Movement

## Overview

Movement is a dataset for object detection tasks - it contains Car Truck Bus MotorCycle annotations for 1,374 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

The LTMM database contains 3-day 3D accelerometer recordings of 71 elder community residents, used to study gait, stability, and fall risk.

This project is to determine horizontal and vertical movement patterns of two jellyfish species in Hood Canal, in relation to environmental variables. It is being conducted by NMFS scientists in collaboration with a NOAA Hollings Scholar; we also are making use of publicly available oceanographic data from the University of Washington. We used acoustic tags and receivers to track jellyfish movement patterns and correlated their movements with oceanographic data. This project will produce peer reviewed manuscripts. The target audience is fisheries and marine resource managers in Puget Sound and along the West Coast. This is a one-time, standalone project without a firm deadline. This data set contains acoustic telemetry data for lions mane and fried egg jellyfish.

The dataset contains tri-axial accelerometer and tri-axial gyroscope readings from the four IMUs and labels. There are three sub-datasets, which have different ground-truth labelling configurations, included in this dataset. Please note that the labelling is subjective to the mother's perception. The dataset, as a whole, contains recordings spanning 14 weeks from 26th week to 39th week and in total, about 71 hours of recordings.

The three sub-datasets included are:

Sub-dataset One: In this sub-dataset, only the occurrence of the particular type of fetal movement known as the fetal kick is considered for ground-truth labelling.

Sub-dataset Two: All types of fetal movement felt by the mother -- including trunk movement, isolated limb movement, and general body movement -- were considered for ground truth-labelling as fetal movements in this sub-dataset.

Sub-dataset Three: In this sub-dataset, the emphasis was given to the classification of different types of fetal movements. Three types of fetal movements are labelled: trunk movement, isolated limb movement, and general body movement.

Additional data are provided in three additional 'csv' files, which contains the record number, the Period of Amenorrhoea (POA), start time, and end time of each recording. Also, additional details about the mother and the baby are provided in the README file.

For more details, refer to the README.pdf.

Container Movement

## Overview

Container Movement is a dataset for object detection tasks - it contains Container Number annotations for 480 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The electrooculography signal is widely used to analyze eye movements

The field of movement ecology has rapidly grown during the last decade, with important advancements in tracking devices and analytical tools that have provided unprecedented insights into where, when, and why species move across a landscape. Although there has been an increasing emphasis on making animal movement data publicly available, there has also been a conspicuous dearth in the availability of such data on large carnivores. Globally, large predators are of conservation concern. However, due to their secretive behavior and low densities, obtaining movement data on apex predators is expensive and logistically challenging. Consequently, the relatively small sample sizes typical of large carnivore movement studies may limit insights into the ecology and behavior of these elusive predators. The aim of this initiative is to make available to the conservation-scientific community a dataset of 134,690 locations of jaguars (Panthera onca) collected from 117 individuals (54 males and 63 females) tracked by GPS technology. Individual jaguars were monitored in five different range countries representing a large portion of the species’ distribution. This dataset may be used to answer a variety of ecological questions including but not limited to: improved models of connectivity from local to continental scales; the use of natural or human-modified landscapes by jaguars; movement behavior of jaguars in regions not represented in this dataset; intraspecific interactions; and predator-prey interactions. In making our dataset publicly available, we hope to motivate other research groups to do the same in the near future. Specifically, we aim to help inform a better understanding of jaguar movement ecology with applications towards effective decision making and maximizing long-term conservation efforts for this ecologically important species.

Movement Data Synthesis Dataset

  Dataset Summary

This dataset contains 106 examples of movement tracking data specifically designed for training Large Language Models to generate synthetic physiotherapy and rehabilitation movement data. The dataset focuses on left arm circular exercises performed in a clockwise direction, captured using MediaPipe pose estimation technology.

  Intended Use





  Primary Use Cases

Fine-tuning LLMs for synthetic movement data… See the full description on the dataset page: https://huggingface.co/datasets/lucasbrandao/movement-synthesis-dataset.

Movement Classification

## Overview

Movement Classification is a dataset for object detection tasks - it contains Movement annotations for 9,664 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The CeTI-Age-Kinematics dataset provides full-body movements from 30 common daily tasks in nine categories, e.g., targeted reaching, lifting and walking tasks, and of multiple object interactions. Kinematic data were recorded at Dresden University of Technology (Germany) with informed consent of the participants under well-controlled conditions, covering multiple motion repetitions and task variations. The tasks were performed by 32 participants covering a wider age range, including older adults (66-75 years) and younger adults (19-28 years). Data were recorded using sensor suits and gloves with inertial measurement units (Rokoko Electronics, Denmark), with utilizing 33 sensors in total for full-body, wrist, and finger movements. The dataset also entails anthropometric body measurements and further demographic data. Additionally, the dataset provides spatial measurements of the experimental setups to enhance the interpretation of the kinematic data in relation to body characteristics and situational surroundings.The data records are structured according to the Motion-BIDS standard, with (i) meta-files describing the participants' characteristics such as demographic, anthropomorphic, and other data (see participant[.json|.tsv] files); (ii) metadata and additional information on the dataset (see dataset_description.json, README.md); (iii) TSV and BVH data that contain the kinematic motion data for each movement task, along with metadata on the task descriptions and instructions, (iv) source code to process and visualize the data, (v) materials that document the acquisition process such as recording protocols and exemplary videos of the motion executions.Detailed information, visualizations of the dataset content, and explanations of algorithmic approaches are available in the linked supplements, such as in the data descriptor and supplementary information accessible here: Pogrzeba, L., Muschter, E., Hanisch, S. et al. (2025). A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults. Scientific Data, 12, 531. https://doi.org/10.1038/s41597-025-04818-y

This dataset contains key characteristics about the data described in the Data Descriptor An Asian-centric human movement database capturing activities of daily living. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Comprehensive financial and analytical metrics for Movement, including key performance indicators, market data, and ecosystem analytics.

CAPSTONE 1 MOVEMENT

## Overview

CAPSTONE 1 MOVEMENT is a dataset for object detection tasks - it contains MOVEMENT annotations for 500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Dataset

This dataset was created by oummuo

Contents

It contains the following files:

To ensure the best possible achievement, the temperature is measured periodically using a mercury thermometer and it is range (20c - 22c). The ventilation is also good, which prevented the overheating of the camera. The camera is placed away from direct sunlight and to ensure good lighting, the brightness measured frequently using the Lux Light Meter application on Samsung galaxy note nine and it in range (76 Lux - 87 Lux). The Kinect camera is placed at a height of 0.75m. The recording was started thirty minutes after the camera had turned on. Children were asked to walk along a line, at normal speed, towards the Kinect camera. The cameras recorded color video and skeleton tracking videos ten times then choosing one suitable gait cycle. Each time the participant walks about two gait cycles in the range of [1.5m to 4m] in front of the camera. Then extracting one gait cycle to use in the following stages.

'eog_upload.mat' contains a variable valled 'eog_art', which are ocular artifacts measured using EOG. 'eyemov_all_upload.mat' contains all eye movement (saccades and microsaccades) parameters computed from the Eyelink files.'eyemov_hor_upload.mat' contains all horizontal eye movement (saccades and microsaccades) parameters computed from the Eyelink files.'eyemov_ver_upload.mat' contains all vertical eye movement (saccades and microsaccades) parameters computed from the Eyelink files. Note that vertical eye movements were only recorded for subjects 19 to 28. The matrix contains data also for subjects 1 to 18, but it is not meaningful. See Paper for details.All above variables have the same dimensions: N_subj x N_pharma x N_behav, where N_subj are the number of subjects (28), N_pharma are the number of pharma conditions (1 = placebo, 2 = atomoxetine, 3 = donepezil) and N_behav are the two behavioral contexts (1 = Fixation, 2 = Task-counting).

A database was created in .XLSX and .CSV formats containing the processing of an EMG signal and the position and angle error during the execution of three dynamic tasks based on the three-dimensional movement of the upper limb. This data was recorded from the quantification of the hand position error.

Access the world's largest and most diverse human motion dataset. A professional-grade collection of motion capture data suitable for advanced applications, including machine learning, animation, research, and AI development.

This is an initial study to characterise rollator movement. An inertial measurement unit (IMU) was used to measure the motion of the rollator and analytical approaches were developed to extract features characterising the rollator movement, properties of the surface, and push events. The analytics were tested in two situations, firstly a healthy participant used a rollator in a laboratory using a motion capture system to obtain ground truth. Secondly the IMU was used to measure the movement of a rollator being used by a user with multiple sclerosis (MS) on a flat surface, cross-slope, up and down slopes, and up and down a step The dataset of motion sensing movement is comprised of seven straight-lighting walking trials (between 4-5 m) performed by a healthy participant using a rollator in a gait lab. The raw data were in csv format recorded by VICON system and subsequently analysed in Microsoft Excel 2013 to calculate the distance travelled by a rollator.

Detailed Active users (monthly) metrics and analytics for Movement, including historical data and trends.