Find research datasets worth reusing

opencc-byOct 2017View details →

Bā Jiā Jiāng (eight generals) Motion Capture

八家將甘柳大神展演四角動作捕捉 Bā Jiā Jiāng (eight generals) are performers painted their faces, playing four generals Gan, Liu, Xie, Fan, and the Four Seasons God and other characters. When the celebration festival parade, as Lord God's the guardian team. It is an intangible cultural heritage of Taiwan's important religious and performing arts. This study uses optical motion capture technology to capture the actions of the Bā Jiā Jiāng, recorded as digital information. Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Feb 2024View details →

Dataset of Motion Capture of Cyclists

The system used is the  Phasespace Impulse X2E motion capture system, featuring active LEDs. This system uses 24 cameras designed for capturing 3D motion through modulated LEDs. These cameras incorporate pairs of linear scanner arrays operating at high frequencies, enabling the capture of the position of bright spots of light generated by the LEDs.  Methodology for Mocap Data Collection Participants received detailed information about the mocap data collection procedure and purpose. Informed consent was obtained, ensuring understanding and agreement and a pre-session questionnaire collected demographic and health information. Next, the participant’s road or time trial (TT) bike was placed on a turbo trainer and the meticulous marker placement on the cyclist’s body. Cyclists followed a specific workout plan based on their bike type. The plan included warm-up, cycling positions, and recovery intervals. Sensors were strategically put mostly on the back side of the torso due to the body position while cycling. Workout: - Warm-Up: 1 minute in any position. - 30 seconds at 60 RPM - 15 seconds at 75 RPM - 15 seconds at 90 RPM - 10 seconds at 100 RPM - 10 seconds at 110 RPM Cycling Positions and Cadences For Road Bikes: The workout was performed for each of the following positions with 1 minute between each position to allow for recuperation: - Straight Arms - Comfortable - Aggressive - Aero Position - Standing (not above 90 RPM) For TT Bikes: The workout was performed for each of the following positions with 1 minute between each position to allow for recuperation: - Comfortable - Aero Position - Standing (not above 90 RPM) Participants were monitored throughout the session to ensure well-being and comfort. They had the option to terminate the procedure if they feel unwell or wish to stop. The motion capture dataset is organized as follows. There is a dedicated folder for each participant, labeled according to the following naming convention: "1_RB_M42_20230719_PK" <ul> <li>1 = Index Number</li> <li>RB = Road Bike (RB) or Time Trial bike (TT) </li> <li>M42 = Gender (M/F) and age</li> <li>20230719 = Capture Date (YYYYMMDD)</li> <li>PK = Unique Identifier of participant</li> </ul> In each folder there are three files: <ol> <li>C3D = Motion Capture Raw Data</li> <li>BVH = BioVision Hierarchy (BVH), data mapped to skeletal data ready for animation (errors may still remain)</li> <li>GPX = Data from related zwift workout</li> </ol> The Project (Smart Cyclo) is funded by the European Union Recovery and Resilience Facility of the NextGenerationEU instrument, through the Research and Innovation Foundation.

opencc-by-4.0Jul 2022View details →

Optical motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation

This is the data belonging to the publication "Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data". This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations. Please see the README and the publication for further details.

opencc-by-4.0Jun 2024View details →

Lower-body Inertial Sensor and Optical Motion Capture Recordings of Walking and Running

<pre>This dataset contains lower-body inertial sensor (IMU) data and optical motion capture (OMC) data from ten participants walking and running overground at different speeds. The data recording is described in this publication: Dorschky, E., Nitschke, M., Seifer, A. K., van den Bogert, A. J., & Eskofier, B. M. (2019). Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. Journal of biomechanics, 95, 109278. (https://doi.org/10.1016/j.jbiomech.2019.07.022) Please look at the README.txt file for further information.</pre>

opencc-by-4.0Sep 2019View details →

Dataset: Indoor Localization with Narrow-band, Ultra-Wideband, and Motion Capture Systems

Localization Dataset README In this, data for BLE and UWB calibration is included through the use of UWB and OptiTrack Motion capture system respectively. There are two sets of data each covering two scenarios; these being walking and trolley. The two groups containing the data are laid out identically as indicated below. Bluetooth Low Energy / Ultra-Wideband | Session ID | 8 x AoA | 8 x RSSI | BLE x | BLE y | UWB x | UWB y | Where : - AoA  is Angle of Arrival, with two values given for each anchor node. - RSSI is the Received Signal Strength Indicator, also with two values given for each anchor node. - BLE x and y location estimates [1]. - UWB x and y location estimates. Number of Samples (BLE/UWB) The datasets contains the following number of samples: - Walk - 4896 samples. - Trolley - 4856 samples. Ultra-Wideband / OptiTrack Motion Capture | Session ID | 4 x CIR | 4 x PSA | Distance | UWB x | UWB y | OPT x | OPT y | Where: - CIR is Channel Impulse Response for each received signal from the Anchors to the target tag. - PSA is the Preamble Symbol Accumulation, from each of the 4 Anchors to the target tag. - Distances of the tag to each of the 4 anchors. - UWB x and y location estimates. - OptiTrack location estimates. Number of Samples (UWB/OPT) - Walk - 2797 samples. - Trolley - 3202 samples. Total Number of Samples : 15751 [1]: A Khan, T Farnham, R Kou, U Raza, T Premalal, A Stanoev, W Thompson, "Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies", IEEE Global Communications Conference (GLOBECOM) 2019

openJun 2023View details →

CARRT - Motion Capture Data for Robotic Human Upper Body Model

As advancements in the study of human activity have progressed in recent years, researchers have directed their attention towards analyzing human daily activities to investigate a diverse range of performance metrics unconsciously optimized by individuals while engaged in specific tasks. To replicate these movements in robotic systems based on human models, researchers have developed a framework for robot motion planning capable of utilizing various optimization methods to reproduce such motions through human demonstrations. In this process, capturing the movements of the human body and the objects involved in the demonstrations is imperative, as they provide essential information for the motion planning procedure. The objective of this dataset is to present human motion data while performing activities of daily living. This dataset encompasses comprehensive and precise whole-body motion data of individuals collected using a Vicon motion capture system, which facilitated the development of a full-body model integrated into OpenSim and MATLAB. The dataset comprises nine different daily living activities and eight Range of Motion activities performed by ten healthy participants. A publicly accessible whole-body human motion database has been established, encompassing raw motion data in .c3d format, motion data in .csv format for the OpenSim model, and post-processed motion data for the MATLAB model.

opencc-by-4.0Nov 2023View details →

The Motion Capture of Dress in Etruscan Dance: preliminary tests

The process or technique of recording patterns of movement digitally is now regularly used for different human movements, such as dance. But what about textiles? Recording textiles presents several challenges, notably due to the lack of a fixed structure and the great variability of the material. But there would be also opportunities. The Motion Capture system has been used to record body movements of specific spinning and weaving process (see the experiments at the Centre for Textile Research, University of Copenhagen, Denmark). But in which extent can it be used for textiles themselves? What would be the possibilities offered by this technic? Can the challenges be solved? How and in which extent this technic could produce new information about ancient clothing and society? The two preliminary MoCap tests published here present the case of ancient clothes that were used in performative rituals during pre-Roman Antiquity. They specifically build on the results of the Marie Curie project TEXDANCE (ID n° 839799) that aimed at studying dress in Etruscan dance. One of the objectives was to study dress movement in dance. Clothes give indeed complementary information to the body movements, which permit to better understand the kind of dance represented in Etruscan iconography. The preliminary tests were performed with a skirt in viscose. The aim was to remove existing doubts about the possibility or not of digitally capturing the movements of dress, and to determine what were the difficulties to be solved when working with objects as unstable as textiles. The tests were carried out on the FR-SCV digital platform, with Laurence Willard Delbarre, instrumentation and experimental techniques engineer. Test 1: the first phase of the experimentation, which consists of placing the different sensors on the cloth, is crucial. It was decided to resolve as much as possible the problem of variability between two sensors, by multiplying the sensors over the area to be recorded, and by placing them very close to each other. The aim was to keep as much of a continuous trace in the structure as possible. We began the investigation by focusing on the movement produced by the lower part of the garment selected for the experiment. The aim was to see how it behaved in relation to the movements of the dancing body. Thus, a Velcro strip was stuck all over the garment, to which the sensors were attached. This resulted in a horizontal strip of sensors in the lower part of the skirt. And in order to give the garment an orientation, we placed two sensors vertically on the front part. The next phase of the experiment was to record the movement of the skirt. This skirt (worn by the author) was positioned in the centre of the MoCap system, and therefore was surrounded by multiple high-speed cameras. To set the fabric in motion, the movements performed were as follows: 1. walk; 2. turn around; 3. turn from side to side; 4. alternating walk and lift of each leg; 5. alternating jump and lift of each leg. The first results were very satisfying since we were able to follow the different movements of the fabric. Some sensors tended to sometimes disappear from the capture, notably during the first four seconds of the recording, then occasionally during the recording. This is due to minor issues with calibration and placement of the cameras around the body. Despite these minor issues, we can track and calculate the movement of the cloth. Test 2: with test 1, we discovered that we were missing information regarding the cloth's height, its overall movement, and its movement relative to the upper body. So, we decided to add vertical lines of sensors on the right and left sides of the fabric, and the wearer put on the MoCap upper jacket with sensors placed on several crucial articulation points. Subsequent testing and recording, based on a similar sequence of movements, yielded more results on the overall trajectory of the garment, on the relationship between the fabric and the moving body, on fabric behaviour, and ergonomics. These MoCap tests are possible thanks to the support of the CPER ENHANCE and the Research Federation Visual Sciences and Cultures (University of Lille, France).

opencc-by-4.0Nov 2023View details →

The Motion Capture of Etruscan Dance: the castanet player

Hypothesis of reconstruction of the movement possibly performed by the Etruscan castanet player. Motion capture achieved at the Research Federation Visual Sciences and Cultures (University of Lille, France), with the CPER ENHANCE, based of the PhD thesis completed by Audrey Gouy in 2017 (PSL-EPHE Paris, France, and University Ca' Foscari of Venice, Italy).

opencc-by-4.0Mar 2024View details →

Dataset to "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture"

This dataset is related to the paper "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture". Contents: Recordings comparing a human performance and two robot performances (named case A and case B). <ul> <li>Synchronised motion-capture data (time, bow markers, nut marker and bridge marker; Fs = 240 fps - .csv files)</li> <li>Synchronised audio recordings (sound in front of the instrument; Fs = 44100 Hz - .wav files)</li> <li>Music score</li> <li>Read-me file with further details</li> </ul>

dryad32/100

Beneficial wake-capture effect for forward propulsion with a restrained wing-pitch motion of a butterfly

Unlike other insects, a butterfly uses a small amplitude of the wing-pitch motion for flight. From an analysis of the dynamics of real flying butterflies, we show that the restrained amplitude of the wing-pitch motion enhances the wake-capture effect so as to enhance forward propulsion. A numerical simulation refined with experimental data shows that, for a small amplitude of the wing-pitch motion, the shed vortex generated in the downstroke induces air in the wake region to flow towards the wings, which enables a butterfly to capture this induced flow and to acquire an additional forward propulsion. When the amplitude of the wing-pitch motion exceeds 45o, the flow induced by the shed vortex drifts away from the wings; it attenuates the wake-capture effect and causes the butterfly to lose a part of its forward propulsion. Our results provide a physical elucidation for a butterfly adopting a small amplitude of the wing-pitch motion to enhance the wake-capture effect and forward propulsion. This work clarifies the variation of the flow field correlated with the wing-pitch motion, which is useful in the design of a micro-aerial vehicle.

opencc-zeroAug 2021View details →

opencc-by-4.0Mar 2023View details →

A New Dataset of People Flow in an Industrial Site with UWB and Motion Capture Systems

Improving performance and safety conditions at industrial sites remain key elements of the Group's strategy. Major issues require the ability to dynamically locate people and assets on the site. Currently, the security and regulation of access to areas with different characteristics (types of tasks, level of risk, or confidentiality...) are often carried out with badge doors or barriers. These means present several weaknesses in the face of inappropriate movements of people, but also of objects or tools. Also, there is increasing use of technological devices requiring precise localization in the industrial environment such as AGVs (mobile robots or drones) or augmented reality devices. It is therefore becoming essential to have tools to dynamically manage these flows of people or goods associated with precise location technologies. An Ultra-Wide-Band solution will be employed to quickly and efficiently identify persons who may find themselves in unauthorized areas or perform tasks for which they are uninstructed. Besides dynamic people tracking, this solution can overcome problems of moving objects and tools in production workshops. We propose a new dataset to have information about workers' displacement and evaluate workers' performance and safety. The dataset is available at this link: IndoorIndustrialLocalisationDataset: <a href="https://github.com/vauchey/IndoorInsdustrialLocalisationDataset/">https://github.com/vauchey/IndoorInsdustrialLocalisationDataset/</a>   Paper : Mickael Delamare1, Fabrice Duval2, Remi Boutteau3. 1<a href="http://www.cesi.fr./">cesi</a>, CESI,Rouen campus,  76000 ROUEN, France,<a href="mailto:mdelamare@cesi.fr">mdelamare@cesi.fr</a> 2 <a href="https://lineact.cesi.fr/">LINEACT CESI</a>, LINEACT CESI, Rouen campus,76000 ROUEN, France, <a href="mailto:fduval@cesi.fr">fduval@cesi.fr</a> 3 <a href="http://www.esigelec.fr/">ESIGELEC</a> , IRSEEM, Rouen, France, Normandie Univ, UNIROUEN, <a href="mailto:remi.boutteau@univ-rouen.fr">remi.boutteau@univ-rouen.fr</a>, Link : <a href="https://www.mdpi.com/1424-8220/20/16/4511">https://www.mdpi.com/1424-8220/20/16/4511</a> DOI : <a href="https://doi.org/10.3390/s20164511">https://doi.org/10.3390/s20164511</a> Accepted to the journal Sensors (MDPI)

restrictedIPD-UNDECIDEDFeb 2026View details →

Assessment of Competence in Colonoscopy Using Motion Capture

ClinicalTrials.gov study NCT01997177. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Motion Capture as a Tool for the Assessment of Laparoscopic Performance

ClinicalTrials.gov study NCT04646304. IPD Sharing: NO. Countries: 1. Publications: 6.

Smartphone-Based Markerless Motion Capture for Orthopaedic Clinical Use

ClinicalTrials.gov study NCT05370534. IPD Sharing: NO. Countries: 1. Publications: 15.

Motion Capture: Baseball Swing

ClinicalTrials.gov study NCT04503070. IPD Sharing: NO. Countries: 1. Publications: 22.

Accuracy of Markerless Motion Capture Evaluation in Parkinson's Disease After DBS

ClinicalTrials.gov study NCT03607721. IPD Sharing: NO. Countries: 1. Publications: 9.