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250 results for “Knee joints”
Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets
<p>This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).</p> <p><strong>DATA ORIGINS & LICENSE INFORMATION</strong></p> <p>The data comes from four existing open datasets collected by others:</p> <p>Schreiber & Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants</p> <ul> <li>article: https://www.nature.com/articles/s41597-019-0124-4</li> <li>dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767</li> </ul> <p>Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals</p> <ul> <li>article: https://peerj.com/articles/4640/</li> <li>dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711</li> </ul> <p>Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals</p> <ul> <li>article: https://www.nature.com/articles/s41598-019-38748-8</li> <li>dataset: https://data.mendeley.com/datasets/svx74xcrjr/3</li> </ul> <p>Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions</p> <ul> <li>article: https://www.sciencedirect.com/science/article/pii/S0021929021001007</li> <li>dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1</li> </ul> <p>In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively.<br> The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).</p> <p>We have modified the datasets by analyzing the data with musculoskeletal simulations & analysis software (OpenSim).<br> In this dataset, we publish modified data as well as some of the original data.</p> <p><br> <strong>STRUCTURE OF THE DATASET</strong><br> The dataset contains two kinds of text files: those starting with "predictors_" and those starting with "response_".</p> <p>Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks.<br> Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on.<br> The file names are of the form "predictors_X" for predictors and "response_X" for responses, where X describes which response (outcome) variable is predicted with them.<br> X can be:<br> - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments<br> - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment<br> - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment<br> - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments<br> - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment<br> - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment<br> - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments<br> - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment<br> - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment<br> - MFR_common: the medial force ratio for the entire stance phase<br> - MFR_LR: the medial force ratio for the first peak of stance<br> - MFR_TE: the medial force ratio for the second peak of stance</p> <p>The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials.<br> The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.</p> <ul> <li>DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}</li> <li>SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject "3").</li> <li>KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.</li> <li>MASS is the mass of the subject in kilograms</li> <li>HEIGHT is the height of the subject in millimeters</li> <li>BMI is the body mass index of the subject</li> <li>WALKING_SPEED is the mean walking speed of the subject during the trial</li> <li>HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject's pelvis markers at the instant of heel strike</li> <li>AGE is the age of the subject in years</li> <li>GENDER is an integer/boolean where {1=male, 0=female}</li> </ul> <p>The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial.<br> The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).</p> <p><br> See our journal article "Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks" (https://doi.org/10.1007/s10439-023-03278-y) for more information.</p> <p>Questions & other contacts: jere.lavikainen@uef.fi</p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Grood and Suntay joint coordinate system for knee kinematics
<p>Grood and Suntay joint coordinate system for description of knee kinematics</p> <p>This software implements the Grood and Suntay joint coordinate system for the clinical description of the three-dimensional motion of the knee. The Grood and Suntay joint coordinate system is described in the research paper :</p> <p>Grood E.S., Suntay W.J., “A joint coordinate system for the clinical description of three-dimensional motions: application to the knee”, J Biomech Eng., 1983 May; 105(2), 136-44, 1983.</p> <p>The software was used in producing the numerical results shown in various research papers such as :</p> <p>Arsene, CT, Gabrys, B., “Probabilistic finite element predictions of the human lower limb model in the total knee replacement”, Med Eng Phys, 2013 Aug; 35(8): 1116-32, 2013.</p> <p>Description of the Matlab files:</p> <p>Uncerpasl.m – starts the process of calculating the knee joint coordinate system;</p> <p>Anglesn.m – calculates the angles and the displacements of interest for the tibio-femoral component</p> <p>Anglespf.m – calculates the angles and the displacements of interest for the patello-femoral component</p> <p>Readinput.m – reads the text files containing the raw data obtained from the Finie Element simulations such as :</p> <p>a) femur_COG_I_X - means the X coordinate of the I point which defines a coordinate system together with points J and K with respect to the center of gravity (COG) of the femoral component.</p> <p>b) femur_COG_X - means the X coordinate of the center of gravity (COG) of the femoral component which together with points I, J , K forms a sort of local coordinate system attached to the femoral component.</p> <p>All the text files such as femur_COG_I_X_1.txt, femur_COG_I_Y_1.txt, etc were produced with the finite element software PAM-CRASH/PAMOPT from the company ESI, Paris, France (<a href="https://www.esi-group.com/">https://www.esi-group.com</a>). The text files contain a single result consisting of 0.12 seconds of the passive flexion cycle and therefore are of no real use but only to verify that the Grood and Suntay Matlab software code works.</p> <p>Please acknowledge the project financed by the European Union Framework Programme 6 (FP6) entitled Decision Support Software for Orthopaedic Surgery and Mr Corneliu T.C. Arsene if you are going to use this software anywhere in your work. The license for this software is a Creative Commons Attribution 4.0 International License (CCL). This project has also a DOI : 10.5281/zenodo.2136822</p> <p>It is provided here with no warranty. Direct all questions and requests to <a href="mailto:galenpalimpsestproject@gmail.com">galenpalimpsestproject@gmail.com</a>.</p>
Limb work and joint work minimisation reveal an energetic benefit to the elbows-back, knees-forward limb design in parasagittal quadrupeds. Supplementary material including data, simulation code and simulation results
<p>Quadrupedal animal locomotion is energetically costly. We explore two forms of mechanical work that may be relevant in imposing these physiological demands. Limb work, due to the forces and velocities between the stance foot and the centre of mass, could theoretically be zero given vertical limb forces and horizontal centre of mass path. To prevent pitching, skewed vertical force profiles would then be required, with forelimb forces high in late stance and hindlimb forces high in early stance. By contrast, joint work – the positive mechanical work performed by the limb joints – would be reduced with forces directed through the hip or shoulder joints. Measured quadruped kinetics show features consistent with compromised reduction of both forms of work, suggesting some degree of, but not perfect, inter-joint energy transfer. The elbows-back, knees-forward design reduces the joint work demand of a low limb-work, skewed, vertical force profile. This geometry allows periods of high force to be supported when the distal segment is near vertical, imposing low moments about the elbow or knee, while the shoulder or hip avoids high joint power despite high moments because the proximal segment barely rotates – translation over this period is due to rotation of the distal segment.</p>
Robot-aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance
<p>Dataset linked with the manuscript "Robot-aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance". Please see attached readme document for details</p>
Study of TG-C in Patients With Grade 3 Degenerative Joint Disease of the Knee
ClinicalTrials.gov study NCT01221441. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effectiveness of Muscle Energy Techniques and Mulligan Mobilization Along With Conventional Physical Therapy in Knee Joint Osteoarthritis Patients
ClinicalTrials.gov study NCT06954363. IPD Sharing: YES. Countries: 1. Publications: 5.
Persistent Postoperative Pain and Joint Stiffness After Total Knee Arthroplasty Performed for Osteoarthritis
ClinicalTrials.gov study NCT02626533. IPD Sharing: NO. Countries: 1. Publications: 1.
Tranexamic Acid in Knee Joint Surgery
ClinicalTrials.gov study NCT02278263. IPD Sharing: NO. Countries: 1. Publications: 12.
Efficacy of Ipack Block and Surgeon's Joint Infiltration in Total Knee Arthroplasty
ClinicalTrials.gov study NCT03653416. IPD Sharing: NO. Countries: 1. Publications: 2.
A Study Evaluating the Safety, Tolerability, and Efficacy of SM04690 Injected in the Target Knee Joint of Moderately to Severely Symptomatic Osteoarthritis Subjects
ClinicalTrials.gov study NCT02536833. IPD Sharing: NO. Countries: 1. Publications: 2.
Limb work and joint work minimisation reveal an energetic benefit to the elbows-back, knees-forward limb design in parasagittal quadrupeds. Supplementary material including data, simulation code and simulation results
Open the record for dataset details and reuse information.
Rest-activity daily rhythm and physical activity levels after hip and knee joint replacement: the role of actigraphy in orthopedic clinical practice.
<p>Elective hip and knee joint replacement for osteoarthritis are cost-effective surgical procedures requiring specific rehabilitation programs. Actigraphy is widely used in both research and clinical practice to study activity patterns with great accuracy and validity but it has never been utilized in orthopedic patients. Therefore, the aim of this study was to objectively assess, through actigraphy, physical activity (PA) levels and rest-activity daily rhythm (RAR) in patients undergoing hip or knee joint replacement and hospitalized for ten days after surgery. Twenty subjects (11 males and 9 females; age: 62.68 ± 10.39 years old; BMI: 29.03 ± 3.92 kg/m2) wore the Actiwatch 2 actigraph (Philips Respironics, Portland, OR) to record both PA levels and RAR for 11 consecutive days and data on subjective scores of pain, by a visual analog scale (VAS), and functional and clinical scores were collected. The following time-points were considered for the statistical analysis: pre-surgery (PRE), the first (POST1), the fourth (POST4) and the tenth (POST10) day after surgery. RAR were processed with the population mean cosinor to describe the rhythm's characteristics (acrophase, amplitude and MESOR) while data on actigraphy-based PA, VAS, and functional clinical scores were compared among PRE, POST1, POST4 and POST 10 with the RM-ANOVA or the non-parametric Friedman test. The day after surgery the subjects had a flattened RAR compared to the other conditions: lower values were detected in POST1 compared to both PRE, POST4 and POST10 for MESOR (<em>p</em> < .0001; η2p = .71, large) and amplitude (<em>p</em> < .0001; η2p = .63, large) while RAR's acrophase (<em>p</em> < .0001; η2p = .61, large) was delayed in PRE (16:45) compared to POST1 (12:42), POST4 (14:38), and POST10 (14:38). PA levels were significantly lower at POST 1 (76.7 ± 33.4) compared to PRE (192.3 ± 91.5; <em>p</em> < .0001 and ES: 1.68, large), POST4 (137.9 ± 45.9; <em>p</em> < .0001 and ES: 1.54, large), and POST10 (131.2 ± 54.3; <em>p</em> < .0001 and ES: 1.21, large) whereas VAS and functional clinical values significantly improved at POST10. Hip and knee joint replacement negatively influenced RAR and PA the first day after surgery but a progressive improvement in the circadian pattern of rest-activity cycle, PA levels, VAS and functional ability was recorded from POST4 to POST10. Actigraphy has the ability to collect real-life data without interfering with clinical practice and give clinicians a new measure of performance that is currently not available. This tool could allow to identify patients with disrupted circadian rhythm and reduced PA in the peri-operative period in orthopedic surgery, and timely intervene on these subjects with personalized rehabilitative intervention.</p>
Unilateral below-knee prosthesis users walking on uneven terrain: the effect of adding a toe joint to a passive prosthesis
<p>See attached ReadMe.txt</p>
Exploring the Interaction Between Proprioceptive Sensitivity in the Hip, Knee, and Ankle Joints, Alongside Stability Limits Evaluated Through Computerized Posturography, in Elderly Individuals Afflicted with Fibromyalgia Syndrome.
<p><strong><span>Objectives: </span></strong><span>This study investigates proprioceptive sensitivity and stability limits in patients with Fibromyalgia syndrome (FMS) compared to healthy controls, focusing on joint reposition sense (JRS) and limits of stability (LOS). By delineating differences between the groups, it aims to uncover the proprioceptive deficits that contribute to balance impairments in FM sufferers. Additionally, the study explores correlations between JRS and LOS within the FM group, potentially informing targeted therapeutic strategies to enhance patient outcomes.</span></p> <p><span> </span></p> <p><strong><span>Methods: </span></strong><span>Fifty-four individuals with FM and 54 healthy controls participated in this research. Using precise digital inclinometers, JRS was evaluated at the hip, knee, and ankle joints. LOS was assessed using dynamic posturography with advanced iso-free technology. Various parameters were measured, including reaction time, maximum excursion, and directional control. </span></p> <p><span> </span></p> <p><strong><span>Results:</span></strong><span> Individuals with FM exhibited significant differences in JRS and LOS compared to healthy controls. For JRS errors in the hip, knee, and ankle joints (mean ± SD), the FM group showed 3.2° ± 0.6°, 2.9° ± 0.5°, and 3.5° ± 0.7°, respectively, while the control group exhibited 1.1° ± 0.4°, 1.0° ± 0.3°, and 1.2° ± 0.4°, respectively. In LOS, FM individuals had a reaction time of 1.8 ± 0.3 seconds, maximum excursion of 54.6% ± 6.2%, and directional control of 65.2% ± 5.8%, while healthy controls showed 0.9 ± 0.2 seconds, 72.4% ± 7.1%, and 77.8% ± 6.9%, respectively. </span></p> <p><span> </span></p> <p><strong><span>Conclusions:</span></strong><span> Correlation analysis revealed significant associations between JRS parameters and LOS outcomes within the FMS group, indicating that better proprioception was linked to improved postural stability. These findings have clinical implications for assessing and managing balance impairments in individuals with FMS, potentially contributing to the development of targeted interventions to improve their quality of life.</span></p>
Predictive factors of inpatient rehabilitation stay and post-discharge care burden after joint replacement for hip and knee osteoarthritis: a retrospective study on 1,678 patients.
<p>Dataset of the study entitled "<span>Predictive factors of inpatient rehabilitation stay and post-discharge care burden after joint replacement for hip and knee osteoarthritis: a retrospective study on 1,678 patients"</span></p>
Optimization of a Fast-track Concept for Knee Joint Replacement
ClinicalTrials.gov study NCT03920930. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Exercise Training and Knee Joint Proprioception in Rheumatoid Arthritis
ClinicalTrials.gov study NCT06887725. IPD Sharing: NO. Countries: 1. Publications: 1.
Human Growth Hormone Injections in the Knee Joint to Treat Osteoarthritis
ClinicalTrials.gov study NCT06354023. IPD Sharing: NO. Countries: 1. Publications: 5.
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