Elbow Flexion-extension Movements Research Articles

Muscle fatigue tracking during dynamic elbow flexion-extension movements with a varying hand load

Muscle fatigue monitoring, an important element in a fatigue risk management process, can help optimize work intensity and reduce risks for musculoskeletal injuries. An experiment was conducted to determine whether myoelectric manifestations of muscle fatigue can reflect the pace of fatigue development associated with varying load intensity. Twenty male participants performed elbow flexion-extension movements with alternating hand loads (2 kg vs. 1 kg) for 16 min. The pace of fatigue in the biceps brachii in response to load variation was quantified by electromyographic (EMG) fatigue measures collected during the dynamic elbow flexion-extension movements and periodic submaximal isometric elbow flexion trials. The isometric and dynamic EMG measures, except for the amplitude of dynamic EMG, indicated fatigue development during the 2-kg isotonic movements and partial recovery with the 1 kg load. Study results suggest the potential of EMG measures for fatigue monitoring during dynamic work tasks with varying load intensity.

Elbow Kinematics and Function Following Treatment with Open Arthrolysis and Hinged External Fixator.

Open arthrolysis (OA) combined with hinged external fixator (HEF) is a promising surgical option for patients with elbow stiffness. This study aimed to investigate elbow kinematics and function following a combined treatment with OA and HEF in elbow stiffness cases. Patients treated with OA with or without HEF due to elbow stiffness were recruited between August 2017 and July 2019. Elbow flexion-extension motion and function (Mayo elbow performance scores, MEPS) were recorded and compared between patients with and without HEF during a 1-year follow-up period. Additionally, those with HEF were assessed by dual fluoroscopy at week 6 postoperatively. Flexion-extension and varus-valgus motions, as well as ligament insertion distances of the anterior medial collateral ligament (AMCL) and lateral ulnar collateral ligament (LUCL), were compared between the surgical and intact sides. This study included 42 patients, of which 12 with HEF demonstrated a similar flexion-extension angle and range of motion (ROM) and MEPS as the other patients. In patients with HEF, the surgical elbows showed limitations in flexion-extension (maximal flexion, 120.5° ± 5.3° vs 140.4° ± 6.8°; maximal extension, 13.1° ± 6.0° vs 6.4° ± 3.0°; ROM, 107.4° ± 9.9° vs 134.0° ± 6.8°; all Ps < 0.01) compared with the contralateral sides. During elbow flexion, a gradual valgus-to-varus transition of the ulna, increase in the AMCL insertion distance, and steady change in the LUCL insertion distance were observed, with no significant differences between the bilateral sides. Patients treated with OA and HEF demonstrated similar elbow flexion-extension motion and function to those treated with OA alone. Although the use of HEF could not restore an intact flexion-extension ROM and might result in some minor but not significant changes in kinematics, it contributed to clinical outcomes comparable to that of the treatment with OA alone.

A 3D-printed passive exoskeleton for upper limb assistance in children with motor disorders: proof of concept through an electromyography-based assessment.

The rehabilitation of children with motor disorders is mainly focused on physical interventions. Numerous studies have demonstrated the benefits of upper function using robotic exoskeletons. However, there is still a gap between research and clinical practice, owing to the cost and complexity of these devices. This study presents a proof of concept of a 3D-printed exoskeleton for the upper limb, following a design that replicates the main characteristics of other effective exoskeletons described in the literature. 3D printing enables rapid prototyping, low cost, and easy adjustment to the patient anthropometry. The 3D-printed exoskeleton, called POWERUP, assists the user's movement by reducing the effect of gravity, thereby allowing them to perform upper limb exercises. To validate the design, this study performed an electromyography-based assessment of the assistive performance of POWERUP, focusing on the muscular response of both the biceps and triceps during elbow flexion-extension movements in 11 healthy children. The Muscle Activity Distribution (MAD) is the proposed metric for the assessment. The results show that (1) the exoskeleton correctly assists elbow flexion, and (2) the proposed metric easily identifies the exoskeleton configuration: statistically significant differences (p-value = 2.26 ⋅ 10-7 < 0.001) and a large effect size (Cohen's d = 3.78 > 0.8) in the mean MAD value were identified for both the biceps and triceps when comparing the transparent mode (no assistance provided) with the assistive mode (anti-gravity effect). Therefore, this metric was proposed as a method for assessing the assistive performance of exoskeletons. Further research is required to determine its usefulness for both the evaluation of selective motor control (SMC) and the impact of robot-assisted therapies.

Wide-Awake Olecranon Fracture Fixation: Is it Possible?

The wide-awake local anesthesia no tourniquet technique has been widely performed in hand and wrist surgery with remarkable results. However, its use on the elbow has rarely been reported. Here we describe the use of wide-awake local anesthesia no tourniquet in olecranon fracture fixation in 4 cases. Tumescent anesthesia was injected from the proximal ulna to approximately 10 cm distally and into the periosteum and fracture site, approximately 25 minutes before skin incision. The fracture underwent closed reduction and was fixed using a long 6.5-mm cancellous screw with a washer through a small incision. No tourniquet was applied and none or mild sedation was administered. At the end of the operation, patients were asked to perform active elbow flexion-extension and forearm pronosupination movements under an image intensifier to test the range of motion and fracture stability. The surgical procedure was completed in all 4 cases. Two patients reported mild pain during ulnar medullary canal reaming, with pain scores of 3 and 4 on a 10-point scale, respectively. One case was resolved with additional local anesthetic injection. The other case required the administration of intravenous propofol. Both patients were able to actively move the elbow at the end of the operation. The use of wide-awake local anesthesia no tourniquet for olecranon fracture fixation has the advantage of obviating the need for an arm tourniquet, general anesthesia or heavy sedation, preoperative tests, and discontinuing routine medications (including anticoagulants). The stability of the elbow fixation was tested by active motion during surgery. This simple, safe, low-cost, and reproducible technique may be a good option for patients with contraindications or high risk of general or regional nerve block anesthesia. Therapeutic IV.

Kinetics of the front-arm technique in cricket fast bowling

The aim of this study was to investigate the kinetics of the front arm in cricket fast bowling. Eighteen male fast bowlers (mean age = 17.2 ± 1.6 years; mean height = 1.89 ± 0.05 m; mean mass = 85.0 ± 10.0 kg) were recruited from NSW grade club level and their bowling actions recorded by a Cortex 2.0 motion analysis system (200 Hz). The interaction between joint power and joint angular velocity data was observed simultaneously for the front shoulder and elbow joints. A joint actuation strategy (active or controlled) was assigned to shoulder flexion–extension, abduction–adduction, and elbow flexion–extension motion to understand whether the joint kinematics followed the time-history of the joint torque. The results only partially agreed with the commonly prescribed coaching maxim of “pulling down” the front arm for faster ball speeds. Active extension and adduction of the front shoulder joint were observed during the delivery phase of the action, whereas the acceleration phase was marked by periods of controlled shoulder extension and active shoulder flexion. At the elbow joint, active elbow flexion was observed at a time when it may reduce the moment of inertia about the thoracic and lumbar spine rotation axes. Overall, combinations of active and controlled joint powers were observed for the front shoulder and elbow joints, implying that this sample of bowlers did not adhere to the traditional notion of “pulling down” the front arm as fast as possible.

Assessing Time-Varying Lumbar Flexion-Extension Kinematics Using Automated Pose Estimation.

The purpose of this research was to evaluate the algorithm DeepLabCut (DLC) against a 3D motion capture system (Vicon Motion Systems Ltd) in the analysis of lumbar and elbow flexion-extension movements. Data were acquired concurrently and tracked using DLC and Vicon. A novel DLC model was trained using video data derived from a subset of participants (training group). Accuracy and precision were assessed using data derived from the training group as well as in a new set of participants (testing group). Two-way analysis of variance were used to detect significant differences between the training and testing sets, capture methods (Vicon vs DLC), as well as potential higher order interaction effect between these independent variables in the estimation of flexion-extension angles and variability. No significant differences were observed in any planar angles, nor were any higher order interactions observed between each motion capture modality with the training versus testing data sets. Bland-Altman plots were used to depict the mean bias and level of agreement between DLC and Vicon for both training and testing data sets. This research suggests that DLC-derived planar kinematics of both the elbow and lumbar spine are of acceptable accuracy and precision when compared with conventional laboratory gold standards (Vicon).

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems.

Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) digital twin system for health condition assessment and motion prediction. While machine learning approaches with capabilities in extracting complex features and patterns from a large amount of data have been applied to motion prediction given sEMG signals, the learned data-driven mapping is black-box and may not satisfy the underlying physics and has reduced generality. In this work, we propose a feature-encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. In this approach, features of high-dimensional noisy sEMG signals are projected onto a low-dimensional noise-filtered embedding space for the enhancement of forwarding dynamics prediction. This FEPI-PINN model can be trained to relate sEMG signals to joint motion and simultaneously identify key MSK parameters. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion predictions of elbow flexion-extension motion that are in good agreement with the measured joint motion data.

A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System.

This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task.

Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

User-Centered Virtual Environment for Poststroke Motor Rehabilitation

Abstract The use of virtual reality in the rehabilitation of lost or diminished functions after a stroke has been shown to be an innovative means in motor recovery. However, there are still several design challenges to increment the efficiency of these systems. This paper presents the development and evaluation of a nonimmersive three-dimensional virtual environment for poststroke rehabilitation of elbow flexion–extension movements, which considers the therapist as a direct user and the patient as a secondary user. The development of virtual environment was supported by the criteria of a team of specialists in physical and occupational therapy, following the philosophy of user-centered design through three iterations, and incorporating tasks based on the activities of daily living of the Barthel scale. Tests were carried out with healthy users and a patient with a diagnosis of stroke, using the system usability scale (SUS) test and a flow status questionnaire, respectively. Average satisfaction of user group without diagnosis was 79.6 out of 100 points. On the other hand, according to mean values observed with the patient, dimensions of control sense (6.33) and positive emotional experience (6.66) reflect an “optimal” experience, which indicates an enjoyment of virtual tasks despite the effort made to fulfill them.

Margins of stability and trunk coordination during Nordic walking

Although it has already been demonstrated that Nordic walking has some peculiar biomechanical features with respect to walking, the effects on balance and trunk coordination are still unknown. Our aim here was to compare margins of stability, hip stabilizer muscle activation and scapular-pelvis coordination (mean and variability of continuous relative phase) between walking and two different pole walking techniques (observational design). Eleven Nordic walking instructors were asked to walk at 5.5 km·h−1 on a flat treadmill while 1) walking, 2) Nordic walking and 3) pole walking with just elbow flexion–extension motion allowed and constrained shoulder motion (elbow technique). The 3D movements of limbs and poles were measured by an optoelectronic motion capture system, and gluteus medius activation was measured through surface electromyography. Both techniques using poles show larger mediolateral margins of stability and similar anterior-posterior margins of stability in comparison with walking (p < 0.001). The larger mediolateral margin of stability using poles (conditions 2 and 3) is accompanied by greater trunk coordination stability (greater continuous relative phase variability) than walking. Although the Nordic walking (condition 2) technique results in a similar range of scapular and pelvis transverse rotation, the general pattern of scapular-pelvis coordination was temporally delayed by approximately 20% of the gait cycle in relation to other conditions (1 and 3). In conclusion, Nordic walking provides enhanced mediolateral support and coordination stability of trunk compared with walking, suggesting that it could be proposed as a safer exercise modality than walking.

The effect of washing on the electrical performance of knitted textile strain sensors for quantifying joint motion

Successful market penetration of textile-based strain sensors requires long-term reliability which in turn relies on the washability of the sensor. First, this paper presents an evaluation of the effect of 5 washing cycles on the electrical performance of a knitted conductive transducer, over 1500 cycles of repetitive elongation. The promising behaviour of the textile sensor in this study showed that it might be possible to make a smart garment, capable of quantifying elbow flexion-extension motion, by integrating it into an elbow sleeve. Second, a prototype sleeve, incorporating a knitted sensor (the so-called smart sleeve), was tested in a simulated training/clinical setting by performing 50 flexion-extension cycles after 1, 5, 15, 25, 50 and 75 washes. In both studies, the electrical resistance of the sensor increased with the number of washes in a predictable manner and exhibited a repeatable, reliable and prompt response to elongation. In particular, the electrical pattern representing flexion-extension motion measured using the sleeve was clear and distinguishable up to the 75th wash. Moreover, resistance measurements within the same trial were repeatable at maximum flexion (≤2% variation) and at maximum extension (≤3% variation) and predictable with increasing washes (R2 = 0.992 at maximum flexion and R2 = 0.989 at maximum extension). The good washability of the smart sleeve, evidenced by its ability to detect, distinguish and measure parameters of flexion-extension motion up to 75 washes, makes it a suitable and sustainable choice for applications, such as strength conditioning or rehabilitation, where repetition count and speed are useful.

Continuous Passive Motion Machine for Elbow Rehabilitation

Continuous Passive Machines (CPM) facilitate patients in eliminating joint stiffness after surgery and lead to a faster and more efficient recovery. However, many previous CPM machined are mechanically complicated, expensive, and lack a user interface. This paper presents a new CPM machine for elbow flexion-extension and forearm pronation-supination. The machine is simple, low-cost, and equipped with Graphical User Interface (GUI). Its mechanism is designed so that it can be used on the left or right arms interchangeably. It is developed using aluminum, perspex, and steel rods. The electrical part of the machine consists of Arduino Uno to drive the motors and a potentiometer to measure the patients’ Range of Motion (ROM). The GUI for setting the exercise parameters and monitoring the patients’ progress has been developed using MATLAB software. The experimental results show that the machine has successfully provided the repetitive desired motions. The machine realizes elbow flexion-extension and forearm pronation-supination movements with 0ᵒ-135ᵒ and 0ᵒ-90ᵒ ranges of motion (ROM), respectively. The machine is also capable of increasing the elbow joint’s ROM by 5ᵒ increments for the therapy. The results show that the machine has the potential to be used in hospitals and rehabilitation centers.

Automatic estimation of continuous elbow flexion–extension movement based on electromyographic and electroencephalographic signals

The aim of this study was to estimate the elbow joint angle based on EMG and EEG signals using signal processing and machine learning techniques. 21 subjects (ten females, eleven males) performed synchronous flexion–extension movements while EMG, EEG, and elbow kinematic signals were recorded. The EMG and EEG signals were used to estimate the elbow angle employing a long short-term memory neural network. The best results were obtained by training one network per subject (intra subject). The lowest error was reached using the EMG signal, RMSE = 8.59°±2.17° and R2=0.95 with a 95% CI (0.93–0.96). Employing EEG signals generated an RMSE = 9.27°±1.85° and R2=0.95 with a 95% CI (0.94–0.95). When both signals, EMG/EEG, were used, the results were RMSE = 9.53°±2.13° and R2=0.95 with a 95% CI (0.94–0.95). Statistically, for intra-subject data, there is no significant difference in RMSE on using a particular type of signal. In the case of inter-subject data, we obtained the lowest RMSE values considering the combination of EMG/EEG signals, for both, women and men, RMSE = 10.96°±5.28° and RMSE = 9.92°±4.62°, respectively. On the other hand, using subject-wise cross validation, errors increased as expected; however, men’s EMG/EEG signals proved to be robust increasing the RMSE only in 3.47°. A new methodology is proposed for estimating elbow angles based on EMG and EEG biosignals. This can be useful for generating control signals for prostheses and/or exoskeletons designed to provide the support that people with motor disabilities require.

Classification of Task Weight During Dynamic Motion Using EEG–EMG Fusion

Musculoskeletal disorders are the biggest cause of disability worldwide and wearable mechatronic rehabilitation devices have been proposed as a potential tool for providing treatment; however, before such devices can be widely adopted, improvements in reliability are necessary. Changes in system dynamics caused by user actions, such as picking up a weight, can greatly affect control stability; hence, detecting these changes can lead to improved system performance. It is difficult to integrate conventional sensing technologies for completing this task into a wearable device in an unobtrusive way, therefore an alternative solution using bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to detect task weight is proposed. In this study, EEG and EMG signals were collected during dynamic elbow flexion–extension motion at different speeds, while holding different weights. These biosignals were used to develop different EEG–EMG fusion models to classify the weight the user was holding while moving. It was found that using a Weighted Average fusion method, and incorporating speed information into the model, provided the best performance, with an accuracy of 83.01 ± 6.04% when classifying three task weights. This work demonstrated the feasibility of using EEG–EMG fusion for classification of task weight during dynamic motion, which can be used to improve the adaptability and robustness of wearable mechatronic rehabilitation devices.

Performance Evaluation of EEG/EMG Fusion Methods for Motion Classification.

Wearable robotic systems have shown potential to improve the lives of musculoskeletal disorder patients; however, to be used practically, they require a reliable method of control. The user needs to be able to indicate that they wish to move in a way that feels intuitive and comfortable. One proposed method for detecting motion intention is through the combined use of muscle activity, known as electromyography (EMG), and brain activity, known as electroencephalography (EEG). Other groups have developed various methods of fusing EEG/EMG signals for classification of motion intention, but a comprehensive evaluation of their performance has yet to be completed. This work evaluates EEG/EMG fusion methods during elbow flexion-extension motion while varying parameters, such as speed of motion, weight held, and muscle fatigue. Overall, the use of EEG/EMG fusion was found to not be more accurate than using just EMG alone $(86.81 \pm 3.98$%), with some fusion methods demonstrating equivalent performance to EMG $(p=1.000)$. EEG/EMG fusion was, however, demonstrated to be less sensitive to changes in motion parameters, allowing it to perform more consistently across different speed/weight combinations. The results of this work provide further justification for the use of EEG/EMG fusion for control of a wearable robotic device.

Corticomuscular Coherence for Upper Arm Flexor and Extensor Muscles During Isometric Exercise and Cyclically Isokinetic Movement

Cortical-muscular functional coupling reflects the interaction between the cerebral cortex and the muscle activities. Corticomuscular coherence (CMC) has been extensively revealed in sustained contractions of various upper- and lower-limb muscles during static and dynamic force outputs. However, it is not well-understood that the CMC modulation mechanisms, i.e., the relation between a cerebral hemisphere and dynamic motor controlling limbs at constant speeds, such as isokinetic movement. In this paper, we explore the CMC between upper arm flexors/extensors movement and motor cortex during isometric exercise and cyclically isokinetic movement. We also provide further insights of frequency-shift and the neural pathway mechanisms in isokinetic movement by evaluating the coherence between motor cortex and agonistic or antagonistic muscles. This study is the first to investigate the relationship between cortical-muscular functional connections in elbow flexion-extension movement with constant speeds. The result shows that gamma-range coherence for isokinetic movement is greatly increased compared with isometric exercise, and significant CMC is observed in the entire flexion-extension stage regardless the nature of muscles contraction, although dominant synchronization of cortical oscillation and muscular activity resonated in sustained contraction stage principally. Besides, the CMC for extensors and flexors are explicitly consistent in contraction stage during cyclically isokinetic elbow movement. It is concluded that cortical-muscular coherence can be dynamically modulated as well as selective by cognitive demands of the body, and the time-varying mechanisms of the synchronous motor oscillation exist in healthy individuals during dynamic movement.

An Orthopaedic Robotic-Assisted Rehabilitation Method of the Forearm in Virtual Reality Physiotherapy.

The use of robotic rehabilitation in orthopaedics has been briefly explored. Despite its possible advantages, the use of computer-assisted physiotherapy of patients with musculoskeletal injuries has received little attention. In this paper, we detailed the development and evaluation of a robotic-assisted rehabilitation system as a new methodology of assisted physiotherapy in orthopaedics. The proposal consists of an enhanced end-effector haptic interface mounted in a passive mechanism for allowing patients to perform upper-limb exercising and integrates virtual reality games conceived explicitly for assisting the treatment of the forearm after injuries at the wrist or elbow joints. The present methodology represents a new approach to assisted physiotherapy for strength and motion recovery of wrist pronation/supination and elbow flexion-extension movements. We design specific game scenarios enriched by proprioceptive and haptic force feedback in three training modes: passive, active, and assisted exercising. The system allows the therapist to tailor the difficulty level on the observed motion capacity of the patients and the kinesiology measurements provided by the system itself. We evaluated the system through the analysis of the muscular activity of two healthy subjects, showing that the system can assign significant working loads during typical physiotherapy treatment profiles. Subsequently, a group of ten patients undergoing manual orthopaedic rehabilitation of the forearm tested the system, under similar conditions at variable intensities. Patients tolerated changes in difficulty through the tests, and they expressed a favourable opinion of the system through the administered questionnaires, which indicates that the system was well accepted and that the proposed methodology was feasible for the case study for subsequently controlled trials. Finally, a predictive model of the performance score in the form of a linear combination of kinesiology observations was implemented in function of difficult training parameters, as a way of systematically individualising the training during the therapy, for subsequent studies.

An X-ray-free method to accurately identify the elbow flexion-extension axis for the placement of a hinged external fixator.

Identifying the elbow flexion-extension (F-E) movement axis is important for placing a hinged elbow external fixator. An X-ray fluoroscopy-based method is widely used in clinical practice, exposing the patient and surgeons to high doses of radiation. Additionally, the accuracy and repeatability of the fluoroscopy-based method are very low and affected by subjective factors. To solve this problem, an X-ray-free method based on kinematics analysis was proposed to identify the elbow F-E movement axis, and a navigation system was built to guide the placement of the elbow external fixator. Our X-ray-free navigation method is more repeatable than the current X-ray fluoroscopy method used clinically. Both our algorithm and the NIST (National Institute of Standards and Technology) algorithm showed high accuracy and repeatability to identify the axis. The method proposed in this study is very promising to avoid a large dose of X-ray radiation and increases the repeatability and performance of identifying the elbow F-E movement axis for the placement of the hinged elbow external fixator.

Joint torque variability and repeatability during cyclic flexion-extension of the elbow.

BackgroundJoint torques are generally of primary importance for clinicians to analyze the effect of a surgery and to obtain an indicator of functional capability to perform a motion. Given the current need to standardize the functional evaluation of the upper limb, the aim of this paper is to assess (1) the variability of the calculated maximal elbow joint torque during cyclic elbow flexion-extension movements and (2) participant test-retest repeatability in healthy young adults. Calculations were based on an existing non-invasive method including kinematic identification and inverse dynamics processes.MethodsTwelve healthy young adults (male n = 6) performed 10 elbow flexion-extension movement carrying five different dumbbells (0, 1, 2, 3 and 4 kg) with several flexion-extension frequencies (½, 1/3, ¼ Hz) to evaluate peak elbow joint torques.ResultsWhatever the condition, the variability coefficient of trial peak torques remained under 4 %. Bland and Altman plot also showed good test-retest, whatever the frequency conditions for the 0, 1, 2, and 3 kg conditions.ConclusionThe good repeatability of the flexion-extension peak torques represents a key step to standardize the functional evaluation of the upper limb.