Elbow Joint Angle Research Articles

Analysis of the mechanical characteristics of progressive one-handed underhand shooting in basketball play through kinematics

Background: Progressive one-handed underhand shooting is not only a key movement in basketball but also a primary method of scoring. Objective: This paper aims to compare the mechanical characteristics of players at different levels while performing the progressive one-handed underhand shooting movement to guide training. Methods: Ten athletes were divided into an excellent group and an ordinary group. The shooting was conducted using two SONY cameras, and the kinematics data were acquired through re-analysis in the Simi 3D Motion system for comparative analysis. Results: The average number of successful shots per person in the excellent group was 8.12 ± 0.81, which was significantly different from the ordinary group. During ball holding, the first step length of the excellent group was 1.91 ± 0.03 m, showing a significant difference compared to the ordinary group. At the beginning of ball holding, the right elbow joint angle for the excellent group was 121.26° ± 0.58° and the right hip joint angle was 135.64° ± 0.78°, both significantly different from those in the ordinary group. At the end of holding the ball, the excellent group had a right shoulder joint angle of 51.26° ± 2.36° and a right elbow joint angle of 70.34° ± 1.68°, which was significantly different from the ordinary group. At the end of jump, the excellent group had a right shoulder joint angle of 80.16° ± 2.21° and a right elbow joint angle of 87.45° ± 1.68°, which was significantly different from the ordinary group. During the shooting phase, the excellent group had a shooting angle of 60.12° ± 2.36°, a shooting height of 2.92 ± 0.03 m, and a shooting speed of 4.12 ± 0.46 m/s, all showing significant differences compared to the ordinary group. Conclusion: The excellent group with more sufficient stride, push, and extension and better shooting parameters performed better in performing the movement of progressive one-handed underhand shooting.

Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait.

Pathological gait in patients with Hakim's disease (HD, synonymous with idiopathic normal-pressure hydrocephalus; iNPH), Parkinson's disease (PD), and cervical myelopathy (CM) has been subjectively evaluated in this study. We quantified the characteristics of upper and lower limb movements in patients with pathological gait. We analyzed 1491 measurements of 1 m diameter circular walking from 122, 12, and 93 patients with HD, PD, and CM, respectively, and 200 healthy volunteers using the Three-Dimensional Pose Tracker for Gait Test. Upper and lower limb movements of 2D coordinates projected onto body axis sections were derived from estimated 3D relative coordinates. The hip and knee joint angle ranges on the sagittal plane were significantly smaller in the following order: healthy > CM > PD > HD, whereas the shoulder and elbow joint angle ranges were significantly smaller, as follows: healthy > CM > HD > PD. The outward shift of the leg on the axial plane was significantly greater, as follows: healthy < CM < PD < HD, whereas the outward shift of the upper limb followed the order of healthy > CM > HD > PD. The strongest correlation between the upper and lower limb movements was identified in the angle ranges of the hip and elbow joints on the sagittal plane. The lower and upper limb movements during circular walking were correlated. Patients with HD and PD exhibited reduced back-and-forth swings of the upper and lower limbs.

Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry.

Movement analysis is a critical tool in understanding and addressing various disabilities associated with movement deficits. By analyzing movement patterns, healthcare professionals can identify the root causes of these alterations, which is essential for preventing, diagnosing, and rehabilitating a broad spectrum of medical conditions, disabilities, and injuries. With the advent of affordable motion capture technologies, quantitative data on patient movement is more accessible to clinicians, enhancing the quality of care. Nonetheless, it is crucial that these technologies undergo rigorous validation to ensure their accuracy in collecting and monitoring patient movements, particularly for remote healthcare services where direct patient observation is not possible. In this study, motion capture technology was used to track upper extremity movements during a reaching task presented in virtual reality. Kinematic data was then calculated for each participant using a scaled dynamic inertial model. The goal was to evaluate the accuracy of joint angle calculations using inverse kinematics from motion capture relative to the typical movement redundancy. Shoulder, elbow, radioulnar, and wrist joint angles were calculated with models scaled using either direct measurements of each individual's arm segment lengths or those lengths were calculated from individual height using published average proportions. The errors in joint angle trajectories calculated using the two methods of model scaling were compared to the inter-trial variability of those trajectories. The variance of this error was primarily within the normal range of variability between repetitions of the same movements. This suggests that arm joint angles can be inferred with good enough accuracy from motion capture data and individual height to be useful for the clinical assessment of motor deficits.

Estimation of time duration on the Basis of Decrease in Elbow Joint Angle for Successful Application of Choke in Judo

Estimation of time duration on the Basis of Decrease in Elbow Joint Angle for Successful Application of Choke in Judo

Characterizing the Sensing Response of Carbon Nanocomposite-Based Wearable Sensors on Elbow Joint Using an End Point Robot and Virtual Reality.

Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements.

Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation.

Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint. Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA). Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly. Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.

Biomechanical Analyses of Scoop in Field Hockey

Field hockey is an Olympic sport played all over the world. Hockey is played with a stick where one requires endurance, agility, balance coordination, and high skill to convert the shot. There are various kinds of skills i.e. hitting, dribbling, scooping, tackling etc. Only scoop was selected for the study in different phases (stance phase, execution phase, follow through phase). Field hockey scoop is the aerial passing by lifting the ball from the ground inserting head of hockey stick under the ball. Scoop plays vital role in competitive field hockey games. The aim of the study was to find out the kinematic difference in the elbow joint, knee joint, and ankle joint. The objective of the study was to find out the angular differences of elbow joint, knee joint, and ankle joint in different phases of delivery of field hockey scoop shot. Researchers hypothesised that there are significance differences in elbow joint angle, knee joint angle, and ankle joint angle during delivering of field hockey scoop shot. Six males inter university players aged from 20-24 years were selected for the study and at least they represented at inter university games /national level. Set of the camera is at 1.10 m in height and 3.70 meters. The focal length is 5.6, the resolution is 1080, and the frame rate is 100 per sec. A Sony FRD-AX700 camera was placed 3.7 meters apart on the right side of the hockey player. Angle of elbow joint, angle of knee joint, angle ankle joint, cadence, stride length, and numbers of frames were analysed using Kinovea 0.9.5 software. This research will help the players to execute scoop after knowing elbow joint angle, knee joint angle, and ankle joint angle. Key Words: Field Hockey, Scoop, Elbow, Ankle, Knee.

Compensation-corrective adaptive control strategy for upper-limb rehabilitation robots

Trunk compensation is a common behavior observed in stroke patients during rehabilitation, and it can hinder their recovery outcomes. To address this issue, we developed a new upper-limb rehabilitation robot that takes advantage of both end-effector and exoskeleton robots. Moreover, we propose a compensation-corrective adaptive control (CCAC) strategy, which employs an admittance model and incorporates two estimators. Specifically, the first estimator is designed to assess human intention, allowing for compliant human-robot interaction. The second estimator calculates dynamic assistance that adjusts for trunk compensation, utilizing two virtual forces applied to the hand and shoulder. Based on this novel CCAC strategy, the newly designed robot is capable of assisting upper limb movements and correcting compensatory postures simultaneously. Results indicate a significant reduction in trunk compensation across three types of reaching tasks when the robot provides assistance. Moreover, the CCAC strategy enhances upper-limb motor performance, resulting in reduced position errors and increased shoulder and elbow joint angles. These findings underscore the potential of the proposed CCAC strategy, combined with upper-limb exoskeleton robots, as a promising approach for correcting compensatory postures and optimizing the advantages of robotic stroke rehabilitation.

Kinematic characteristics of elbow joint range of motion in elite Chinese freestyle swimmers with elbow pain during dry-land simulations of swimming strokes

ABSTRACT The aim of this study was to obtain quantitative data on elbow joint ROM in elite freestyle swimmers with EP in China. Of the 50 elite freestyle swimmers recruited, 41 completed all measurements during dry-land swimming stroke simulations. Elbow joint angle, velocity, and acceleration were measured using inertial measurement units. The RMSE/D was calculated to determine the elbow joint ROM deviation. Joint angle (3.33 ∘ –42.96 ∘ ), angular velocity (−364.15 to 245.69 ∘ / s ), and angular acceleration (−7051.80 to 1465.35 ∘ / s 2 ) were significantly different between the critical pain and healthy. The probability distributions of joint angle (15.47 ∘ ±14.54 ∘ ), angular velocity (2.41 ∘ ±111.06 ∘ / s ), and angular acceleration (1.93 ± 2222.6 ∘ / s 2 ) in the slight pain group were significantly different betweenhealthy and critical pain. The RMSE/D distributions of angular velocity (28.3%) and acceleration (21.48%) in the critical pain deviated from the healthy. The peak value-RMSE/D matrix model obtained proved that elbow ROM significantly differed between the elite freestyle swimmers with EP and the healthy. Angular velocity and acceleration indicate the weakness and negative influence of kinematics on patients with EP. Thus, Potential solutions are to constantly optimise freestyle swimming techniques and strengthen the arm muscles.

Distributed sensing along fibers for smart clothing.

Textile sensors transform our everyday clothing into a means to track movement and biosignals in a completely unobtrusive way. One major hindrance to the adoption of "smart" clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: Connections between rigid and textile elements are often unreliable, and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fiber. We use a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fiber. Compared to optical motion capture, we achieve around five degrees error in reconstructing shoulder, elbow, and wrist joint angles.

Improved Estimation of Elbow Flexion Angle from IMU Measurements Using Anatomical Constraints

ObjectivesInertial Measurement Units (IMUs) are a valid alternative to optical tracking systems for human motion capture, but they are subject to several disturbances that limit their accuracy. We aim to improve the accuracy of elbow joint angle estimation from IMU measurements by introducing a novel postprocessing algorithm that uses anatomical constraints and does not require any prior calibration or knowledge of anthropometric parameters. Materials and MethodsWe propose a new error model that addresses sensor misalignment and fusion errors. We use an error state extended Kalman filter (ESEKF) with state constraints to integrate the anatomical constraints. We validate the proposed algorithm by testing it in different scenarios and comparing it with a state-of-the-art optical tracking system. ResultsThe research results highlight the superior performance of the proposed method compared with existing techniques. The study demonstrates a significant reduction in errors, particularly in complex arm movements and under strong external disturbances. The results obtained in the three different tested scenarios underscore the robustness and effectiveness of the developed algorithm, reaching half the error committed by the existing calibration-free correction algorithms proposed in the literature. ConclusionsThe developed technique provides highly accurate estimates of joint angles in several challenging real-world scenarios.

Effects of a Sustained, Isometric Forearm Flexion Task to Failure on Torque and Neuromuscular Responses at 3 Elbow Joint Angles

Abstract Ortega, DG, Housh, TJ, Smith, RW, Arnett, JE, Neltner, TJ, Anders, JPV, Schmidt, RJ, and Johnson, GO. The effects of a sustained, isometric forearm flexion task to failure on torque and neuromuscular responses at 3 elbow joint angles. J Strength Cond Res 38(1): e25–e33, 2024—This study examined the effects of a sustained, isometric forearm flexion task anchored to torque to task failure on maximal voluntary isometric contraction (MVIC) and neuromuscular responses at 3 elbow joint angles. Eleven women (mean ± SD: age = 20.8 ± 2.7 years, height = 169.3 ± 7.4 cm, body mass = 67.7 ± 6.9 kg) performed two 3s forearm flexion MVICs at elbow joint angles (JAs) of 75°, 100°, and 125° before and after a sustained, isometric forearm flexion task to failure at a fatiguing joint angle of 100° anchored to a torque value that corresponded to a rating of perceived exertion of 8 (RPE = 8). The amplitude (AMP) and mean power frequency (MPF) of the electromyographic (EMG) and mechanomyographic (MMG) signals were recorded from the biceps brachii. Repeated-measures ANOVAs were used to compare mean differences for MVIC and neuromuscular parameters. Collapsed across JAs, MVIC (p &lt; 0.001) and EMG MPF (p = 0.006) pretest values were greater than posttest values. Collapsed across time, EMG MPF at JA75 was greater than JA100 (p &lt; 0.001) and JA125 (p &lt; 0.001), and JA100 was greater (p = 0.007) than JA125. For EMG AMP, there was a fatigue-induced decrease at JA75 (p = 0.003). For neuromuscular efficiency (NME = normalized torque/normalized EMG AMP), there were decreases from pretest to posttest at JA100 (p = 0.002) and JA125 (p = 0.008). There were no significant interactions or main effects for MMG AMP and MMG MPF. From these findings, it was hypothesized that the decline in MVICs at JA75, JA100, and JA125 was due to fatigue-induced metabolic perturbations that resulted in JA-specific neuromuscular responses. Thus, neuromuscular parameters may provide insight into the JA-specific mechanisms of fatigue.

Особливості кутових характеристик техніки прямого удару правою рукою з лівосторонньої стійки у висококваліфікованих спортсменів, які спеціалізуються в рукопашному бою

Topicality. Among the most urgent and fundamental areas of hand-to-hand combat technique research, one can single out kinematic analysis within certain phases, as well as within the framework of a complete exercise. The Research Purpose is to determine of the angular characteristics of the right-hand strike technique from the left-sided stance of highly qualified athletes specializing in hand-to-hand combat. 12 highly qualified athletes took part in the research. The following Research Methods as theoretical analysis, pedagogical and mathematical methods have been used in the study. The Research Results. Since there is no swing phase of straight kick, the preparation phase of kicking immediately precedes the impact action phase. During the preparation phase, athletes slightly bend their left leg, the left knee joint angle decreases by 3,8° (from 123,1° to 119,3°), and in the left hip joint by 4,2° (from 134,4° to 130,2°). Since there is no early swing phase of straight kick, the preparation kick phase immediately precedes the percussive impact phase. During the preparation phase, athletes slightly bend their left leg, the left knee joint angle decreases by 3,8° (from 123,1° to 119,3°), and in the left hip joint angle – by 4,2° (from 134,4° to 130,2°). The right leg remains practically immobile: the right knee joint angle at the beginning of the phase is 137,1°, and at the end of the phase it is 136,5°, that is, it decreases by only 0,6°; the right hip joint angle at the beginning of the phase is 145,9°, and at the end of the phase is 147,8°, increasing by 1,9°. The angular indicators of the upper limbs movements during the preparation phase for the impact have the following changes: the left shoulder joint angle increases by 6° (from 44.6° to 50,6°), the left elbow joint angle decreases by 6° (from 57,3° to 51,3°), the right shoulder joint angle increases by 4,5° (from 30,4° to 34,9°), and in the right elbow joint angle increases by 19,8° (from 51,8° to 71,6°). Findings. The straight right hand kick from the left stance is one of the most widely used and effective kicks in hand-to-hand combat. Also, due this kick, the greatest damage is inflicted on the opponent.

Validity and Reliability of Upper Limb Kinematic Assessment Using a Markerless Motion Capture (MMC) System: A Pilot Study

ObjectiveTo investigate the validity and test-retest reliability of a customized markerless motion capture (MMC) system that used iPad Pros with a Light Detection And Ranging scanner at two different viewing angles to measure the active range of motion (AROM) and the angular waveform of the upper-limb-joint angles of healthy adults performing functional tasks. DesignParticipants were asked to perform shoulder and elbow actions for the investigator to take AROM measurements, followed by four tasks that simulated daily functioning. Each participant attended 2 experimental sessions, which were held at least 2 days and at most 14 days apart. SettingA Vicon system and 2 iPad Pros installed with our MMC system were placed at 2 different angles to the participants and recorded their movements concurrently during each task. ParticipantsThirty healthy adults (mean age: 28.9, M/F ratio: 40/60). InterventionsNot applicable. Main Outcome MeasuresThe AROM and the angular waveform of the upper-limb-joint angles. ResultsThe iPad Pro MMC system underestimated the shoulder joint and elbow joint angles in all four simulated functional tasks. The MMC demonstrated good to excellent test-retest reliability for the shoulder joint AROM measurements in all 4 tasks. ConclusionsThe maximal AROM measurements calculated by the MMC system had consistently smaller values than those measured by the goniometer. An MMC in iPad Pro system might not be able to replace conventional goniometry for clinical ROM measurements, but it is still suggested for use in home-based and telerehabilitation training for intra-subject measurements because of its good reliability, low cost, and portability. Further development to improve its performance in motion capture and analysis in disease populations is warranted.

Monocular camera-based online sensor-to-segment calibration for upper body pose estimation

Robot-assisted rehabilitation training usually requires body pose estimation for either evaluation or robot control. Wearable sensors such as inertial measurement units (IMUs) have been extensively studied as affordable solutions. However, a prerequisite is to calibrate the relative transform between the sensor frame and segment anatomical frame, which is usually complex and time-consuming. In this paper, a visual-inertial fusion approach is proposed where a monocular camera is employed to overcome the drawbacks of IMUs and simplify the calibration process. The locations of IMUs are detected from vision using ArUco markers attached to them and the sensor-to-segment calibration is achieved using additional skeletal keypoint locations detected using the MediaPipe framework during arbitrary movements. Experiments were conducted to evaluate the accuracy of the proposed calibration method and the root mean square error of the shoulder and elbow joint angles are 5.13–7.86° with correlation coefficients above 0.96 in comparison to ground truth joint angles from the optical motion capture system. The proposed method achieves similar accuracy to traditional predefined poses-based calibration methods but simplifies the procedure, making it suitable for patients who cannot perform poses accurately in rehabilitation.

Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control

To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb’s structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.

Test-retest reliability of joint position sense in the elbow among healthy adults

BackgroundProprioception of the elbow is necessary for daily activities. Proprioception can be impaired with injury or pathology, and rehabilitation efforts seek to improve proprioception for improved upper extremity functioning. In clinical practice, joint position sense (JPS) testing is often utilized to assess conscious proprioception; however, varying methods exist with little evidence of psychometric properties, especially for the elbow. PurposeThe primary aim of this study was to create a standardized elbow JPS test protocol using goniometry measurement to determine the most reliable testing position. The secondary aim was to determine if the elbow JPS test had acceptable test-retest reliability in healthy adults. Study DesignThis was a cross-sectional study using a convenience sample of healthy adults. MethodsThree elbow positions (45°, 60°, and 75° of elbow flexion) were measured twice on the same day by a single rater using a universal goniometer. We calculated the absolute error in degrees between each position and reposition and the intraclass correlation coefficient (3,1) for relative reliability. We also calculated the standard error of the measurement and visually inspected Bland-Altman plots for absolute reliability. ResultsSixty-eight healthy adults (mean age 27.9 years, standard deviation 8.59) were assessed. The mean absolute error for all positions for test and retest was 5.7°. The intraclass correlation coefficients were moderate to good (0.57-0.75), and the standard error of the measurement was 2° (rounded) for all positions. The limits of agreement were moderately narrow, and the Bland-Altman plots showed a random distribution of errors for each position, indicating clinically acceptable measurement error. ConclusionsThe joint position reproduction test for JPS using goniometry demonstrated moderate to good test-retest reliability and acceptable measurement error in healthy adults. The elbow joint angle of 60° flexion was the most reliable for JPS testing, providing insight for JPS assessment in clinical practice.

Effect of Forearm Postures and Elbow Joint Angles on Elbow Flexion Torque and Mechanomyography in Neuromuscular Electrical Stimulation of the Biceps Brachii.

Neuromuscular electrical stimulation plays a pivotal role in rehabilitating muscle function among individuals with neurological impairment. However, there remains uncertainty regarding whether the muscle's response to electrical excitation is affected by forearm posture, joint angle, or a combination of both factors. This study aimed to investigate the effects of forearm postures and elbow joint angles on the muscle torque and MMG signals. Measurements of the torque around the elbow and MMG of the biceps brachii (BB) muscle were conducted in 36 healthy subjects (age, 22.24 ± 2.94 years; height, 172 ± 0.5 cm; and weight, 67.01 ± 7.22 kg) using an in-house elbow flexion testbed and neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle was stimulated while the forearm was positioned in the neutral, pronation, or supination positions. The elbow was flexed at angles of 10°, 30°, 60°, and 90°. The study analyzed the impact of the forearm posture(s) and elbow joint angle(s) on the root-mean-square value of the torque (TQRMS). Subsequently, various MMG parameters, such as the root-mean-square value (MMGRMS), the mean power frequency (MMGMPF), and the median frequency (MMGMDF), were analyzed along the longitudinal, lateral, and transverse axes of the BB muscle fibers. The test-retest interclass correlation coefficient (ICC21) for the torque and MMG ranged from 0.522 to 0.828. Repeated-measure ANOVAs showed that the forearm posture and elbow flexion angle significantly influenced the TQRMS (p < 0.05). Similarly, the MMGRMS, MMGMPF, and MMGMDF showed significant differences among all the postures and angles (p < 0.05). However, the combined main effect of the forearm posture and elbow joint angle was insignificant along the longitudinal axis (p > 0.05). The study also found that the MMGRMS and TQRMS increased with increases in the joint angle from 10° to 60° and decreased at greater angles. However, during this investigation, the MMGMPF and MMGMDF exhibited a consistent decrease in response to increases in the joint angle for the lateral and transverse axes of the BB muscle. These findings suggest that the muscle contraction evoked by NMES may be influenced by the interplay between actin and myosin filaments, which are responsible for muscle contraction and are, in turn, influenced by the muscle length. Because restoring the function of limbs is a common goal in rehabilitation services, the use of MMG in the development of methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels is imperative.

Elbow joint loads during simulated activities of daily living: implications for formulating recommendations after total elbow arthroplasty

Overloading of the elbow joint prosthesis following total elbow arthroplasty can lead to implant failure. Joint moments during daily activities are not well-contextualized for a prosthesis' failure limits and the effect of the current postoperative instruction on elbow joint loading is unclear. This study investigates the difference in elbow joint moments between simulated daily tasks and between flexion-extension, pronation-supination, varus-valgus movement directions. Additionally, the effect of the current postoperative instruction on elbow joint load is examined. Nine healthy participants (age 45.8 ± 17 years, 3 males) performed eight tasks; driving a car, opening a door, rising from chair, lifting, sliding, combing hair, drinking, emptying cup, without and with the instruction "not lifting more than 1 kg". Upper limb kinematics and hand contact forces were measured. Elbow joint angles and net moments were analyzed using inverse dynamic analysis, where the net moments are estimated from movement data and external forces. Peak elbow joint moments differed significantly between tasks (p < 0.01) and movement directions (p < 0.01). The most and least demanding tasks were, rising from a chair (13.4 Nm extension, 5.0 Nm supination, 15.2 Nm valgus) and sliding (4.3 Nm flexion, 1.7 Nm supination, 2.6 Nm varus). Net moments were significantly reduced after instruction only in the chair task (p < 0.01). This study analyzed elbow joint moments in different directions during daily tasks. The outcomes question whether postoperative instruction can lead to decreasing elbow loads. Future research might focus on reducing elbow loads in the flexion-extension and varus-valgus directions.

Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist.

Joint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to differences in motion dynamics. Furthermore, the extent that post-processed sensor fusion algorithms can improve measurement accuracy relative to more commonly used Kalman filter-based methods remains unknown. This study calculated the elbow and wrist joint angles of 13 participants performing a simple ≥30 min material transfer task at three rates (slow, medium, fast) using IMUs and kinematic constraints. The best-performing sensor fusion algorithm produced total root mean square errors (i.e., encompassing all three motion planes) of 6.6°, 3.6°, and 2.0° for the slow, medium, and fast transfer rates for the elbow and 2.2°, 1.7°, and 1.5° for the wrist, respectively.