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Bakgrund: Det har tidigare undersökts hur muskelaktivering skiljer sig åt vid en unilateral (UL) och bilateral (BL) knäextension och observerats ett så kallat “Bilateral limb deficit” (BLD). Forskning saknas däremot kring hur BLD skiljer sig beroende på styrketräningserfarenhet. Syfte: Undersöka om elektromyografisk amplitud (EMGa) skiljer sig i m. vastus lateralis (VL) vid isokinetisk maximal knäextension mellan träningsgrupp (TrG) och icke-träningsgrupp (i-TrG). Metod: 6 kvinnor och 6 män rekryterades till studien. Studiedeltagarna delades i två grupper där en grupp hade utövat styrketräning regelbundet och den andra inte. EMGa och kraft mättes på deltagarna under en maximal isokinetisk knäextension i dynamometer. 6 mätningar utfördes på såväl dominant ben (DB) som icke-dominant ben (i-DB) vid 6 UL och 3 BL knäextensioner. Variablerna analyserades och jämfördes mellan grupperna. Resultat: Den genomsnittliga procentuella skillnaden i TrG var på DB 0% (±12.9) och på i-DB 0.7% (±10.8). I i-TrG var den genomsnittliga procentuella skillnaden på DB 21.3 (±11.9) och på i-DB 18.8 (±14.7). Den procentuella skillnaden mellan grupperna vid BL jämfört med UL muskelaktivering var signifikant i både DB (p=0.014) och i-DB (p=0.035). Vid BL knäextension jämfört med total UL kraft uppmätte TrG 1.5 % mindre kraft och i-TrG 17.5 % mindre kraft vid BL knäextension jämfört med total UL kraft. Kraftmomentet och EMGa visade stor korrelation (r=0.831, p=0.001). Konklusion: Studiens primära fynd indikerar att styrketräning minskar BLD. Detta har troligen sin grund i neural adaption men bekräftande orsak är oklar.

En este trabajo se va a evaluar la actividad electromiográfica de los músculos encargados del movimiento del brazo durante tareas de miembro superior utilizando un robot colaborativo UR3. Se analizarán las señales EMG para extraer métricas relacionadas con la fatiga muscular y determinar que músculos se implican más o menos en el movimiento realizado y como son afectados por el aumento de la resistencia del sistema.

[EN] The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective., QC 20240708

(1) Background: Pelvic-floor-muscle (PFM) activation acts synergistically with multiple muscles while performing functional actions in humans. The purpose of this study was to characterize the activity of the PFMs and gluteus medius (GM) while walking and running in physically active nulliparous females. (2) Methods: The peak and average amplitude of maximal voluntary contractions (MVCs) during 60 s of walking (5 and 7 km/h) and running (9 and 11 km/h) were measured with electromyography of the GM and PFMs in 10 healthy female runners. (3) Results: The activation of both muscles increased (p < 0.001) while walking and running. The MVC of the GM was reached when walking and tripled when running, while the PFMs were activated at half their MVC when running. The global ratio of the GM (75.3%) was predominant over that of the PFMs (24.6%) while static and walking. The ratio reached 9/1 (GM/PFM) while running. (4) Conclusion: The GM and PFMs were active while walking and running. The GM's MVC tripled at high speeds, while the PFMs reached only half of their maximum contraction.

El hareketinin sınıflandırılması, özellikle inme rahatsızlığı geçiren kişilerde nörorehabilitasyon amaçlı beyin bilgisayar arayüzü (BBA) modellerinin geliştirilmesinde büyük önem arz etmektedir. Ancak, el hareketi odaklı BBA modellerinin geliştirilmesinde kullanılan kas ve beyin aktivitesi ölçüm modalitelerinin tek başlarına kullanılmasında, nörolojik adaptasyon ve bazı hasta gruplarının nöromusküler hastalık barındırması gibi çeşitli problemler bulunmaktadır. Bu çalışmada bir kavrama kuvveti görevi aracılığı ile gerçekleştirilen el hareketinin sonucu elde edilen işlevsel yakın kızılaltı spektroskopisi (iYKAS) ve elektromiyografi (EMG) sinyalleri kullanılarak el hareketinin sınıflandırılması gerçekleştirilmiştir. Bu sinyallerden çıkartılan öznitelikler, L1 norm tabanlı bir destek vektör makinesi (DVM) ile seçildikten sonra, K-en yakın komşuluk, doğrusal ve radyal temelli DVM, Gradyan Artırma, Adaboost, Naive Bayes, Doğrusal Diskriminant, Kuadratik Diskriminant ve Lojistik regresyon sınıflandırıcılarına verilmiştir. Sınıflandırıcıların başarımı, bir katılımcıyı dışarıda bırak (leave-one-subject-out) çapraz geçerliliği uygulanarak gerçekleştirilmiştir. Sınıflandırıcılar arasında en yüksek doğruluk yüzdesi, iYKAS ve EMG odaklı özniteliklerden faydalanılarak, Doğrusal Diskriminant metodu ile %84 olarak bulunmuştur. Sonuçlarımız bize işlevsel yakın kızılaltı spektroskopisi ve elektromiyografi verilerinin el hareketinin sınıflandırılmasında kullanılabileceğini ve bunun BBA sistemlerine de entegre edilebileceğini ortaya koymaktadır., Classification of hand movement is of great importance in the development of brain-computer interface (BCI) models for neurorehabilitation, particularly in stroke patients. However, there are various problems in the application of muscle and brain activity measurement modalities used in the performance of hand movement-oriented BCI systems, such as neurological adaptation and neuromuscular disease in some patient groups. In this study, classification of hand movement was performed using functional near infrared spectroscopy (fNIRS) and electromyography (EMG) signals obtained as a result of hand movement performed through a grip strength task. Features extracted from these signals are given to K-nearest neighbor, linear and radial basis SVM, Gradient Boost, Adaboost, Naive Bayes, Linear Discriminant, Quadratic Discriminant and Logistic regression classifiers after they are selected with an L1 norm-based support vector machine (SVM). The performance of the classifiers was achieved by applying the leaveone-subject-out cross-validation. Among the classifiers, the highest percentage of accuracy was found to be 84% with the Linear Discriminant method, using iYKAS and EMG focused features. Our results reveal that functional near-infrared spectroscopy and electromyography data can be used to classify hand movement and can be integrated into BCI systems.

The active control of the lumbar musculature provides a stable platform critical for postures and goal-directed movements. Voluntary and perturbation-evoked motor commands can recruit individual lumbar muscles in a task-specific manner according to their presumed biomechanics. Here, we investigated the vestibular control of the deep and superficial lumbar musculature. Ten healthy participants were exposed to noisy electrical vestibular stimulation while balancing upright with their head facing forward, left, or right to characterize the differential modulation in the vestibular-evoked lumbar extensor responses in generating multidirectional whole body motion. We quantified the activation of the lumbar muscles on the right side using indwelling [deep multifidus, superficial multifidus, caudal longissimus (L4), and cranial longissimus (L1)] and high-density surface recordings. We characterized the vestibular-evoked responses using coherence and peak-to-peak cross-covariance amplitude between the vestibular and electromyographic signals. Participants exhibited responses in all lumbar muscles. The vestibular control of the lumbar musculature exhibited muscle-specific modulations: responses were larger in the longissimus (combined cranio-caudal) compared with the multifidus (combined deep-superficial) when participants faced forward (P < 0.001) and right (P = 0.011) but not when they faced left. The high-density surface recordings partly supported this observation: the location of the responses was more lateral when facing right compared with left (P < 0.001). The vestibular control of muscle subregions within the longissimus or the multifidus was similar. Our results demonstrate muscle-specific vestibular control of the lumbar muscles in response to perturbations of vestibular origin. The lack of differential activation of lumbar muscle subregions suggests the vestibular control of these subregions is co-regulated for standing balance. NEW & NOTEWORTHY We inves

This study analyzed muscle activity during the stand-up paddle stroke, considering the paddling side and the adjacent and opposing muscles relative to the position of the arms during paddling. Methods: Fourteen male paddleboarders performed three trials covering 195 m in which surface electromyography of the upper trapezius, biceps brachii, triceps brachii, tibialis anterior, and gastrocnemius medialis were recorded (four-cycle strokes on each side). The data were processed according to percentage of maximum voluntary contraction (% MVC). The MVC activation values (mu V) for each muscle were then calculated and presented as percentage MVC (% MVC). Results: The recovery phase accounted for 60% of the paddle cycle, while the pull phase represented 39%. During right-side paddling, higher % MVC was found in the opposite-side upper trapezius (24.35%, p < 0.01) during the pulling phase and in the adjacent biceps brachii (8.36%, p < 0.03) during the recovery phase. In left-side paddling, greater % MVC was found in the opposite-side upper trapezius (27.60%, p < 0.01) during the pulling phase and in the opposite-side triceps brachii (42.25%, p < 0.04) during the recovery phase. Furthermore, the pulling phase exhibited higher MVC in the opposite-side upper trapezius compared to the recovery phase, both in the right-side (24.35%, p < 0.03) and left-side (27.60%, p < 0.01) paddling. Conclusions: these findings help establish the muscular activity of both sides of the paddling technique and the differences between the upper and lower limbs.

Every year, 250,000 people worldwide suffer a spinal cord injury (SCI) that leaves them with chronic paraplegia - permanent loss of ability to move their legs. SCI interrupts axons passing along the spinal cord, thereby isolating motor neurons from brain inputs. To date, there are no effective treatments that can reconnect these interrupted axons. In a recent breakthrough, .NeuroRestore developed the STIMO neuroprosthesis that can restore walking after paralyzing SCI using Epidural Electrical Stimulation (EES) of the lumbar spinal cord. Yet, the calibration of EES requires highly trained personnel and a vast amount of time, and the mechanism by which EES restores movement is not fully understood. In this master thesis, we propose to address this issue using modeling combined with Artificial Neural Networks (ANNs). To do so, we introduce the CyberSpine model to predict EES-induced motor response. The implementation of the model relies on the construction of a multipolar basis of solution of the Poisson equation which is then coupled to an ANN trained against actual data of an implanted STIMO user. Furthermore, we show that our CyberSpine model is particularly well adapted to extract biologically relevant information regarding the efficient connectivity of the patient’s spine. Finally, a user-friendly interactive visualization software is built., Varje år drabbas 250 000 människor i hela världen av en ryggmärgsskada som ger dem kronisk paraplegi - permanent förlust av förmågan att röra benen. Vid en ryggmärgsskada bryts axonerna som passerar längs ryggmärgen, vilket isolerar de motoriska neuronpoolerna från hjärnans ingångar. Hittills finns det inga effektiva behandlingar som kan återansluta dessa avbrutna axoner. NeuroRestore utvecklade nyligen neuroprotesen STIMO som kan återställa gångförmågan efter förlamande ryggmärgsskada med hjälp av epidural elektrisk stimulering (EES) av ländryggmärgen. Kalibreringen av EES-stimuleringar kräver dock högutbildad personal och mycket tid, och den mekanism genom vilken EES återställer rörelse är inte helt klarlagd. I denna masteruppsats föreslår vi att vi tar itu med denna fråga med hjälp av modellering i kombination med artificiell intelligens. För att göra detta introducerar vi CyberSpine-modellen, en modell som kan förutsäga EES-inducerad motorisk respons. Implementeringen av modellen bygger på konstruktionen av en multipolär bas för lösning av Poisson-ekvationen som sedan kopplas till ett artificiellt neuralt nätverk som tränas mot faktiska data från en implanterad STIMO-deltagare. Dessutom visar vi att vår CyberSpine-modell är särskilt väl anpassad för att extrahera biologiskt relevant information om den effektiva anslutningen av patientens ryggrad. Slutligen bygger vi en användarvänlig interaktiv visualiseringsprogramvara.

In the last decade, the development of classification models through machine learning for the control of multifunctional prosthetic devices has been increasing. Electromyography (EMG) are recordings produced by muscle fibers naturally when performing movements; if modeled, they could play a more active role in this type of control. These signals are used to control devices/applications. The problem with these models is the stochastic nature of the signal, the variability between subjects and the inherent cross-communication that makes them inaccurate when faced with a high number of movements. The stochastic nature and variability of the signal are already widely studied, however, there are still no definitive results that describe generalizable movement classification models. Here, two databases available on the CapgMyo network and the Ninapro project are studied, their characteristics are evaluated, with the objective of investigating the variability of the muscle signal between subjects, the factors that modify it and how the use of analysis affects principal components (PCA) and independent component analysis (ICA) to EMG information in classification models. A comparison was made between the results in terms of recognition percentages of classic machine learning methods such as linear discriminant analysis (LDA) and quadratic analysis (QDA) using transformation techniques to new spaces introducing the possibility of performing a dimensionality reduction. with PCA and ICA, algorithms usually used to solve problems such as blind source separation (BSS), which is applicable to the phenomenon presented in muscle signals and their acquisition through surface electrodes. The results can be evaluated through the recognition percentage of the classification models created, these show that for raw EMG signals the PCA and ICA methods are useful to perform a reduction in the dimensionality of the data without providing a significant increase in the recognition percentages, En la última década el desarrollo de modelos de clasificación a través de aprendizaje automático para control de dispositivos protésicos multifuncionales ha ido en aumento. La electromiografía (EMG) son registros producidos por las fibras musculares de forma natural al realizar movimientos, de modelarse podrían tener un papel de forma más activa en este tipo de control. Estas señales son utilizadas para control de dispositivos/aplicaciones, el problema con estos modelos es la naturaleza estocástica de la señal, la variabilidad entre sujetos y la comunicación cruzada inherente que los vuelve inexactos ante un número alto de movimientos. La naturaleza estocástica y la variabilidad de la señal ya son ampliamente estudiadas, sin embargo, no existen aún resultados definitivos que describan modelos de clasificación de movimientos generalizables. Aquí se estudian dos bases de datos disponibles en la red CapgMyo y the Ninapro project, se evalúan las características de estas, teniendo como objetivo investigar la variabilidad de la señal muscular entre sujetos, los factores que la modifican y como afecta el uso de análisis de componentes principales (PCA) y el análisis de componentes independientes (ICA) a la información del EMG en modelos de clasificación. Se realizó una comparación entre los resultados en términos de porcentajes de reconocimiento de métodos clásicos de aprendizaje automático como el análisis discriminante lineal (LDA) y el cuadrático (QDA) utilizando técnicas de trasformación a nuevos espacios introduciendo la posibilidad de realizar una reducción de la dimensionalidad con PCA e ICA, algoritmos usualmente utilizados para resolver problemas como la separación ciega de fuentes (BSS) que es aplicable al fenómeno presentado en señales musculares y su adquisición a través de electrodos superficiales. Los resultados pueden evaluarse a través del porcentaje de reconocimiento de los modelos de clasificación creados, estos muestran que para señales crudas de EMG lo

Beyond recognizing words, parsing sentences, building situation models, and other cognitive accomplishments, language comprehension always involves some degree of emotion too, with or without awareness. Language excites, bores, or otherwise moves us, and studying how it does so is crucial. This chapter examines the potential of facial electromyography (EMG) to study language-elicited emotion. After discussing the limitations of self-report measures, we examine various other tools to tap into emotion, and then zoom in on the electrophysiological recording of facial muscle activity. Surveying psycholinguistics, communication science, and other fields, we provide an exhaustive qualitative review of the relevant facial EMG research to date, exploring 55 affective comprehension experiments with single words, phrases, sentences, or larger pieces of discourse. We discuss the outcomes of this research, and evaluate the various practices, biases, and omissions in the field. We also present the fALC model, a new conceptual model that lays out the various potential sources of facial EMG activity during language comprehension. Our review suggests that facial EMG recording is a powerful tool for exploring the conscious as well as unconscious aspects of affective language comprehension. However, we also think it is time to take on a bit more complexity in this research field, by for example considering the possibility that multiple active generators can simultaneously contribute to an emotional facial expression, by studying how the communicator’s stance and social intention can give rise to emotion, and by studying facial expressions not just as indexes of inner states, but also as social tools that enrich everyday verbal interactions.

According to statistics, approximately 160,000 people in Malaysia, out of the current population of 32 million, need prosthetic or orthotic equipment. For individuals who have experienced upper extremity amputations, significant challenges are posed by the loss of functionality and the desire for a cosmetically appealing solution. To address this issue, a cost-effective prosthetic hand was proposed and developed. An overview of existing prosthetic hands is also offered, with an emphasis on cost-effectiveness, challenges, strengths, and weaknesses. The developed prosthetic hand incorporates a practical and underactuated finger mechanism. It is equipped with controllers based on EMG sensors to ensure that optimal responses are achieved during the grasping and releasing of objects. A suitable motor was carefully chosen to facilitate effective grasping and ungrasping activities. The proposed design was realized using SolidWorks and a 3D Printer. The capabilities of the prosthetic hand were demonstrated through a series of tests involving various objects, including pliers, a screwdriver, and a phone. The results indicate that objects of different sizes and shapes can be effectively grasped and ungrasped by the prosthetic hand. The unique bending angles in each finger result from the way tendons are connected via flexible cords and fishing lines to the servo motor. This design allows for a dynamic response based on the user's muscle flex and strength. The affordability of this cost-effective prosthetic hand demonstrates its potential as a practical and viable solution for amputees aiming to restore their grasping functionalities.

El avance de la medicina dirige las técnicas de intervención hacia aquellas mínimamente invasivas (CMI). Su instrumental, en ocasiones, carece de ergonomía lo que puede provocar lesiones al personal sanitario. El objetivo de este trabajo es evaluar, a través de biomarcadores obtenidos de registros electromiográficos de superficie (sEMG), dos herramientas de laparoscopia: un prototipo y otra empleada en la práctica clínica habitual. Para ello, se dispone de registros de sEMG de 17 sujetos de los músculos bíceps, deltoides, trapecio y flexor radial del carpo, obtenidos mediante un protocolo isométrico. Se han computado parámetros temporales, espectrales y de complejidad como biomarcadores de la actividad muscular determinando la existencia de diferencias estadísticamente significativas entre herramientas y posiciones para cada uno de los biomarcadores. Se ha completado el estudio a través de un Joint Analysis of Spectrum and Amplitude (JASA) tradicional y con una nueva propuesta que considera otros parámetros. Finalmente, los biomarcadores parecen apuntar hacia una menor tendencia a la aparición de fatiga muscular con la herramienta prototipo para el bíceps, deltoides y trapecio, y con la herramienta control para el flexor radial del carpo. Los nuevos análisis bivariados propuestos permiten una caracterización más exhaustiva del estado muscular.

Background: The high incidence of anterior cruciate ligament (ACL) injuries in sports suggests an involvement of both biomechanical and neurocognitive risk factors. Athletes are constantly exposed to challenging sports scenarios, which are often characterised by high-intensity movements combined with a multi-stimuli environment and continuous psychological pressure. Post-injury loss of knee proprioception and long-term injury-associated neuroplasticity arguably place an athlete in a disadvantage when coping with such situations when returning to sports (RTS). This is postulated to contribute to a high rate of re-injuries, seen despite achieving RTS eligibility. Psychological factors such as re-injury fears and anxieties are also suggested to influence central sensorimotor processing and to therefore play a role in the generation and control of functional movements. Their assessment is however based on suboptimal tools, particularly when administered to the athletic population. In general, current clinical assessments focus primarily on coarse outcome measures while disregarding aspects such as multi-joint control and the influence of psychological aspects on motor performance. This thesis focuses on the role of proprioception and re-injury anxiety on functional movement control following ACL injury and reconstruction (ACLR), with implications for risks of re-injury. Methods: This thesis is comprised of four cross-sectional studies (Papers I-IV), that stem from two data collections performed in a motion analysis laboratory. Paper I introduces a novel obstacle clearance test aimed to functionally assess proprioception and sensorimotor control. The goal of the test was to cross an obstacle, downward vision occluded, aiming for minimal foot clearance. Individuals following ACLR and rehabilitation were compared to both mildly active uninjured persons (CTRL) and elite athletes (ATH). A kinematic analysis, using 3D motion capture, included estimates of lower limb movement, Bakgrund: Den höga förekomsten av främre korsbandsskador (ACL-skada) i sport tyder på en inblandning av både biomekaniska och neurokognitiva riskfaktorer. Idrottare utsätts ständigt för utmanande idrottsscenarier som ofta kännetecknas av komplexa rörelser i en miljö med många olika stimuli och även psykologisk press. Förlust av knäproprioception efter ligamentskadan och långvariga neuroplastiska förändringar utgör en utmaning för idrottare i sådana situationer efter återgång till idrott (RTS). Detta antas bidra till den höga frekvensen av sekundära skador, trots att man uppfyllt kriterierna för RTS. Psykologiska faktorer som t.ex. rädsla eller oro för sekundära skador antas också påverka central sensorimotorisk bearbetning och därför spela en roll i hur funktionella rörelser genereras och kontrolleras. Bedömning av rädsla för skada är dock en utmaning och baseras vanligen på suboptimala verktyg, särskilt när de används för elitidrottare. Nuvarande kliniska bedömningar utgår främst från grova utfallsmått samtidigt som man bortser från flerledskontroll och psykologiska aspekter på motorisk prestation. Denna avhandling fokuserar på betydelsen av proprioception och rädsla/oro för sekundär skada med avseende på funktionell rörelsekontroll efter ACL-skada som behandlats med rekonstruktion (ACLR) och rehabilitering. Metoder: Avhandlingen består av fyra tvärsnittsstudier (delarbeten I-IV), som härrör från två datainsamlingar utförda i ett rörelseanalyslaboratorium. Delarbete I introducerar ett nytt test där man kliver över ett hinder med så litet avstånd som möjligt och utan information från nedre synfältet. Testet syftar till att funktionellt bedöma proprioception och sensorimotorisk kontroll. Individer med ACLR och rehabilitering jämfördes med två kontrollgrupper av icke-skadade individer; moderat aktiva personer (CTRL) samt elitidrottare (ATH). En kinematisk analys, med 3D motion capture, inkluderade rörelsenoggrannhet, variabilitet och symmetri i nedre extremiteterna. D

Objective: To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio. Methods: Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG Results: Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations. Conclusions: Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods. Significance: The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique.

Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. Nevertheless, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN (Adaptive Domain Adversarial Neural Network), which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, this work provides the first topological data analysis of EMG-based gesture recognition for the characterization of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. In the later layers, the learned features are inclined to instead adopt a one-vs.-all strategy for a given class. Furthermore, by using convolutional network visualization techniques, it is revealed that learned features actually tend to ignore the most activated channel during contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and han

Traditional input methods to interface with computer systems prove to be challenging for individuals with amputations or paralysis. Although several brain-machine interfaces were developed to address this problem, their invasive nature prevents widespread adoption. Alternatively, developing interfaces using non-invasive signals has been shown to be effective but they require large, non-intuitive gestures to function. In this work, we propose a framework to decode the subtle finger movements that occur naturally during typing via analyzing non-invasive EMG signals. Here, we establish synchronized communication with an amplifier to get signal recordings, perform signal preprocessing and utilize deep learning architectures for feature extraction and classification. Our approach achieves a within-session accuracy of up to 89.23% in detecting individual finger movements during a randomized typing task, with an average accuracy of 77.64% across all sessions. The time needed for classification is 4.16 ms per sample, making our framework suitable for real-time operation. Our framework demonstrates the possibility of identifying finger movements during typing in real-time using non-invasive EMG signals and provides a starting point for future work to allow individuals with amputations or disabilities to communicate effectively with computers.

IntroductionThe paralysis that occurs after a spinal cord injury, particularly during the early stages of post-lesion recovery (∼6 weeks), appears to be attributable to the inability to activate motor pools well beyond their motor threshold. In the later stages of recovery, however, the inability to perform a motor task effectively can be attributed to abnormal activation patterns among motor pools, resulting in poor coordination.MethodWe have tested this hypothesis on four adult male Rhesus monkeys (Macaca mulatta), ages 6-10 years, by recording the EMG activity levels and patterns of multiple proximal and distal muscles controlling the upper limb of the Rhesus when performing three tasks requiring different levels of skill before and up to 24 weeks after a lateral hemisection at C7. During the recovery period the animals were provided routine daily care, including access to a large exercise cage (5' × 7' × 10') and tested every 3-4 weeks for each of the three motor tasks.ResultsAt approximately 6-8 weeks the animals were able to begin to step on a treadmill, perform a spring-loaded task with the upper limb, and reaching, grasping, and eating a grape placed on a vertical stick. The predominant changes that occurred, beginning at ∼6-8 weeks of the recovery of these tasks was an elevated level of activation of most motor pools well beyond the pre-lesion level.DiscussionAs the chronic phase progressed there was a slight reduction in the EMG burst amplitudes of some muscles and less incidence of co-contraction of agonists and antagonists, probably contributing to an improved ability to selectively activate motor pools in a more effective temporal pattern. Relative to pre-lesion, however, the EMG patterns even at the initial stages of recovery of successfully performing the different motor tasks, the level of activity of most muscle remained higher. Perhaps the most important concept that emerges from these data is the large combinations of adaptive strategies in the re

Muscle fatigue is a severe problem for elite athletes, and this is due to the long resting times, which can vary. Various mechanisms can cause muscle fatigue which signifies that the specific muscle has reached its maximum force and cannot continue the task. This thesis was about surveying and exploring state-of-the-art methods and systematically, theoretically, and practically testing the applicability and performance of more recent machine learning methods on an existing EMG to muscle fatigue pipeline. Several challenges within the EMG domain exist, such as inadequate data, finding the most suitable model, and how they should be addressed to achieve reliable prediction. This required approaches for addressing these problems by combining and comparing various state-of-the-art methodologies, such as data augmentation techniques for upsampling, spectrogram methods for signal processing, and transfer learning to gain a reliable prediction by various pre-trained CNN models. The approach during this study was to conduct seven experiments consisting of a classification task that aims to predict muscle fatigue in various stages. These stages are divided into 7 classes from 0-6, and higher classes represent a fatigued muscle. In the tabular part of the experiments, the Decision Tree, Random Forest, and Support Vector Machine (SVM) were trained, and the accuracy was determined. A similar approach was made for the spectrogram part, where the signals were converted to spectrogram images, and with a combination of traditional- and intelligent data augmentation techniques, such as noise and DCGAN, the limited dataset was increased. A comparison between the performance of AlexNet, VGG16, DenseNet, and InceptionV3 pre-trained CNN models was made to predict differences in jump heights. The result was evaluated by implementing baseline classifiers on tabular data and pre-trained CNN model classifiers for CWT and STFT spectrograms with and without data augmentation. The evaluation o

This study aimed to investigate the influence of head position on the electromyographical activity of the core muscles during the prone plank exercise. Twenty healthy participants were enrolled in this study. Muscle activation was registered using surface electromyography in the rectus abdominis, external oblique, internal oblique, and the lumbar portion of erector spinae. Three plank conditions were randomly evaluated, varying the position of the cranio-cervical segment during the plank performance (neutral, flexion and extension). The activation of each individual examined muscle and the overall core muscle activity (Total Intensity), as well as the ratings of perceived exertion were analysed with statistical significance set at p < 0.05. Flexion revealed significantly higher Total Intensity values compared to neutral and extension (p < 0.001; effect size (ES) > 0.90). The rectus abdominis presented larger activation in flexion compared to the other variations (p < 0.05; ES > 0.70). Flexion elicited a greater response for both sides of external oblique when compared to neutral and extension, and also for both sides of internal oblique compared to the other conditions (p < 0.05; ES > 0.70). Both flexion and extension reported higher exertion rating values compared to neutral (p < 0.05; ES > 0.90). Head position influenced the electromyographical activation of core musculature, showing highest values when performing the plank in a head flexion.

Postural control depends on attentional resources besides automatic processes. The dualtask paradigm is a possible approach to analyzing the interference and performance between motor and/or cognitive tasks. Various studies showed that, when individuals simultaneously perform two tasks, the postural stability can decline during a dual-task compared with a single-task due to the attentional resources required performing the tasks. However, little is known about the cortical and muscular activity pattern during dual-task performance. Therefore, this study aims to analyze the muscular and prefrontal activity under dual-task performance in healthy young adults. Thirty-four healthy young adults (mean age ± SD = 22.74 ± 3.74 years) were recruited to perform a postural task (standing posture) and a dual-task (maintaining standing posture while performing a cognitive task). Lower-limb muscle activity was bilaterally collected from five muscles using surface electromyography (sEMG), and the co-contraction index (CCI) was also calculated for selected muscle pairings. The oxy- and deoxyhemoglobin concentrations (prefrontal cortex activity) were recorded using functional near-infrared spectroscopy (fNIRS). Data were compared between single- and dual-task performance. Prefrontal activity increased (p < 0.05), and muscle activity decreased in most analyzed muscles (p < 0.05), from the single-task to cognitive dual-task performing. The co-contraction index patterns changed from single- to dual-task conditions in most selected muscle pairs (p < 0.05). We conclude that the cognitive task negatively interfered with motor performance once the muscle activity decreased and the prefrontal cortex activity increased under a dual-task, suggesting that young adults prioritized cognitive task performance, and they allocated more attentional resources to the cognitive task over the motor performance. Understanding the neuromotor changes can help adopt a better clinical practice to prevent inj

Considering the stochastic nature of electromyographic (EMG) signals, nonlinear methods may be a more accurate approach to study intermuscular coordination than the linear approach. The aims of this study were to assess the coordination between two ankle plantar flexors using EMG by applying the causal decomposition approach and assessing whether the intermuscular coordination is affected by the slope of the treadmill. The medial gastrocnemius (MG) and soleus muscles (SOL) were analyzed during the treadmill walking at inclinations of 0°, 5°, and 10°. The coordination was evaluated using ensemble empirical mode decomposition, and the causal interaction was encoded by the instantaneous phase dependence of time series bi-directional causality. To estimate the mutual predictability between MG and SOL, the cross-approximate entropy (XApEn) was assessed. The maximal causal interaction was observed between 40 and 75 Hz independent of inclination. XApEn showed a significant decrease between 0° and 5° (p = 0.028), between 5° and 10° (p = 0.038), and between 0° and 10° (p = 0.014), indicating an increase in coordination. Thus, causal decomposition is an appropriate methodology to study intermuscular coordination. These results indicate that the variation of loading through the change in treadmill inclination increases the interaction of the shared input between MG and SOL, suggesting increased intermuscular coordination. Graphical abstract: [Figure not available: see fulltext.].

Background In injury prevention or rehabilitation programs, exercises that facilitate enhanced hamstring activity may be beneficial when aiming to enhance knee joint stability during movements in sports with higher risk of acute knee injury. Information about neuromuscular activation of the hamstring muscles in commonly used exercises may improve exercise selection and progression in programs for knee injury prevention or rehabilitation. Purpose To investigate (1) how balance devices with progressing degrees of instability influence the activity of muscles controlling the knee joint in typical balance exercises with different demands on postural control, and (2) if any between-sex differences exist. Study design Cross-sectional study. Methods Twenty habitually active healthy adults (11 males) participated in this cross-sectional study. Single-leg stance, single-leg squat and single-leg landing were performed on the floor and two different balance devices imposing various levels of challenge to postural control. Three-dimensional motion analysis was used to obtain hip and knee joint angles, and as primary outcomes, and peak normalized EMG activity from the hamstrings and quadriceps muscles was measured for comparison between exercises. Results The more challenging in terms of maintaining stable balance the devices were, the higher hamstring muscle activity levels were observed. There was a clear progression across balance devices from single-leg stance to single-leg squat and further to single-leg landing displaying increasing hamstring activity levels. The change in medial hamstring activity across all devices when changing from single-leg squat to single-leg landing was significantly higher for the female participants than for the males reaching a higher level of activity. Conclusion The muscle activity of the hamstrings and quadriceps increased when the motor task was more dynamic. Specifically, single-leg landings were effective in increasing the hamstring mus

[Resumen] La rehabilitación es una herramienta esencial que ayuda a las personas a restaurar la movilidad en las extremidades afectadas por diversas afecciones, como enfermedades neurológicas. Las terapias convencionales, que incluyen terapia ocupacional, física y del habla, se han mejorado con nuevas tecnologías, como sistemas robóticos asistidos y juegos de realidad virtual y aumentada, para aumentar la participación y, en consecuencia, la efectividad. Esta investigación se centra en la implementación de un dispositivo portátil de sensores de electromiograma (EMG) de ocho canales, el brazalete Mindrove, para el reconocimiento de gestos. El objetivo es desarrollar un modelo clasificador utilizando el algoritmo de Máquinas de Vectores de Soporte (SVM) para distinguir ocho gestos diferentes de la mano y aplicarlo en un sistema de reconocimiento de gestos. El estudio demuestra la viabilidad de este sistema de reconocimiento y explora la aplicación potencial de esta tecnología en juegos interactivos de Unity para terapia de rehabilitación. Los resultados muestran una precisión prometedora en la clasificación del modelo y se necesita más investigación para abordar los desafíos relacionados con la especificidad del usuario y la precisión del reconocimiento de gestos. El trabajo futuro implica ampliar el repertorio de gestos reconocidos, incorporar datos adicionales del sensor y explorar técnicas de extracción de características más avanzadas para mejorar el rendimiento general del sistema de reconocimiento de gestos en terapias de rehabilitación., [Abstract] Rehabilitation is an essential tool that aids individuals in restoring mobility in limbs affected by various conditions, such as neurological diseases. Conventional therapies, including occupational, physical, and speech therapy, have been improved by new technologies, such as assistive robotic systems, along with virtual and augmented reality games, to enhance engagement and, consequently, effectiveness. This research focuses on implementing an eight-channel electromyogram (EMG) wearable sensor device, Mindrove armband, for gesture recognition. The objective is to develop a classifier model using the Support Vector Machine (SVM) algorithm to distinguish eight different hand gestures and apply it in a gesture recognition system. The study demonstrates the feasibility of this recognition system and explores the potential application of this technology in interactive Unity games for rehabilitation therapy. The results show promising accuracy in model classification, and further research is needed to address challenges related to user specificity and gesture recognition accuracy. Future work involves expanding the repertoire of recognized gestures, incorporating additional sensor data, and exploring more advanced feature extraction techniques to enhance the overall performance of the gesture recognition system in rehabilitation therapies.

Previous studies have identified a ‘defensive graded field’ in the peripersonal front space where potential threatening stimuli induce stronger blink responses, mainly modulated by top–down mechanisms, which include various factors, such as proximity to the body, stimulus valence, and social cues. However, very little is known about the mechanisms responsible for representation of the back space and the possible role of bottom–up information. By means of acoustic stimuli, we evaluated individuals’ representation for front and back space in an ambiguous environment that offered some degree of uncertainty in terms of both distance (close vs. far) and front–back egocentric location of sound sources. We aimed to consider verbal responses about localization of sound sources and EMG data on blink reflex. Results suggested that stimulus distance evaluations were better explained by subjective front–back discrimination, rather than real position. Moreover, blink response data were also better explained by subjective front–back discrimination. Taken together, these findings suggest that the mechanisms that dictate blink response magnitude might also affect sound localization (possible bottom–up mechanism), probably interacting with top–down mechanisms that modulate stimuli location and distance. These findings are interpreted within the defensive peripersonal framework, suggesting a close relationship between bottom–up and top–down mechanisms on spatial representation.

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies

Evidence is emerging on how whole-body powered exoskeleton (EXO) use impacts users in basic occupational work scenarios, yet our understanding of how users learn to use this complex technology is limited. We explored how novice users adapted to using an EXO during gait. Six novices and five experienced users completed the study. Novices completed an initial training/familiarization gait session, followed by three subsequent gait sessions using the EXO, while experienced users completed one gait session with the EXO. Spatiotemporal gait measures, pelvis and lower limb joint kinematics, muscle activities, EXO torques, and human-EXO interaction forces were measured. Adaptations among novices were most pronounced in spatiotemporal gait measures, followed by joint kinematics, with smaller changes evident in muscle activity and EXO joint torques. Compared to the experienced users, novices exhibited a shorter step length and walked with significantly greater anterior pelvic tilt and less hip extension. Novices also used lower joint torques from the EXO at the hip and knee, and they had greater biceps femoris activity. Overall, our results may suggest that novices exhibited clear progress in learning, but they had not yet adopted motor strategies similar to those of experienced users after the three sessions. We suggest potential future directions to enhance motor adaptations to powered EXO in terms of both training protocols and human-EXO interfaces.

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

The cortical mechanisms underlying the act of taking a step-including planning, execution, and modification - are not well understood. We hypothesized that oscillatory communication in a parieto-frontal and corticomuscular network is involved in the neural control of visually guided steps. We addressed this hypothesis using source reconstruction and lagged coherence analysis of electroencephalographic and electromyographic recordings during visually guided stepping and 2 control tasks that aimed to investigate processes involved in (i) preparing and taking a step and (ii) adjusting a step based on visual information. Steps were divided into planning, initiation, and execution phases. Taking a step was characterized by an upregulation of beta/gamma coherence within the parieto-frontal network during planning followed by a downregulation of alpha and beta/gamma coherence during initiation and execution. Step modification was characterized by bidirectional modulations of alpha and beta/gamma coherence in the parieto-frontal network during the phases leading up to step execution. Corticomuscular coherence did not exhibit task-related effects. We suggest that these task-related modulations indicate that the brain makes use of communication through coherence in the context of large-scale, whole-body movements, reflecting a process of flexibly fine-tuning inter-regional communication to achieve precision control during human stepping.

Usability challenges and social acceptance of textual input in a context of extended realities (XR) motivate the research of novel input modalities. We investigate the fusion of inertial measurement unit (IMU) control and surface electromyography (sEMG) gesture recognition applied to text entry using a QWERTY-layout virtual keyboard. We design, implement and evaluate our proposed multi-modal solution, MyoBoard, which is an IMU-driven and EMG-based text entry system based on the QWERTY keyboard layout for XR headsets. The user can select characters with a combination of arm movements and hand gestures. MyoBoard employs a light-weight convolutional neural network classifier that can be deployed on a mobile device with insignificant inference time. We demonstrate the practicality of interruption-free text entry in mobile scenarios with MyoBoard, by recruiting 12 participants and by testing three sets of grasp micro-gestures in three scenarios - empty hand text input, input holding a pen, and a large object, such as an umbrella. With MyoBoard, users achieve an average text entry rate of 9.33 words per minute (WPM), 8.76 WPM, and 8.35 WPM for the freehand, pen, and umbrella conditions, respectively.

Introduction The rupture of the anterior cruciate ligament (ACL) is one of the most common injuries of the knee. Women have a higher injury rate for ACL ruptures than men. Various indicators for this sex-specific difference are controversially discussed. Aim A systematic review of the literature that compares surface electromyography (EMG) values of adult female and male subjects to find out if there is a difference in neuromuscular activation of the knee stabilizing muscles. Methods This systematic review has been guided and informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Studies which examined sex-specific differences with surface EMG measurements (integral, root mean squares, mean values, analysis of time and amplitude) of the knee stabilizing muscles were retrieved via searches from the databases PubMed, CINAHL, Embase, CENTRAL and SPORTDiscus. The risk of bias of included studies was assessed with the National Heart, Lung and Blood Institute (NHLBI) study quality assessment tool. A synthesis of results was performed for relevant outcomes. Results Fifteen studies with 462 healthy participants, 233 women (mean age 21.9 (} 2.29) years) and 299 men (mean age 22.6 (} 2.43) years), were included in the systematic review. The methodological quality of the studies was mostly rated “fair” (40%). A significantly higher activity of the muscles vastus lateralis and vastus medialis was found in females, in three studies. Two studies found significantly lower neuromuscular activity in the muscles biceps femoris and semitendinosus in females. All other included studies found no significant differences or reported even contradicting results. Conclusion The controversial findings do not allow for a concluding answer to the question of a sex-specific neuromuscular activation. Further research with higher statistical power and a more homogeneous methodical procedure (tasks and data normalisation) of the included studies ma