Immediate changes in stroke patients' gait following the application of lower extremity elastic strap binding technique.

Objective: To ascertain the immediate changes in stroke patients' temporal and spatial parameters of gait and the joint angles of stroke patients throughout the entire gait cycle following the application of lower extremity elastic strap binding technique. Methods: Twenty-nine stroke patients were invited as the study participants. The patient seated, flexed the hip and knee, utilized a 5 cm-wide elastic strap, positioning its midpoint beneath the affected foot and crossing it anterior to the ankle joint. Upon standing, the strap encircled the posterior aspect of the lower leg, proceeded around the back of the knee, and ascended the thigh on the affected side. Crossing anteriorly over the thigh, it then encircled the back of the waist before being secured in place. Using Qualisys motion capture system to collect kinematic data of the lower extremities during walking while wearing shoes only or strapping. A paired sample t-test was used to analyze the effects of the technique on gait spatiotemporal parameters and joint angles in stroke patients. Results: The patients' step length decreased (P = 0.024), and step width increased (P = 0.008) during the gait cycle after the strapping. In the gait cycle between 0% and 2%, 7%-77%, and 95%-100%, the hip flexion angle on the affected side was significantly larger after the strapping (P < 0.05). In the gait cycle between 0% to 69% and 94%-100%, the knee flexion angle on the affected side was significantly larger after the strapping (P < 0.05). In the gait cycle between 0% to 57% and 67%-100%, the ankle dorsiflexion angle on the affected side was significantly smaller after the strapping (P < 0.05), and in the gait cycle between 0% to 35% and 68%-100%, the ankle inversion angle on the affected side was significantly smaller after the strapping (P < 0.05). Conclusion: The lower extremity elastic strap binding technique can decrease the hip flexion and knee flexion limitations in stroke patients during walking, and reduce the ankle plantar flexion and ankle inversion angle of stroke patients. The lower extremity elastic strap binding technique enabled stroke patients to adopt a more stable gait pattern.

Accuracy Validation of a Sensor-Based Inertial Measurement Unit and Motion Capture System for Assessment of Lower Limb Muscle Strength in Older Adults-A Novel and Convenient Measurement Approach.

(1) Background: The aim of this study was to assess lower limb muscle strength in older adults during the transfer from sitting to standing (STS) using an inertial measurement unit (IMU). Muscle weakness in this population can severely impact function and independence in daily living and increase the risk of falls. By using an IMU, we quantified lower limb joint moments in the STS test to support health management and individualized rehabilitation program development for older adults. (2) Methods: This study involved 28 healthy older adults (13 males and 15 females) aged 60-70 years. The lower limb joint angles and moments estimated using the IMU were compared with a motion capture system (Mocap) (pair t-test, ICC, Spearman correlations, Bland-Altman plots) to verify the accuracy of the IMU in estimating lower limb muscle strength in the elderly. (3) Results: There was no significant difference in the lower limb joint angles and moments calculated by the two systems. Joint angles and moments were not significantly different (p > 0.05), and the accuracy and consistency of the IMU system was comparable to that of the Mocap system. For the hip, knee, and ankle joints, the ICCs for joint angles were 0.990, 0.989, and 0.885, and the ICCs for joint moments were 0.94, 0.92, and 0.89, respectively. In addition, the results of the two systems were highly correlated with each other: the r-values for hip, knee, and ankle joint angles were 0.99, 0.99, and 0.96, and the r-values for joint moments were 0.92, 0.96, and 0.85. In the present study, there was no significant difference (p > 0.05) between the IMU system and the Mocap system in calculating lower limb joint angles and moments. (4) Conclusions: This study confirms the accuracy of the IMU in assessing lower limb muscle strength in the elderly. It provides a portable and accurate alternative for the assessment of lower limb muscle strength in the elderly.

Effect of Repetitive Pitching on the Control of Lower Extremity Joints and Center of Mass in Collegiate Baseball Pitchers.

BACKGROUND: Repetitive motion can alter joint angles and subsequently affect the control of the center of mass (CoM). While the CoM has been studied as a fatigue indicator in various sports, the control of the whole-body CoM during repetitive pitching in baseball pitchers has not been examined. This study aimed to investigate changes in lower-extremity joint angles and CoM control in collegiate baseball pitchers after repetitive pitching. HYPOTHESIS: Baseball pitchers would exhibit significant increase in lower-extremity flexion angles, CoM position, and CoM variability after repetitive pitching. STUDY DESIGN: Descriptive laboratory study. LEVEL OF EVIDENCE: Level 3. METHODS: A total of 23 pitchers from the Collegiate Baseball League were recruited. A motion analysis system was employed to assess lower-extremity joint angles and CoM position during the simulated game, while pitching accuracy and velocity were also recorded. RESULTS: The results revealed a significant forward and downward shift in CoM position (P < 0.05), along with increased CoM variability in all directions (P < 0.05) after the simulated game. Furthermore, there was a significant increase in flexion angles of the knee and hip (P < 0.05); however, pitching velocity and accuracy did not demonstrate significant changes. CONCLUSION: Repetitive pitching leads to kinematic changes that should be monitored to prevent sports injuries. CLINICAL RELEVANCE: Baseball pitchers have the ability to modify the control of their CoM and angles of their lower-extremity joints to sustain their pitching performance. It is crucial to monitor compensatory strategies closely to avoid shoulder and elbow injuries among these pitchers.

Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human-machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models-a kinematic extrapolation algorithm and a Random Forest machine learning algorithm-when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device.

Design and Verification of Piano Playing Assisted Hand Exoskeleton Robot.

Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton's motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton's ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety.

Shoulder kinematics during cyclic overhead work are affected by a passive arm support exoskeleton.

PURPOSE: We investigated the influence of passive arm-support exoskeleton (ASE) with different levels of torque (50, 75, and 100%) on upper arm osteokinematics. METHODS: Twenty participants completed a cyclic overhead drilling task with and without ASE. Task duration, joint angles, and angular acceleration peaks were analyzed during ascent and descent phases of the dominant upper arm. RESULTS: Maximum ASE torque was associated with decreased peak acceleration during ascent (32.2%; SD 17.8; p < 0.001) and descent phases (38.8%; SD 17.8; p < 0.001). Task duration remained consistent. Increased torque led to a more flexed (7.2°; SD 5.5; p > 0.001) and internally rotated arm posture (17.6°; SD 12.1; p < 0.001), with minimal changes in arm abduction. CONCLUSION: The small arm accelerations and changes in osteokinematics we observed, support the use of this ASE, even while performing overhead cyclic tasks with the highest level of support.

Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks.

Low-cost markerless motion capture systems offer the potential for 3D measurement of joint angles during human movement. This study aimed to validate a smartphone-based markerless motion capture system's (OpenCap) derived lower extremity kinematics during common return-to-sport tasks, comparing it to an established optoelectronic motion capture system. Athletes with prior anterior cruciate ligament reconstruction (12-18 months post-surgery) performed three movements: a jump-landing-rebound, single-leg hop, and lateral-vertical hop. Kinematics were recorded concurrently with two smartphones running OpenCap's software and with a 10-camera, marker-based motion capture system. Validity of lower extremity joint kinematics was assessed across 437 recorded trials using measures of agreement (coefficient of multiple correlation: CMC) and error (mean absolute error: MAE, root mean squared error: RMSE) across the time series of movement. Agreement was best in the sagittal plane for the knee and hip in all movements (CMC > 0.94), followed by the ankle (CMC = 0.84-0.93). Lower agreement was observed for frontal (CMC = 0.47-0.78) and transverse (CMC = 0.51-0.6) plane motion. OpenCap presented a grand mean error of 3.85° (MAE) and 4.34° (RMSE) across all joint angles and movements. These results were comparable to other available markerless systems. Most notably, OpenCap's user-friendly interface, free software, and small physical footprint have the potential to extend motion analysis applications beyond conventional biomechanics labs, thus enhancing the accessibility for a diverse range of users.

Effect of Data and Gap Characteristics on the Nonlinear Calculation of Motion During Locomotor Activities.

This study investigated how data series length and gaps in human kinematic data impact the accuracy of Lyapunov exponents (LyE) calculations with and without cubic spline interpolation. Kinematic time series were manipulated to create various data series lengths (28% and 100% of original) and gap durations (0.05-0.20 s). Longer gaps generally resulted in significantly higher LyE% error values in each plane in noninterpolated data. During cubic spline interpolation, only the 0.20-second gap in frontal plane data resulted in a significantly higher LyE% error. Data series length did not significantly affect LyE% error in noninterpolated data. During cubic spline interpolation, sagittal plane LyE% errors were significantly higher at shorter versus longer data series lengths. These findings suggest that not interpolating gaps in data could lead to erroneously high LyE values and mischaracterization of movement variability. When applying cubic spline, a long gap length (0.20 s) in the frontal plane or a short sagittal plane data series length (1000 data points) could also lead to erroneously high LyE values and mischaracterization of movement variability. These insights emphasize the necessity of detailed reporting on gap durations, data series lengths, and interpolation techniques when characterizing human movement variability using LyE values.

Longitudinal changes in vertical stride regularity, hip flexion, and knee flexion contribute to the alteration in gait speed during hospitalization for stroke.

Changes in stride regularity and joint motion during gait appear to be related to improved gait speed in hospitalized patients with stroke. We aimed to clarify the changes in stride regularity and joint motion during gait through longitudinal observations. Furthermore, we aimed to clarify the relationship between changes in gait speed, stride regularity, and joint motion during gait. Seventeen inpatients with stroke were assessed for physical and gait functions at baseline, when they reached functional ambulation category 3, and before discharge. Physical function was assessed using the Fugl-Meyer assessment for the lower extremities and the Berg Balance Scale. Gait function was assessed on the basis of gait speed, joint motion, stride regularity, and step symmetry using inertial sensors. The correlations between the ratio of change in gait speed and each indicator from baseline to discharge were analyzed. Both physical and gait functions improved significantly during the hospital stay. The ratio of change in gait speed was significantly and positively correlated with the ratio of change in vertical stride regularity (r = 0.662), vertical step symmetry (rs = 0.627), hip flexion (rs = 0.652), knee flexion (affected side) (r = 0.611), and ankle plantarflexion (unaffected side) (rs = 0.547). Vertical stride regularity, hip flexion, and knee flexion (affected side) were significant factors in determining the ratio of changes in gait speed. Our results suggest that stride regularity, hip flexion, and knee flexion could explain the entire gait cycle and that of the affected side. These parameters can be used as indices to improve gait speed.

How reliable are femoropelvic kinematics during deep squats? The influence of subject-specific skeletal modelling on measurement variability.

BACKGROUND: Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling. METHODS: 8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches. RESULTS: Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (-2.8±2.7), hip extension (-2.2±3.8), hip abduction (-1.2±3.6) and external rotation (-13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip. SIGNIFICANCE: S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.

Static posture weightbearing joint angle differences in patients with varus ankle osteoarthritis.

BACKGROUND: Advanced varus ankle osteoarthritis is a debilitating disease that can present with limited physical function, severe pain, and diminished quality of life. Weightbearing computed tomography enables submillimeter 3-dimensional visualization, computational analyses, and enhanced diagnoses in reporting complex degenerative changes more accurately. RESEARCH QUESTION: This study set to compare static posture weightbearing joint angle differences in healthy and varus ankle osteoarthritis patients (compensated and non-compensated). METHODS: Our retrospective assessment included 70 individuals, 44 of whom were diagnosed with advanced varus ankle osteoarthritis, and the remaining 26 were healthy participants to serve as controls. An automatic anatomic coordinate system was applied to each patient's 3-dimensional talus and calcaneus bone reconstructions from weightbearing computed tomography scans. Subtalar and midtarsal joint angles were calculated using Euler angles. RESULTS: We report statistical differences between the healthy group and both advanced varus osteoarthritis groups for midtarsal inversion/eversion. Specifically, both osteoarthritis groups' midtarsal joints were more inverted and plantarflexed as compared to healthy participants. Compensated and non-compensated subtalar joints were statistically different with respect to inversion/eversion. Non-compensated ankles exhibited a similar mean to healthy ankles who were both less inverted than compensated ankles. SIGNIFICANCE: Our study helps physicians to better understand underlying mechanisms of peritalar compensation in varus ankle osteoarthritis. Patients featuring hindfoot compensation on average had a greater subtalar joint angle indicating greater inversion than healthy and non-compensated patients.

Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue?

BACKGROUND: This study investigated the most accurate method for estimating the hip joint center position in clinical 3D gait analysis for young individuals with high amounts of soft tissue. We compared position estimates of five regression-based and two functional methods to the hip joint center position obtained through 3D free-hand ultrasound. METHODS: For this purpose, the data of 14 overweight or obese individuals with a mean age of 13.6 (SD 2.1 yrs) and a BMI of 36.5 (SD 7.1 kg/m2, range 26-52 kg/m2) who underwent standard clinical 3D gait analysis were used. The data of each participant were processed with five regression-based and two functional methods and compared to the hip joint center identified via 3D free-hand ultrasound. FINDINGS: The absolute location errors to 3D free-hand ultrasound for each anatomical plane and the Euclidean distances served as outcomes next to their effects on gait variables. The data suggest that regression-based methods are preferable to functional methods in this population, as the latter demonstrated the highest variability in accuracy with large errors for some individuals. INTERPRETATION: Based on our findings we recommend using the regression method presented by Hara et al. due to its superior overall accuracy of <9 mm on average in all planes and the lowest impact on kinematic and kinetic output variables. We do not recommend using the Harrington equations (single and multiple) in populations with high amounts of soft tissue as they require pelvic depth as input, which can be massively biased when a lot of soft tissue is present around the pelvis.

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects.

Introduction: Surface Electromyographic (sEMG) signals are widely utilized for estimating finger kinematics continuously in human-machine interfaces (HMI), and deep learning approaches are crucial in constructing the models. At present, most models are extracted on specific subjects and do not have cross-subject generalizability. Considering the erratic nature of sEMG signals, a model trained on a specific subject cannot be directly applied to other subjects. Therefore, in this study, we proposed a cross-subject model based on the Rotary Transformer (RoFormer) to extract features of multiple subjects for continuous estimation kinematics and extend it to new subjects by adversarial transfer learning (ATL) approach. Methods: We utilized the new subject's training data and an ATL approach to calibrate the cross-subject model. To improve the performance of the classic transformer network, we compare the impact of different position embeddings on model performance, including learnable absolute position embedding, Sinusoidal absolute position embedding, and Rotary Position Embedding (RoPE), and eventually selected RoPE. We conducted experiments on 10 randomly selected subjects from the NinaproDB2 dataset, using Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and coefficient of determination (R2) as performance metrics. Results: The proposed model was compared with four other models including LSTM, TCN, Transformer, and CNN-Attention. The results demonstrated that both in cross-subject and subject-specific cases the performance of RoFormer was significantly better than the other four models. Additionally, the ATL approach improves the generalization performance of the cross-subject model better than the fine-tuning (FT) transfer learning approach. Discussion: The findings indicate that the proposed RoFormer-based method with an ATL approach has the potential for practical applications in robot hand control and other HMI settings. The model's superior performance suggests its suitability for continuous estimation of finger kinematics across different subjects, addressing the limitations of subject-specific models.

Differential leg and trunk operation during skipping without and with hurdles in bipedal Japanese macaque.

When locomoting bipedally at higher speeds, macaques preferred unilateral skipping (galloping). The same skipping pattern was maintained while hurdling across two low obstacles at the distance of a stride within our experimental track. The present study investigated leg and trunk joint rotations and leg joint moments, with the aim of clarifying the differential leg and trunk operation during skipping in bipedal macaques. Especially at the hip, the range of joint rotation and extension at lift off was larger in the leading than in the trailing leg. The flexing knee absorbed energy and the extending ankle generated work during each step. The trunk showed only minor deviations from symmetry. Hurdling amplified the differences and notably resulted in a quasi-elastic use of the leading knee and in an asymmetric operation of the trunk.

Disease severity versus pain severity: Range of motion differences during single- and multiplanar tasks in women with carpometacarpal osteoarthritis.

In carpometacarpal osteoarthritis (CMC OA) of the thumb, to what extent treatments should be directed by radiographic disease severity versus pain-based indicators remains an open question. To address this gap, this study investigated the relative impact of disease severity and pain severity on the range of motion in participants with CMC OA. We hypothesized larger differences would exist between extremes in the pain severity cohort than the disease severity cohort, suggesting pain modulates movement to a greater extent than joint degradation. Thirty-one female participants (64.6 ± 10.9 years) were grouped as symptomatic or asymptomatic (pain severity cohort) and early stage OA or end-stage OA (disease severity cohort) using radiographs and questionnaires. Kinematics were measured during single-planar and multiplanar range of motion tasks. Joint angle differences between groups were statistically compared. Differences in self-reported pain, function, and disability were evident in both participant cohorts. Notably, substantial distinctions emerged exclusively during multiplanar tasks, with a greater prevalence in the disease severity cohort compared to the pain severity cohort. Participants with end-stage OA also exhibited similar overall area covered during circumduction in comparison to those with early-stage OA, despite having a decreased range of motion at the CMC joint. The study underscores the importance of assessing multiplanar tasks, potentially leading to earlier identification of CMC OA. While movement compensations such as employing the distal thumb joints over the CMC joint were observed, delving deeper into the interplay between pain and movement could yield greater insight into the underlying factors steering these compensatory mechanisms.

Normative 3D gait data of healthy adults walking at three different speeds on an instrumented treadmill in virtual reality.

A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m2) is presented and publicly shared for three walking speed conditions. Raw and processed data are presented for each subject separately and for each walking speed, including data of every single step of both legs. The subject demographics and results from the physical examination are also presented which allows researchers and clinicians to create a self-selected reference group based on specific demographics. Besides the data per individual, data are also presented in age and gender groups. This provides a quick overview of healthy gait parameters which is relevant for use in clinical practice. Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual industrial environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model [1], [2]. Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5m/s and every second the speed was increased with 0.01 m/s until the preferred speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded. The 3D gait data was collected using the D-flow CAREN software. For each subject, raw data of each walking speed condition is provided in .mox files, including the output from the model such as subject data (e.g. gender, body mass, knee and ankle width), center of mass (CoM), marker and force data, kinematic data (joint angles) and kinetic data (joint moments, ground reaction forces (GRFs) and joint powers) for each single step of both legs. Unfiltered and filtered data are included. C3D files with raw marker and GRF data were recorded in Nexus (Vicon software, version 2.8.1) and are available upon request. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to .xls files. For each adult and for each walking speed, an .xls file was created, containing spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of each step of both legs. Overview files per walking speed condition are created in .xls, presenting the averaged gait parameters (calculated as average over all valid steps) of every subject. The processed data is also presented and visualized per gender for different age groups (18-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, ≥70 years). This can serve as normative data for treadmill based 3D gait analyses in adults, applicable for clinical and research purposes. Data is available at OSF.io (https://osf.io/t72cw/).

Development of a Methodology for Low-Cost 3D Underwater Motion Capture: Application to the Biomechanics of Horse Swimming.

Hydrotherapy has been utilized in horse rehabilitation programs for over four decades. However, a comprehensive description of the swimming cycle of horses is still lacking. One of the challenges in studying this motion is 3D underwater motion capture, which holds potential not only for understanding equine locomotion but also for enhancing human swimming performance. In this study, a marker-based system that combines underwater cameras and markers drawn on horses is developed. This system enables the reconstruction of the 3D motion of the front and hind limbs of six horses throughout an entire swimming cycle, with a total of twelve recordings. The procedures for pre- and post-processing the videos are described in detail, along with an assessment of the estimated error. This study estimates the reconstruction error on a checkerboard and computes an estimated error of less than 10 mm for segments of tens of centimeters and less than 1 degree for angles of tens of degrees. This study computes the 3D joint angles of the front limbs (shoulder, elbow, carpus, and front fetlock) and hind limbs (hip, stifle, tarsus, and hind fetlock) during a complete swimming cycle for the six horses. The ranges of motion observed are as follows: shoulder: 17 ± 3°; elbow: 76 ± 11°; carpus: 99 ± 10°; front fetlock: 68 ± 12°; hip: 39 ± 3°; stifle: 68 ± 7°; tarsus: 99 ± 6°; hind fetlock: 94 ± 8°. By comparing the joint angles during a swimming cycle to those observed during classical gaits, this study reveals a greater range of motion (ROM) for most joints during swimming, except for the front and hind fetlocks. This larger ROM is usually achieved through a larger maximal flexion angle (smaller minimal angle of the joints). Finally, the versatility of the system allows us to imagine applications outside the scope of horses, including other large animals and even humans.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy.

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

Hypogravity modeling of upper extremities: an investigation of manual handling in the workplace.

Experiments on the lower limbs are the only approaches being used to study how hypogravity (HG) (0 < g < 1, e.g., Moon: 1/6 g, Mars: 3/8 g) affects human movement. The goal of this study was to expand this field experimentally by investigating the effect of HG on the upper extremities during one-handed manual handling tasks in a sitting posture: static weight holding with an outstretched arm, and slow repetitive weight lifting and lowering motions. The hypothesis was that while completing static and dynamic tasks with elements of repetition in HG, the upper body's tilt (angle regarding the vertical axis) would change differently from Earth's gravity. Specifically, upper arm and spine angles, joint torques, and forces were investigated. Twenty-four healthy participants aged 33.6 ± 8.2 years were involved in the trial. Joint angles were examined using vision-based 3D motion analysis. According to this investigation, there is a correlation between a body tilting backward and a gravity level reduction (p < 0.01). Thus, HG causes postural deviation, and this shows that workplace design must be adapted according to the level of gravity to promote comfortable and balanced body alignment, minimizing stress on muscles and joints. To lower the risk of musculoskeletal disorders (MSDs), enhance overall performance, and increase job satisfaction, proper support systems and restrictions for sitting positions should be taken into account, concerning different levels of gravity.

Comparison of a single-view image-based system to a multi-camera marker-based system for human static pose estimation.

The purpose of this study was to compare human static pose estimation data measured with a single-view image-based system and a multi-camera marker-based system. Thirty participants (20 male/10 female, mean ± standard deviation 29.1 ± 10.0 years old, 1.75 ± 0.10 m tall, 79.1 ± 18.0 kg) performed six repetitions each of static holds of arm-raises and squats, in a different orientation for each repetition. These trials were captured simultaneously with a 120-Hz 12-camera marker-based system and a variable-frequency single-view image-based system. Data for each trial were time-synchronized between the two systems using a near-infrared LED-light that was visible to both systems. Discrete measurements of bilateral shoulder angles during arm-raises and bilateral knee angles during squats were compared between the systems using Bland-Altman plots and descriptive statistics. Pearson correlation coefficients were calculated, comparing the participant trial mean values across systems. Finally, a two-way ANOVA was used to examine whether participant orientation in the capture volume significantly affected either system. Biases for discrete measurements ranged in magnitude from 1.3 to 1.9°, and standard deviations of the differences between systems ranged from 2.4 to 4.7°. Pearson correlation coefficients were all above 0.97, and the ANOVA was unable to find a statistically significant orientation effect for either system. Thus, the marker-based and image-based systems produced similar measurements of static shoulder and knee angles. Future work should examine more complex measurements using volumetric scan-based models and also investigate the ability of single-view image-based systems to measure dynamic movements.