Biomechanics gait analysis data. Temporospatial biomechanical parameters were recorded for subsequent evaluation. Temporal parameters included gait cycle (GC) duration, sec; Cadence or stride rate, steps/min; foot clearance (Cl), cm; walking speed (V), km/h; stride length (SL), cm. Individual time periods of GC (measured as % from GC): stance phase (SP), single support phase (SSP), and the total period of double support phase (DSP). Recording of kinematic parameters was carried out from the joints of the lower ex-tremities: hip, knee, and ankle in the sagittal plane (flexion – extension). The software automatically generated goniograms for each joint in at gait cycle format. The maximum amplitude over GC was recorded in the hip joint (HA, degrees). For the knee joint: first flexion amplitude (Ka1), extension amplitude (Ka2), swing flexion amplitude (Ka3). The maximum amplitude (AA) over GC was analyzed for the ankle joint. The maximum bioelectric activity of muscles over GC, μV, was recorded in the tibialis anterior (TA), gastrocnemius (GA), quadriceps femoris (QA), and hamstring (HM) muscles.

This dataset reports the lower-limb kinematics and kinetics of twenty able-bodied participants walking at multiple stiffness levels (1000 kN/m, 80 kN/m, 40 kN/m, and 25 kN/m) and speeds (0.8 m/s, 1 m/s, and 1.2 m/s). Data were recorded by a Vicon motion capture system and, a unique robotic treadmill, the Variable Stiffness Treadmill 2 (VST 2). The data can be used to identify specific gait patterns, balance strategies, and muscle activation profiles that individuals adopt over compliant surfaces. This information is crucial for designing better controllers for prosthetic limbs, improved rehabilitation protocols, and adaptive assistive devices that can enhance mobility on compliant surfaces, and for optimizing controllers for robotic walkers. The experiment’s data is saved in twenty (20) MATLAB structs, each one corresponding to the data collected from each of the 20 participants. Each struct is named “SUBXX”, where XX represents the single or double-digit participant ID as listed in Table 1 of the accompanying manuscript, e.g. “SUB01” represents the data for participant ID #1, “SUB10” for ID #10 etc. Each struct file contains the anthropometric and demographic information of each participant within the cell “SUBXX.subjectInfo”, as well as the raw experiment data of all trials parsed in gait cycles. The sampling rate for the structure by data type is as follows: • Markers, Joint Angles, Forces, Moments, Powers, CoM - 100Hz • Force mats (GRF, CoP) – 65 Hz • EMG, IMU– 2000 Hz • Heart rate data – 1 Hz (data was resampled to 100Hz for synchronization purposes)

Background: The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.

Methods: First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.

Results: The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.

Conclusions: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/ distinction of muscle activity from background noise in sEMG signals.

This repository contains data from our study titled "Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors." The following file types are included:

- Basic participant demographics can be found in participants.xls.

- README.pdf contains a detailed description of what can be found in each file.

- SX_EMG.mat contains the EMG data for participant X. The file consists of EMG data for left and right erector spinae together with the time vector from that participant.

- SX_Xsens.rar contains the Xsens data for participant X. This includes all joint angles and gait step time stamps from the sensors.

This dataset contains anonymous raw data from a questionnaire on the practice of clinical gait analysis in Europe. This work was initiated by ESMAC (European Society for Movement Analysis in Adults and Children). It includes the analysis of 75 questions answered by 97 laboratories.
The dataset contains 5 files:
- Survey_ESMAC_Questions is a pdf file containing the questions asked.
- Survey_ESMAC_Data.xlsx is an Excel file containing the raw data and the data modified for the analysis. The modifications made were notified in two sheets of the file.
- Survey_ESMAC_Results.pdf is a file containing the export of the results in PDF format.
- Survey_ESMAC_Results.html is a file containing the export of results in HTML format.

The relationship between lameness-related adaptations in equine appendicular motion and muscle activation is poorly understood and has not been studied objectively. The aim of this study was to compare muscle activity of selected fore- and hindlimb muscles, and movement of the joints they act on, between baseline and induced forelimb (iFL) and hindlimb (iHL) lameness. Three-dimensional kinematic data and surface electromyography (sEMG) data from the fore- (triceps brachii, latissimus dorsi) and hindlimbs (superficial gluteal, biceps femoris, semitendinosus) were bilaterally and synchronously collected from clinically non-lame horses (n = 8) trotting over-ground (baseline). Data collections were repeated during iFL and iHL conditions (2–3/5 AAEP), induced on separate days using a modified horseshoe. Motion asymmetry parameters and continuous joint and pro-retraction angles for each limb were calculated from kinematic data. Normalized average rectified value (ARV) and muscle activation onset, offset and activity duration were calculated from sEMG signals. Mixed model analysis and statistical parametric mapping, respectively, compared discrete and continuous variables between conditions (α= 0.05). Asymmetry parameters reflected the degree of iFL and iHL. Increased ARV occurred across muscles following iFL and iHL, except non-lame side forelimb muscles that significantly decreased following iFL. Significant, limb-specific changes in sEMG ARV, and activation timings reflected changes in joint angles and phasic shifts of the limb movement cycle following iFL and iHL. Muscular adaptations during iFL and iHL are detectable using sEMG and primarily involve increased bilateral activity and phasic activation shifts that reflect known compensatory movement patterns for reducing weightbearing on the lame limb. With further research and development, sEMG may provide a valuable diagnostic aid for quantifying the underlying neuromuscular adaptations to equine lameness, which are undetectable through human observation alone.

Walking on compliant substrates requires more energy than walking on hard substrates, but the biomechanical factors that contribute to this increase are debated. Previous studies suggest various causative mechanical factors, including disruption to pendular energy recovery, increased muscle work, decreased muscle efficiency and increased gait variability. We test each of these hypotheses simultaneously by collecting a large kinematic and kinetic data set of human walking on foams of differing thickness. This allowed us to systematically characterise changes in gait with substrate compliance, and, by combining data with mechanical substrate testing, drive the very first subject-specific computer simulations of human locomotion on compliant substrates to estimate the internal kinetic demands on the musculoskeletal system. Negative changes to pendular energy exchange or ankle mechanics are not supported by our analyses. Instead, we find that the mechanistic causes of increased energetic co..., , Matlab R Qualisys Track Manager

The datasets included on this page contain walking data from twelve unimpaired adults walking on a treadmill while wearing bilateral passive ankle exoskeletons. Datasets are four minutes long, and contain kinematic and ground reaction force data, and electromyography from seven leg muscles bilaterally.

The associated Python code can be used to generate data-driven predictive models of response to the ankle exoskeletons. The associated MATLAB code can be used to perform statistical analyses of the data.

This project includes the following software/data packages:

• Modeling and analysis : These files contain CSV files of inverse kinematics results, combined with experimental electromyography data and estimated exoskeleton torque profiles. Code for data-driven modeling and analysis are included.
• Simulation datasets : Datasets from the manuscript: Rosenberg MC, et al., "Predicting walking response to ankle exoskeletons using data-driven models," Submitted to: Journal of the Royal Society Interface, 2020.
• Template Signatures code : This package contains MATLAB-based code package to identify hybrid Template Signatures of center-of-mass dynamics during walking with ankle exoskeletons. Modeling, analysis, and plotting code sets are included.

  Some functions are unmodified from: Mangan NM, Kutz JN, Brunton SL, Proctor JL. Model selection for dynamical systems via sparse regression and information criteria. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2017 Aug 31;473(2204):20170009.

• Template Signatures datasets : This package contains CSV files of center-of-mass kinematics, and foot position estimates from OpenSim 3.3 for 12 unimpaired adults and one adult with post-stroke hemiparesis during walking with and without ankle exoskeletons. Participant demographics are also included. A sample synthetic dataset of a spring-loaded inverted pendulum walker is included for validation of the Hybrid-SINDy algorithm.

MATLAB_pcode.zip (main class + MC detection algorithm) - can check the algorithm performance on attached emg data by on one click solution Matlab pcode of proposed algorithm for movement cycle identification. Averaged SEMG profiles estimation is performed only by using multi-channel SEMG signal. function [x_segment, measure_conf, details] = MC_detection(signal_raw,fs,mode),

REAL_sEMG_MC_KNETIC_REF_database.zip Dataset I.a Nordic walking uphill and downhill, self-selected pace on naturally bumpy terrain with inclination 15 degrees for 2 subjects Muscle (m.) biceps brachii (BB), m. triceps brachii (TB), m. latissimus dorsi (LD), m. pectoralis major (PM), m. deltoid anterior (DA), m. deltoid posterior (DP), m. trapezius descendens (TD), m. gluteus medius (GM) were measured on the right side of the body. M. trapezius transversalis (TT) and m. serratus anterior (SA) were monitored bilaterally. Probands were instrumented by one three-axis accelerometer for gait cycle identification. 16 records with average length 14.88 ± 1.78 MCs, 238 cycles, average period 1.107 ± 0.040 s

Dataset I.b walking and running for two probands on smooth terrain at self-selected pace Signals were measured bilaterally for m. tibialis anterior (TA), m. peroneus longus (PL), m. gastrocnemius lateralis (GasL), m. gastrocnemius medial part (GasM), m. rectus femoris (RF), m. vastus medialis (VM). Pair of foot-switches were fastened under the heel and tip of right and left leg. 20 records with average length 26.75 ± 13.06 MCs in total amount 530 cycles with average period 0.902 ± 0.170 s

Dataset I.c
rowing on a single skiff in natural conditions and ergometer (CONCEPT2) on 7 probands Muscles BB, GM, m. gluteus maximus (Gmax), RF, m. biceps femoris (BF) and m. semitendinosus (SEM) monitored bilaterally Stroke was identified by using a three-axis accelerometer. The accelerometer was attached on the torso with the belt from an elastic fabric. 32 records with average length 17.22 ± 5.64 MCs in total amount 545 cycles with average period 2.760 ± 0.508 s

SYNTHETIC_sEMG_MC_REF_database.zip Synthetic EMG data for walking,running,rowing, Nordic-walking with reference of MC and muscle activity are attached. Details are described in related article.

The signature of cognitive involvement in gait control has rarely been studied using both kinematic and neuromuscular features. The present study aimed to address this gap. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudorandom oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). Subjective mental workload (raw NASA-TLX), cognitive performance (mean reaction time and d-prime), kinematic (steadiness, variability and complexity in the mediolateral and anteroposterior directions) and neuromuscular (duration and variability of motor primitives) control of gait were assessed. The cognitive performance and the number and composition of motor modules were unaffected by simultaneous walking, regardless of the optic flow condition. Kinematic and neuromuscular variability was great..., Subjects:

Healthy young adults, kinematic data: N = 24, 12 men and 12 women, age = 21.67 +/- 2.28 years old. Healthy young adults, EMG data: N = 20, 10 men and 10 women, age = 21.80 +/- 2.42 years old.

The testing session was composed of three blocks performed in a randomised order: (1) three N-back tasks in a seated position (single-task cognitive performance, STC), (2) walking under normal (congruent) optic flow (NOF), and (3) walking under perturbed (continuous mediolateral pseudo-random oscillations) optic flow (POF). In the latter two blocks, the walking tasks were performed under both single-task (STW) and dual-task (DTW) conditions (i.e. walking while performing the N-back tasks). Participants were asked to walk naturally while looking straight ahead. The treadmill speed was adjusted to their preferred walking speed. The blocks (2) and (3) began and ended with a STW condition while the three DTW conditions were performed in a randomized order between the two STW conditions (Sch..., The dataset is composed of 5 files as follows:

1 text file (README_All-files.txt) which contains information about the database and how the files are organized; 1 Microsoft Office Excel spreadsheet (Cognition_All-participants.xlsx) which contains the results of the N-back task for each experimental condition;Â 1 Microsoft Office Excel spreadsheet (RAW-NASA-TLX_All-participants.xlsx) which contains the results of the NASA-TLX questionnaire on perceived cognitive load for each experimental condition; 1 Microsoft Office Excel spreadsheet (Gait_All-participants.xlsx) which contains the results of the kinematic and electromyography analysis for each experimental condition;Â 1 zip file (Gait-parameters.zip) which contains:

1 text file (README_Gait-parameters.txt) which contains information about the gait parameters available in each of the 24 *.csv files; 24 comma-separated value files (P01 to P24.csv) which contain the kinematic and EMG time series for each experimental condition.

Excel..., # Kinematic and neuromuscular characterization of cognitive involvement in gait control in healthy young adults

https://doi.org/10.5061/dryad.bnzs7h4ds

Description of the data and file structure

Subjects:

• Healthy young adults, kinematic data: N = 24, 12 men and 12 women, age = 21.67 +/- 2.28 years old.
• Healthy young adults, EMG data: N = 20, 10 men and 10 women, age = 21.80 +/- 2.42 years old.

The testing session was composed of three blocks performed in a randomised order: (1) three N-back tasks in a seated position (single-task cognitive performance, STC), (2) walking under normal (congruent) optic flow (NOF), and (3) walking under perturbed (continuous mediolateral pseudo-random oscillations) optic flow (POF). In the latter two blocks, the walking tasks were performed under both single-task (STW) and dual-task (DTW) conditions (i.e. walking while performing the N-back tasks). Participants were asked to walk naturally while lookin...

The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.

In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set.

The file “metadata.dat” is available in ASCII and RData format and contains:

Code: the participant’s code

Sex: the participant’s sex (M or F)

Locomotion: the type of locomotion (W=walking, R=running)

Environment: to distinguish between overground (O) and treadmill (T)

Speed: the speed at which the recordings were conducted in m/s

Age: the participant’s age in years

Height: the participant’s height in [cm]

Mass: the participant’s body mass in [kg].

The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”.

Old versions not compatible with the R package musclesyneRgies

The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20th), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively.

The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”.

The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12th), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.

All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.

The Proto-Aging data collection is composed of raw and processed data collected on 20 healthy young adults (age: 28.4 ± 5.0 years, BMI: 22.2 ± 2.8 kg/m2, sex: 10F/10M) and 5 elderly participants (age: 68.0 ± 2.0 years, BMI: 25.6 ± 2.7 kg/m2, sex: 3F/2M). The dataset includes surface electromyography (sEMG) and dynamometry data synchronously collected while the subjects performed a maximum voluntary (and involuntary) isometric contraction test (MVIC), experimental data from a gait assessment (i.e., one static trial and 10 walking trials at self-selected walking speed on level ground; comprising of motion capture, force plates and sEMG data), and the 3D reconstructions of the major thigh muscles of the dominant leg segmented on axial MRI scans. Participants' demographics and additional information are further provided for completeness.

Abstract (en): To access this data collection, please click on the Restricted Data button above. You will need to download and complete the data use agreement and then email it to icpsr-addep@umich.edu. The instructions are in the form.

This study was conducted at the Medical University of South Carolina over the span of one year to delineate the cause/effect relationship between neural output and the biomechanical functions being executed in walking in post-stroke patients. Kinematic, kinetic, and electromyography (EMG) data were collected from 27 post-stroke subjects and from 17 healthy control subjects. Each subject walked on a treadmill at their self-selected walking speed in addition to a randomized block design of four steady-state mobility capability tasks: walking at maximum speed, and walking at self-selected speed with maximum cadence, maximum step length, and maximum step height. Prior to the Medical University of South Carolina Stroke Data (ARRA) study, there has been limited availability of data to understand the electromyography (EMG) modules used by hemiparetic subjects when they walk. Since these modules are thought to represent biomechanical functions performed in a coordinated manner, having data that shows how module use changes as walking task demands change can lead to new understanding of the building blocks of walking behavior. Data were collected from 27 post-stroke subjects and from 17 healthy control subjects for five conditions that were conducted on a treadmill walking over 30 second intervals. These conditions included: Self-Selected (SS) walking speed which was chosen by the participant as their normal walking speed, Fastest Comfortable (FC) where subjects were instructed to find their fastest safe walking speed, High Step (HS) where subjects were instructed to walk with as high of a step as possible while at their SS Speed, Quick Step (QS) where subjects were instructed to walk with as quick of a step as possible at their SS speed, and Long Step (LS) where subjects were instructed to walk with as long as a step as possible at their SS speed. Under each condition kinematics, kinetics (from split belt treadmill force plates) and electromyography (EMG) data were collected. Each subject walked on a treadmill at their Self-Selected walking speed in addition to a randomized block design of four other conditions.

The following equipment were used to collect the data.

Motion Capture System: 12-camera motion capture system (PhaseSpace, Inc., San Leandro, CA) with two linear detectors in each camera, was utilized to measure subject kinematics. The system also utilizes active markers that emit infrared light which are placed on anatomical landmarks of a subject to determine segment size characteristics. It then uses clusters of markers to track the segment motions through 3 dimensional space. The system reports a 3600x3600 pixel resolution (equivalent to 12.4 megapixels of resolution) which equates to sub-millimeter accuracy in the concerned capture volume. The system was controlled with custom prepared software coded in National Instrument's LabVIEW (Austin, TX) that performs automated filtering (3rd order Butterworth with a 25Hz low pass cutoff) and marker interpolation.

6 DOF, 13 Segment, Marker Set: A combination of arrays of markers placed on a rigid surface (clusters) and markers placed on anatomical landmarks. ;

Segments: Head, Right Upper Arm, Left Upper Arm, Right Lower Arm, Left Lower Arm, Trunk, Pelvis, Right Thigh, Left Thigh, Right Shank, Left Shank, Right Foot, Left Foot. ;

Treadmill: Fully instrumented split belt treadmill (FIT, Bertec, Inc.) with incline that measures 3D ground reaction forces and moments.

Electromyograph: MA400,16 channel EMG system:10Hz-2,000Hz -3dB (Motion Lab Systems, Baton Rouge, LA)

Walkway: Gaitrite Platinum instrumented walkway (Franklin, NJ)

Data Collection and Processing: National Instruments DAQ with in-house, custom written software programs for data collection and analysis (LabVIEW, National Instruments Corp., Austin,TX and MATLAB, MathWorks, Natick, MA) For each subject and each condition the data collected includes demographics, clinical assessments, kinetic (from treadmill force plates), kinematic (from active markers), EMG and over-ground spatial temporal measures (GaitRite Platinum Walkway). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely cre...

The global biomechanical acquisition and analysis software market is experiencing robust growth, driven by increasing applications across diverse sectors. The rising prevalence of chronic diseases, coupled with the growing demand for personalized medicine and improved athletic performance, fuels the market expansion. Advanced features such as motion capture, force plate analysis, and electromyography (EMG) integration within these software solutions are significantly enhancing their capabilities, leading to more accurate and detailed biomechanical assessments. This, in turn, is driving adoption across healthcare, sports science, ergonomics, and gaming industries. The market is segmented by application (product design, motion analysis, human-computer interaction, and others) and software type (biomechanical acquisition and biomechanical analysis software). While biomechanical analysis software currently holds a larger market share due to its extensive use in research and clinical settings, biomechanical acquisition software is projected to witness faster growth due to technological advancements in sensor technology and data acquisition methods. The North American region currently dominates the market, largely attributed to high research funding, technological advancements, and the presence of key market players. However, the Asia-Pacific region is expected to exhibit significant growth in the coming years, driven by increasing healthcare expenditure and rising awareness about preventative healthcare measures. Competition in the market is intense, with several established players and emerging companies vying for market share through technological innovations and strategic partnerships. Market restraints include high software costs, the need for specialized training to use the software effectively, and data security concerns. The forecast period (2025-2033) anticipates continued market expansion, propelled by ongoing technological advancements, expanding research and development activities in biomechanics, and increasing adoption across various industries. The development of more user-friendly interfaces and cloud-based solutions is expected to further stimulate market growth. Furthermore, integration with other healthcare technologies, such as wearable sensors and AI-powered diagnostic tools, will create new opportunities within this market. Although challenges related to data privacy and regulatory compliance remain, the overall market outlook for biomechanical acquisition and analysis software is positive, reflecting its increasing importance across various fields. The market is projected to witness a healthy CAGR, leading to substantial revenue growth over the forecast period. A gradual shift toward cloud-based solutions and the increasing use of AI and machine learning capabilities are expected to shape future market trends.

Inter-subject coefficients of variation (CV) and mean and standard deviation (SD) intra-subject CVs calculated across (n = 8) horses and test sessions (session 1 and session 2) for selected superficial muscles.

Standing balance experiment and the measured data-set are fundamental for identifying postural feedback controllers. As the generalized feedback controllers can only be identified from long duration balance data (under random external perturbations), a standing balance experiment is conducted and the long duration motion data was recorded. The data-set includes the perturbation reaction data from eight subjects. Each subject performed four experiment trials, including two quiet standing and two perturbed trials. Each trial lasted five minutes. A total of 80 minutes quiet standing and 80 minutes perturbed standing data are included in this data-set. Recorded information including three dimensional trajectories of thirty-two markers (27 on subjects' trunk and legs and 5 on the treadmill frame), six dimensional ground reaction forces, and nine Electromyography signals (EMGs, on subjects' right leg). In addition, joint angles and torques were calculated using a human body model and inverse dynamics. Basic statistical analysis of the data is also included.

Measured raw data for each subject in each experimental trial includes three files:

1. Mocapxxxx.txt: contains motion capture marker data, ground reaction force, and 76 analog channels. Data was recorded at 100 Hz sampling rate.
2. Mocapxxxx_Motion Analysis_analog.txt: contains 76 high sampling rate (1000Hz) analog channels' data. Analog data is consisted of the analog singal from the froce sensor on the treadmill, EMG signals in the Delsys EMG sensors, and 3 axises acceeleration signals of the Delsys EMG sensors.
3. Recordxxxx.txt: contains the sway motion data of treadmill and the three-axis acceleration data of two Xsens MTi-10 series sensors.

Measured raw data also includes two files of the unloaded trial, which is used for the inertia compensation.

1. Mocap0000.txt: contains motion capture marker data (5 markers on the treadmill frame) and ground reaction forces.
2. Record0000.txt: contains the treadmill sway motion data and the acceleration data (three-axis) of two Xsens MTi-10 series sensors.

Processed data of each subject in each experimental trial contains four files:

1. Mocapxxxx.txt: contains the gap filled motion capture marker data and the inertia compensated ground reaction force data.
2. Motionxxxx.txt: contains the calculated the trajectories of three joints' (hip, knee, and ankle) angles, angular velocities, moments, and joint contact forces.
3. Data_infoxxxx.txt: contains the quality of recorded raw marker data (percentage and biggest duration of missing marker data), and the percentage of removed inertia artifacts in ground reaction forces
4. MotionAnalysis.fig: shows the mean and standard deviation of three joints' trajectories in four experimental trials.

There are two more plots in the processed data folder which shows the joint motion/moment and the raw/compensated ground reaction forces of one example experimental trial (subject 07 trial 03).

The processed data was generated using the code in the 'Processing_Code' folder. The code was wrote using Matlab and the main function is "Data_Processing_Main.m"

More details of the standing balance experiment can be found in the document 'Standing_Balance_Experiment_with_Long_Duration_Random_Pulses_Perturbation.pdf'

This study was performed in accordance with the PHS Policy on Humane Care and Use of Laboratory Animals, federal and state regulations, and was approved by the Institutional Animal Care and Use Committees (IACUC) of Cornell University and the Ethics and Welfare Committee at the Royal Veterinary College.Study design: adult horses were recruited if in good health and following evaluation of the upper airways through endoscopic exam, at rest and during exercise, either overground or on a high-speed treadmill using a wireless videoendoscope. Horses were categorized as “DDSP” affected horses if they presented with exercise-induced intermittent dorsal displacement of the soft palate consistently during multiple (n=3) exercise tests, or “control” horses if they did not experience dorsal displacement of the soft palate during exercise and had no signs compatible with DDSP like palatal instability during exercise, soft palate or sub-epiglottic ulcerations. Horses were instrumented with intramuscular electrodes, in one or both thyro-hyoid muscles for EMG recording, hard wired to a wireless transmitter for remote recording implanted in the cervical area. EMG recordings were then made during an incremental exercise test based on the percentage of maximum heart rate (HRmax). Incremental Exercise Test After surgical instrumentation, each horse performed a 4-step incremental test while recording TH electromyographic activity, heart rate, upper airway videoendoscopy, pharyngeal airway pressures, and gait frequency measurements. Horses were evaluated at exercise intensities corresponding to 50, 80, 90 and 100% of their maximum heart rate with each speed maintained for 1 minute. aryngeal function during the incremental test was recorded using a wireless videoendoscope (Optomed, Les Ulis, France), which was placed into the nasopharynx via the right ventral nasal meatus. Nasopharyngeal pressure was measured using a Teflon catheter (1.3 mm ID, Neoflon) inserted through the left ventral nasal meatus to the level of the left guttural pouch ostium. The catheter was attached to differential pressure transducers (Celesco LCVR, Celesco Transducers Products, Canoga Park, CA, USA) referenced to atmospheric pressure and calibrated from -70 to 70 mmHg. Occurrence of episodes of dorsal displacement of the soft palate was recorded and number of swallows during each exercise trials were counted for each speed interval.
EMG recordingEMG data was recorded through a wireless transmitter device implanted subcutaneously. Two different transmitters were used: 1) TR70BB (Telemetry Research Ltd, Auckland, New Zealand) with 12bit A/D conversion resolution, AC coupled amplifier, -3dB point at 1.5Hz, 2KHz sampling frequency (n=5 horses); or 2) ELI (Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria) [23], with 12bit A/D conversion resolution, AC coupled amplifier, amplifier gain 1450, 1KHz sampling frequency (n=4 horses). The EMG signal was transmitted through a receiver (TR70BB) or Bluetooth (ELI) to a data acquisition system (PowerLab 16/30 - ML880/P, ADInstruments, Bella Vista, Australia). The EMG signal was amplified with octal bio-amplifier (Octal Bioamp, ML138, ADInstruments, Bella Vista, Australia) with a bandwidth frequency ranging from 20-1000 Hz (input impedance = 200 MV, common mode rejection ratio = 85 dB, gain = 1000), and transmitted to a personal computer. All EMG and pharyngeal pressure signals were collected at 2000 Hz rate with LabChart 6 software (ADInstruments, Bella Vista, Australia) that allows for real-time monitoring and storage for post-processing and analysis.
EMG signal processingElectromyographic signals from the TH muscles were processed using two methods; 1) a classical approach to myoelectrical activity and median frequency and 2) wavelet decomposition. For both methods, the beginning and end of recording segments including twenty consecutive breaths, at the end of each speed interval, were marked with comments in the acquisition software (LabChart). The relationship of EMG activity with phase of the respiratory cycle was determined by comparing pharyngeal pressure waveforms with the raw EMG and time-averaged EMG traces.For the classical approach, in a graphical user interface-based software (LabChart), a sixth-order Butterworth filter was applied (common mode rejection ratio, 90 dB; band pass, 20 to 1,000 Hz), the EMG signal was then amplified, full-wave rectified, and smoothed using a triangular Bartlett window (time constant: 150ms). The digitized area under the time-averaged full-wave rectified EMG signal was calculated to define the raw mean electrical activity (MEA) in mV.s. Median Power Frequency (MF) of the EMG power spectrum was calculated after a Fast Fourier Transformation (1024 points, Hann cosine window processing). For the wavelet decomposition, the whole dataset including comments and comment locations was exported as .mat files for processing in MATLAB R2018a with the Signal Processing Toolbox (The MathWorks Inc, Natick, MA, USA). A custom written automated script based on Hodson-Tole & Wakeling [24] was used to first cut the .mat file into the selected 20 breath segments and subsequently process each segment. A bank of 16 wavelets with time and frequency resolution optimized for EMG was used. The center frequencies of the bank ranged from 6.9 Hz to 804.2 Hz [25]. The intensity was summed (mV2) to a total, and the intensity contribution of each wavelet was calculated across all 20 breaths for each horse, with separate results for each trial date and exercise level (80, 90, 100% of HRmax as well as the period preceding episodes of DDSP). To determine the relevant bandwidths for the analysis, a Fast Fourier transform frequency analysis was performed on the horses unaffected by DDSP from 0 to 1000 Hz in increments of 50Hz and the contribution of each interval was calculated in percent of total spectrum as median and interquartile range. According to the Shannon-Nyquist sampling theorem, the relevant signal is below ½ the sample rate and because we had instrumentation sampling either 1000Hz and 2000Hz we choose to perform the frequency analysis up to 1000Hz. The 0-50Hz interval, mostly stride frequency and background noise, was excluded from further analysis. Of the remaining frequency spectrum, we included all intervals from 50-100Hz to 450-500Hz and excluded the remainder because they contributed with less than 5% to the total amplitude.Data analysisAt the end of each exercise speed interval, twenty consecutive breaths were selected and analyzed as described above. To standardize MEA, MF and mV2 within and between horses and trials, and to control for different electrodes size (i.e. different impedance and area of sampling), data were afterward normalized to 80% of HRmax value (HRmax80), referred to as normalized MEA (nMEA), normalized MF (nMF) and normalized mV2 (nmV2). During the initial processing, it became clear that the TH muscle is inconsistently activated at 50% of HRmax and that speed level was therefore excluded from further analysis. The endoscopy video was reviewed and episodes of palatal displacement were marked with comments. For both the classical approach and wavelet analysis, an EMG segment preceding and concurrent to the DDSP episode was analyzed. If multiple episodes were recorded during the same trial, only the period preceding the first palatal displacement was analyzed. In horses that had both TH muscles implanted, the average between the two sides was used for the analysis. Averaged data from multiple trials were considered for each horse. Descriptive data are expressed as means with standard deviation (SD). Normal distribution of data was assessed using the Kolmogorov-Smirnov test and quantile-quantile (Q-Q) plot. To determine the frequency clusters in the EMG signal, a hierarchical agglomerative dendrogram was applied using the packages Matplotlib, pandas, numpy and scipy in python (version 3.6.6) executed through Spyder (version 3.2.2) and Anaconda Navigator. Based on the frequency analysis, wavelets included in the cluster analysis were 92.4 Hz, 128.5 Hz, 170.4 Hz, 218.1 Hz, 271.5 Hz, 330.6 Hz, 395.4 Hz and 465.9 Hz. The number of frequency clusters was set to two based on maximum acceleration in a scree plot and maximum vertical distance in the dendrogram. For continuous outcome measures (number of swallows, MEA, MF, and mV2) a mixed effect model was fitted to the data to determine the relationship between the outcome variable and relevant fixed effects (breed, sex, age, weight, speed, group) using horse as a random effect. Tukey’s post hoc tests and linear contrasts used as appropriate. Statistical analysis was performed using JMP Pro13 (SAS Institute, Cary, NC, USA). Significance set at P < 0.05 throughout.

Rehabilitation post-ACL reconstruction surgery is a lengthy process that involves a variety of exercises, with the goal of achieving leg symmetry. Testing for leg symmetry generally involves a series of tests, which may include walking gait analysis, isokinetic dynamometry, and single leg hop, if able. Isokinetic dynamometry readings have proven useful in understanding leg symmetry by providing a reading of muscle torque during a specific motion. However, the use of surface electromyography (sEMG) may provide further insight into the rehabilitation state of a patient's leg when placed on the mid thigh, by measuring the electrical muscle activity during an exercise. In a cohort of 22 participants (10 healthy, 12 patients), we collected sEMG data during isokinetic dynamometry leg extensions at 90 and 180 degrees. One sEMG sensor was placed on each vastus lateralis, in line with the muscle fiber orientation at the distal 1/3 of the measured distance from the greater trochanter to the super...

Study abstract: Delayed onset of muscle activation is a descriptor of impaired motor control. Activation
onset can be estimated from electromyography (EMG)-registered muscle excitation and
from ultrasound-registered muscle motion, which enables non-invasive measurements in
deep muscles. However, in voluntary activation, EMG- and ultrasound-detected activation
onsets may not correspond. To evaluate this, ten healthy men performed isometric elbow
flexion at 20% to 70% of their maximal force. Utilising a multi-channel electrode
transparent to ultrasound, EMG and M(otion)-mode ultrasound were recorded
simultaneously over the biceps brachii muscle. The time intervals between automated and
visually estimated activation onsets were correlated with the regional variation of EMG
and muscle motion onset, contraction level and speed. Automated and visual onsets
indicated variable time intervals between EMG- and motion onset, median (interquartile
range) 96 (121) ms and 48 (72) ms, respectively. In 17% of trials (computed analysis) or
23% (visual analysis), motion onset was detected before local EMG onset. Multi-channel
EMG and M-mode ultrasound revealed regional differences in activation onset, which
decreased with higher contraction speed (Spearman ρ≥0.45, P<0.001). In voluntary
activation the heterogeneous motor unit recruitment together with immediate motion
transmission may explain the high variation of the time intervals between local EMG- and
ultrasound-detected activation onset.

Data description:

EMG data: folder includes the EMG, torque and synchronization signal data as .otb files. The respective program can be downloaded without costs from http://www.otbioelettronica.it/index.php?lang=en. Data consist of two series (Misome2 and Misome3) of isometric trials at different force levels. The first three digits refer to the subject number.

M-mode ultrasound data: folder includes the M-mode clips of all recorded trials in .tvd format. The respective program can be downloaded for free at http://www.telemedultrasound.com/download/software-downloads/?lang=en. In addition, images of activation onset in DICOM format are provided. The data are sorted for subjects and series (Misome2 and Misome3). The following explanation refers to the filenames of the DICOM images. Usually, the M-mode trace started at the left side synchronously with the synch signal. In this case the rightmost frame of the trace is missing to indicate that the left edge represents the start of the trace. If the file name includes “ons50”, the frame includes the 50. frame = the rightmost frame, still starting with the synch signal. If the filename includes on100, the rightmost frame is the 100. frame and the duration of 50 frames must be added to the visible onset time. Filenames that include “basel” refer to frames that represent a proper delineation of the baseline.

Excel data sheet: Data sheet that includes the computed and visual EMG, M-mode ultrasound and torque onsets, the rate of torque development and the differences between the different types of onsets. The column headers explain the data type, most comprehensively in the first computed data sheet. The colors facilitate the orientation with blue columns referring to torque onsets and green columns referring to ultrasound onsets. The violet EMG channels are those in vicinity to the ultrasound beam. In the second version of each sheet the 5% slowest trials are separated.