2019) pneumonia but not in individuals with asymptomatic infections. We detect auto-Abs neutralizing 100-fold lower, more physiological, concentrations of IFN-alpha and/or IFN-omega (100 pg/ml; in 1:10 dilutions of plasma) in 13.6% of 3595 patients with critical COVID-19, including 21% of 374 patients >80 years, and 6.5% of 522 patients with severe COVID-19. These antibodies are also detected in 18% of the 1124 deceased patients (aged 20 days to 99 years; mean: 70 years). Moreover, another 1.3% of patients with critical COVID-19 and 0.9% of the deceased patients have auto-Abs neutralizing high concentrations of IFN-beta. We also show, in a sample of 34,159 uninfected individuals from the general population, that auto-Abs neutralizing high concentrations of IFN-alpha and/or IFN-omega are present in 0.18% of individuals between 18 and 69 years, 1.1% between 70 and 79 years, and 3.4% >80 years. Moreover, the proportion of individuals carrying auto-Abs neutralizing lower concentrations is greater in a subsample of 10,778 uninfected individuals: 1% of individuals 80 years. By contrast, auto-Abs neutralizing IFN-beta do not become more frequent with age. Auto-Abs neutralizing type I IFNs predate SARS-CoV-2 infection and sharply increase in prevalence after the age of 70 years. They account for about 20% of both critical COVID-19 cases in the over 80s and total fatal COVID-19 cases.Peer reviewe

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

SignificanceThere is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged 4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute; The Rockefeller University; the St. Giles Foundation; the NIH (Grants R01AI088364 and R01AI163029); the National Center for Advancing Translational Sciences; NIH Clinical and Translational Science Awards program (Grant UL1 TR001866); a Fast Grant from Emergent Ventures; Mercatus Center at George Mason University; the Yale Center for Mendelian Genomics and the Genome Sequencing Program Coordinating Center funded by the National Human Genome Research Institute (Grants UM1HG006504 and U24HG008956); the Yale High Performance Computing Center (Grant S10OD018521); the Fisher Center for Alzheimer’s Research Foundation; the Meyer Foundation; the JPB Foundation; the French National Research Agency (ANR) under the “Investments for the Future” program (Grant ANR-10-IAHU-01); the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (Grant ANR-10-LABX-62-IBEID); the French Foundation for Medical Research (FRM) (Grant EQU201903007798); the French Agency for Research on AIDS and Viral hepatitis (ANRS) Nord-Sud (Grant ANRS-COV05); the ANR GENVIR (Grant ANR-20-CE93-003), AABIFNCOV (Grant ANR-20-CO11-0001), CNSVIRGEN (Grant ANR-19-CE15-0009-01), and GenMIS-C (Grant ANR-21-COVR-0039) projects; the Square Foundation; Grandir–Fonds de solidarité pour l’Enfance; the Fondation du Souffle; the SCOR Corporate Foundation for Science; The French Ministry of Higher Education, Research, and Innovation (Grant MESRI-COVID-19); Institut National de la Santé et de la Recherche Médicale (INSERM), REACTing-INSERM; and the University Paris Cité. P. Bastard was supported by the FRM (Award EA20170638020). P. Bastard., J.R., and T.L.V. were supported by the MD-PhD program of the Imagine Institute (with the support of Fondation Bettencourt Schueller). Work at the Neurometabolic Disease lab received funding from Centre for Biomedical Research on Rare Diseases (CIBERER) (Grant ACCI20-767) and the European Union's Horizon 2020 research and innovation program under grant agreement 824110 (EASI Genomics). Work in the Laboratory of Virology and Infectious Disease was supported by the NIH (Grants P01AI138398-S1, 2U19AI111825, and R01AI091707-10S1), a George Mason University Fast Grant, and the G. Harold and Leila Y. Mathers Charitable Foundation. The Infanta Leonor University Hospital supported the research of the Department of Internal Medicine and Allergology. The French COVID Cohort study group was sponsored by INSERM and supported by the REACTing consortium and by a grant from the French Ministry of Health (Grant PHRC 20-0424). The Cov-Contact Cohort was supported by the REACTing consortium, the French Ministry of Health, and the European Commission (Grant RECOVER WP 6). This work was also partly supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases and the National Institute of Dental and Craniofacial Research, NIH (Grants ZIA AI001270 to L.D.N. and 1ZIAAI001265 to H.C.S.). This program is supported by the Agence Nationale de la Recherche (Grant ANR-10-LABX-69-01). K.K.’s group was supported by the Estonian Research Council, through Grants PRG117 and PRG377. R.H. was supported by an Al Jalila Foundation Seed Grant (Grant AJF202019), Dubai, United Arab Emirates, and a COVID-19 research grant (Grant CoV19-0307) from the University of Sharjah, United Arab Emirates. S.G.T. is supported by Investigator and Program Grants awarded by the National Health and Medical Research Council of Australia and a University of New South Wales COVID Rapid Response Initiative Grant. L.I. reports funding from Regione Lombardia, Italy (project “Risposta immune in pazienti con COVID-19 e co-morbidità”). This research was partially supported by the Instituto de Salud Carlos III (Grant COV20/0968). J.R.H. reports funding from Biomedical Advanced Research and Development Authority (Grant HHSO10201600031C). S.O. reports funding from Research Program on Emerging and Re-emerging Infectious Diseases from Japan Agency for Medical Research and Development (Grant JP20fk0108531). G.G. was supported by the ANR Flash COVID-19 program and SARS-CoV-2 Program of the Faculty of Medicine from Sorbonne University iCOVID programs. The 3C Study was conducted under a partnership agreement between INSERM, Victor Segalen Bordeaux 2 University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also supported by the Caisse Nationale d’Assurance Maladie des Travailleurs Salariés, Direction générale de la Santé, Mutuelle Générale de l’Education Nationale, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Program “Cohortes et collections de données biologiques.” S. Debette was supported by the University of Bordeaux Initiative of Excellence. P.K.G. reports funding from the National Cancer Institute, NIH, under Contract 75N91019D00024, Task Order 75N91021F00001. J.W. is supported by a Research Foundation - Flanders (FWO) Fundamental Clinical Mandate (Grant 1833317N). Sample processing at IrsiCaixa was possible thanks to the crowdfunding initiative YoMeCorono. Work at Vall d’Hebron was also partly supported by research funding from Instituto de Salud Carlos III Grant PI17/00660 cofinanced by the European Regional Development Fund (ERDF/FEDER). C.R.-G. and colleagues from the Canarian Health System Sequencing Hub were supported by the Instituto de Salud Carlos III (Grants COV20_01333 and COV20_01334), the Spanish Ministry for Science and Innovation (RTC-2017-6471-1; AEI/FEDER, European Union), Fundación DISA (Grants OA18/017 and OA20/024), and Cabildo Insular de Tenerife (Grants CGIEU0000219140 and “Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19”). T.H.M. was supported by grants from the Novo Nordisk Foundation (Grants NNF20OC0064890 and NNF21OC0067157). C.M.B. is supported by a Michael Smith Foundation for Health Research Health Professional-Investigator Award. P.Q.H. and L. Hammarström were funded by the European Union’s Horizon 2020 research and innovation program (Antibody Therapy Against Coronavirus consortium, Grant 101003650). Work at Y.-L.L.’s laboratory in the University of Hong Kong (HKU) was supported by the Society for the Relief of Disabled Children. MBBS/PhD study of D.L. in HKU was supported by the Croucher Foundation. J.L.F. was supported in part by the Evaluation-Orientation de la Coopération Scientifique (ECOS) Nord - Coopération Scientifique France-Colombie (ECOS-Nord/Columbian Administrative department of Science, Technology and Innovation [COLCIENCIAS]/Colombian Ministry of National Education [MEN]/Colombian Institute of Educational Credit and Technical Studies Abroad [ICETEX, Grant 806-2018] and Colciencias Contract 713-2016 [Code 111574455633]). A. Klocperk was, in part, supported by Grants NU20-05-00282 and NV18-05-00162 issued by the Czech Health Research Council and Ministry of Health, Czech Republic. L.P. was funded by Program Project COVID-19 OSR-UniSR and Ministero della Salute (Grant COVID-2020-12371617). I.M. is a Senior Clinical Investigator at the Research Foundation–Flanders and is supported by the CSL Behring Chair of Primary Immunodeficiencies (PID); by the Katholieke Universiteit Leuven C1 Grant C16/18/007; by a Flanders Institute for Biotechnology-Grand Challenges - PID grant; by the FWO Grants G0C8517N, G0B5120N, and G0E8420N; and by the Jeffrey Modell Foundation. I.M. has received funding under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 948959). E.A. received funding from the Hellenic Foundation for Research and Innovation (Grant INTERFLU 1574). M. Vidigal received funding from the São Paulo Research Foundation (Grant 2020/09702-1) and JBS SA (Grant 69004). The NH-COVAIR study group consortium was supported by a grant from the Meath Foundation.Peer reviewe

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure &lt; 100 mmHg (n = 1127), estimated glomerular filtration rate &lt; 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Development of a Multibody Model for Quantification of Muscle Forces at Upper Limb as a Design Tool for Exoskeleton Synthesis

RÉSUMÉ: Les exosquelettes d'assistance sont considérés comme une solution prometteuse pour apporter un soutien continu aux personnes atteintes d'un diagnostic qui entrave les mouvements des bras. Cependant, la fonctionnalité, la sécurité et l'acceptation de ces dispositifs pourraient être améliorées par un dimensionnement basé sur l'anatomie et la physiologie de l'utilisateur. Dans ce contexte, l'objectif principal de cette recherche est de développer un outil de conception pour valider, par simulation, les performances d'un exosquelette basé sur la quantification des forces musculaires. L'outil développé est appliqué au mouvement de flexion-extension du coude (E.FE) comme cas de référence et réalisé en trois étapes. Premièrement, un système multicorps musculosquelettique du membre supérieur a été développé. Pour ce faire, les quatre principaux muscles responsables de l'E.FE ont été intégrés à un modèle ostéoarticulaire existant. Les muscles ont été modélisés par une méthode de via-points. Le modèle proposé a ensuite été validé en comparant les longueurs musculo-tendineuses et les bras de levier musculaires avec les valeurs de référence de la littérature. L'erreur quadratique moyenne relative (rRMSE) pour les longueurs musculo-tendineuses était inférieure à 2.3 % pour tous les muscles. La rRMSE pour les bras de levier musculaires a atteint respectivement 14.5 % et 21.4 % pour le brachialis et le triceps brachii. Cette erreur élevée s'explique par l'absence d'objets enveloppants dans le modèle proposé et n'a été observée que lorsque le coude est entièrement fléchi ou en extension. Le rRMSE sur les bras de levier musculaires pour les autres muscles était inférieur à 7.0 %. Deuxièmement, le modèle musculosquelettique nouvellement développé a été utilisé pour quantifier les forces musculaires avec une méthode non basée sur l'électromyographie. Cela a été fait avec trois fonctions coût différentes, à savoir Crowninshield, Forster et Wen, en formulant le processus d'optimisation comme un problème de contrôle optimal. Les forces musculaires quantifiées différaient en fonction de la fonction coût utilisée. La fonction coût de Crowninshield a donné lieu aux forces musculaires les plus faibles dans l'ensemble, tandis que celles de Forster et de Wen étaient plus élevées, car elles incluent toutes deux la co-contraction musculaire dans leur formulation. Le rRMSE entre le couple E.FE issu de la dynamique inverse et le couple E.FE calculé à partir des forces musculaires était inférieur à 5 % pour le mouvement E.FE. Le rRMSE était plus élevé pendant les tâches fonctionnelles mais restait inférieur à 10 %. Troisièmement, un exosquelette a été intégré au modèle musculosquelettique pour valider ses performances par simulation. L'exosquelette était capable de compenser 80 % du couple E.FE de l'utilisateur produit par les muscles. Cependant, la réduction du couple articulaire ne s'est pas traduite par une réduction des forces musculaires réelles. En effet, les forces musculaires quantifiées ont montré une augmentation avec l'exosquelette. Cette augmentation des forces musculaires quantifiées se produit très probablement pour compenser les forces ou les couples parasites introduits par le désalignement de l'exosquelette au niveau de l'articulation du coude. Pour conclure, cette recherche a introduit un nouveau modèle musculosquelettique du membre supérieur pour la quantification des forces musculaires qui peut être utilisé comme outil de validation pour la synthèse d'exosquelettes. Cette approche est importante car elle permet d'identifier rapidement les faiblesses de l'architecture d'un exosquelette. Les travaux futurs devraient se concentrer sur l'intégration des muscles de l'épaule et de l'avant-bras au modèle afin qu'il puisse être utilisé sur un plus large éventail de mouvements du membre supérieur et d'articulations de l'exosquelette. ABSTRACT: Assistive exoskeletons are emerging as a promising solution to provide continuous support for people affected by a diagnosis that impairs arm movements. However, functionality, safety and acceptance of these devices could be improved with a sizing based on the user's anatomy and physiology. In this context, the main objective of this research is to develop a design tool to validate, through simulation, the performance of an exoskeleton based on the quantification of muscle forces. The developed tool is applied to the movement of elbow flexion-extension (E.FE) as a benchmark-case and achieved through three steps. Firstly, a musculoskeletal multibody system of the upper limb was developed. This was achieved by integrating the four main muscles responsible for E.FE to an existing osteoarticular model. The muscles were modeled with a via-points method. The proposed model was then validated by comparing the musculo-tendinous lengths and muscle moment-arms with reference values from the literature. The relative root mean square error (rRMSE) for musculo-tendinous lengths was below 2.3 % for all muscles. The rRMSE for muscle moment arms reached respectively 14.5 % and 21.4 % for the brachialis and the triceps brachii. This high error is explained by the absence of wrapping objects in the proposed model and was only observed when the elbow is fully flexed or extended. The rRMSE on muscle moment arms for the other muscles was below 7.0 %. Secondly, the newly developed musculoskeletal model was used to quantify the muscle forces with a non-electromyography-based method. This was done with three different cost functions, namely Crowninshield, Forster, and Wen, by formulating the optimization process as an optimal control problem. The quantified muscles forces differed depending on the cost function used. Crowninshield cost function resulted in the lowest muscle forces overall while Forster and Wen were higher since they both include muscle co-contraction in their formulation. The rRMSE between the E.FE torque from inverse dynamics and the E.FE torque computed from muscle forces was below 5 % for the E.FE movement. The rRMSE was higher during functional tasks but stayed below 10 %. Thirdly, an exoskeleton was integrated to the musculoskeletal model to validate its performance through simulation. The exoskeleton was able to compensate for 80 % of the user's E.FE torque produced by the muscles. However, the reduction of the joint torque did not translate into a reduction of the actual muscle forces. Indeed, the quantified muscle forces showed an increase with the exoskeleton. This increase in the quantified muscle forces is most likely happening to compensate for the spurious forces or torques introduced by the exoskeleton's misalignment at the elbow joint. To conclude, this research introduced a new musculoskeletal model of the upper limb for muscle forces quantification that can be used as a validation tool for exoskeleton synthesis. This approach is important as it helps to rapidly identify weaknesses in the exoskeleton architecture. Future work should focus on integrating shoulder and forearm muscles to the model so it can be used on a wider range of upper limb movements and exoskeleton joints