Journal of Biomechanics

Time Series. Individual but averaged time series of tibial accelerations across the stance phase of running gait. Metrics. Individual metrics of each participant in each running condition. Pre: first running session, post: second running session, APTA: axial peak tibial acceleration, RPTA: resultant peak tibial acceleration, VLR: vertical loading rate of the ground reaction force. Subject names have been abbreviated to preserve confidentiality.

Details of the sample analyzed in the study: demographic parameters, treatment information, preoperative and follow-up radiographic measures as well as measures predicted by the simulation.

matlab files

Summary data from large-scale sensitivity analyses demonstrating impact of a range of factors on blood oxygenation.

Excel files containing anonymized individual data for 3D peak joint moments and 3D angular impulses of the hip, ankle and knee joints for each cut-off frequency condition.

This dataset includes head kinematics data for 20 healthy young adults performing postural tasks recorded by the Oculus Rift or HTC Vive and Qualisys Track Manager System. It also includes raw data figures for each individual comparing the systems.

MATLAB files to run batch process of OpenSim simulations 2018-12-04 - Josh Baxter - University of Pennsylvania - joshrbaxter@gmail.com The series of simulations can be performed using the main MATLAB script (main.m). Inside this script, the user can change the MTU parameter values that are to be tested. This script uses MATLAB’s parallel computing toolbox to increase the total speed of the batch process. Within each loop, a set of MTU parameters are passed to a function (runsimulation.m) that accomplishes two tasks: 1) update MTU tendon slack lengths to replicate the clinically relevant MTU scenarios and 2) run a forward simulation of the single-leg heel raise activity. In an effort to minimize excess data files, OpenSim output data that are desired to be saved are defined by the user in the main script and stored in a single output MAT file that is updated after each simulation.

Tab delimited text file of raw data containing time in s of each foot initial contact and toe-off for all 14 subjects for all 6 speeds. There are 10 columns of data: Subject ID, running speed (m/s), self identified foot strike pattern (FSP: rear foot striker (RFS), non rear foot striker nRFS)), contact times for Milner algorithm (s), contact times for Alvim algorithm (s), contact times for modified Alvim algorithm (s), contact times from force plate (ICFP) (s), toe-off times for Fellin algorithm (s), toe0off ties from force plate (TOFP) (s), observed foot strike pattern (from foot kinematics).

Twenty-five participants (19 females, mean age (S.D) = 30 (10) years, mean body mass = 68 (11) kg, height = 170 (7) cm) with no wheelchair experience were included in the study. Participants propelled the hand-rims of a wheelchair on a large (3.0 x 5.0 m) motor-driven treadmill, while their kinematics were measured with three IMUs (attached to the participants’ sternum, the wheelchair’s frame, and right wheel axle) and the front-wheel load was measured using custom-made load pins (in both front wheel axes). Before the treadmill sessions, participants received a 10-minute overground wheelchair training to get familiar with the wheelchair and a 10-minute training on the treadmill (see Fig. 1). After three treadmill sessions, drag tests were performed on the treadmill to obtain rolling resistance coefficients of the (small) front and (large) rear wheels. To simulate different wheelchair characteristics and push styles, the treadmill session was repeated six times with different tire pressures (1.75 bar, 3.5 bar, 5.25 bar) or added mass (0 kg, 5 kg, 15 kg), see Fig. 1, and with three pushing styles (no trunk motion at 1.2 m/s [style 1], normal trunk motion at 1.2 m/s [style 2], normal trunk motion at 1.7 m/s [style 3]). By following a metronome (25 beats/min in pushing style 1 and 40 beats/min in pushing style 2 and 3), participants were stimulated to make long pushes accompanied by ‘natural’ trunk motion. Each treadmill session consisted of 30s familiarization to the new situation, after which participants propelled 60s in each pushing style. In this way, a dataset was composed of eighteen (three push styles and six treadmill sessions) 60s-time trials per participant. The order of the treadmill sessions differed per participant. The load on the front wheels is expressed as percentage of the total weight (of participant + wheelchair). The dataset consists of 11 columns representing the following variablesv_wc: linear velocity of the wheelchair in m/sa_wc: linear acceleration of the wheelchair in m/s^2av_tr: Angular velocity of trunk (around sagittal axis) in rad/saa_tr: Angular acceleration of trunk (around sagittal axis) in rad/s2ang_tr: Trunk inclination angle in radlaz_tr: Trunk acceleration perpendicular to the frontal plane of the trunk in m/s2lay_tr: Trunk caudal-cranial acceleration in m/s2lar_tr: Magnitude of trunk acceleration vector in m/s2F: front wheel-load as percentage of the total weight (of participant + wheelchair)subjectnr: subject numberblocknr: block number in whichblock 1: rear wheel tyre pressure = 5.25; added mass = 0 kg (practice/familiarization session)block 2: rear wheel tyre pressure = 5.25; added mass = 5 kgblock 3: rear wheel tyre pressure = 5.25; added mass = 15 kgblock 4: rear wheel tyre pressure = 5.25; added mass = 0 kgblock 5: rear wheel tyre pressure = 3.50; added mass = 0 kgblock 6: rear wheel tyre pressure = 1.75; added mass = 0 kg See also the file 'additional information.pdf'.

Twenty-five participants (19 females, mean age (S.D) = 30 (10) years, mean body mass = 68 (11) kg, height = 170 (7) cm) with no wheelchair experience were included in the study. Participants propelled the hand-rims of a wheelchair on a large (3.0 x 5.0 m) motor-driven treadmill, while their kinematics were measured with three IMUs (attached to the participants’ sternum, the wheelchair’s frame, and right wheel axle) and the front-wheel load was measured using custom-made load pins (in both front wheel axes). Before the treadmill sessions, participants received a 10-minute overground wheelchair training to get familiar with the wheelchair and a 10-minute training on the treadmill (see Fig. 1). After three treadmill sessions, drag tests were performed on the treadmill to obtain rolling resistance coefficients of the (small) front and (large) rear wheels. To simulate different wheelchair characteristics and push styles, the treadmill session was repeated six times with different tire pressures (1.75 bar, 3.5 bar, 5.25 bar) or added mass (0 kg, 5 kg, 15 kg), see Fig. 1, and with three pushing styles (no trunk motion at 1.2 m/s [style 1], normal trunk motion at 1.2 m/s [style 2], normal trunk motion at 1.7 m/s [style 3]). By following a metronome (25 beats/min in pushing style 1 and 40 beats/min in pushing style 2 and 3), participants were stimulated to make long pushes accompanied by ‘natural’ trunk motion. Each treadmill session consisted of 30s familiarization to the new situation, after which participants propelled 60s in each pushing style. In this way, a dataset was composed of eighteen (three push styles and six treadmill sessions) 60s-time trials per participant. The order of the treadmill sessions differed per participant. The load on the front wheels is expressed as percentage of the total weight (of participant + wheelchair). The dataset consists of 11 columns representing the following variablesv_wc: linear velocity of the wheelchair in m/sa_wc: linear acceleration of the wheelchair in m/s^2av_tr: Angular velocity of trunk (around sagittal axis) in rad/saa_tr: Angular acceleration of trunk (around sagittal axis) in rad/s2ang_tr: Trunk inclination angle in radlaz_tr: Trunk acceleration perpendicular to the frontal plane of the trunk in m/s2lay_tr: Trunk caudal-cranial acceleration in m/s2lar_tr: Magnitude of trunk acceleration vector in m/s2F: front wheel-load as percentage of the total weight (of participant + wheelchair)subjectnr: subject numberblocknr: block number in whichblock 1: rear wheel tyre pressure = 5.25; added mass = 0 kg (practice/familiarization session)block 2: rear wheel tyre pressure = 5.25; added mass = 5 kgblock 3: rear wheel tyre pressure = 5.25; added mass = 15 kgblock 4: rear wheel tyre pressure = 5.25; added mass = 0 kgblock 5: rear wheel tyre pressure = 3.50; added mass = 0 kgblock 6: rear wheel tyre pressure = 1.75; added mass = 0 kg See also the file 'additional information.pdf'.