ArticleScientific data2023
A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Structuring Variability in Human Gait Datasets: A Covariate-Centered Taxonomy and Systematic Review of Image- and Depth-Based Collections.Journal of imaging · 2026Review
- Differentiating post-stroke patients from healthy individuals via vision-based skeleton-optical fusion.Journal of neuroengineering and rehabilitation · 2025Article
- Non-contact, non-visual, multi-person hallway gait monitoring.Scientific reports · 2025Article
- A Multi-sensor Gait Dataset Collected Under Non-standardized Dual-Task Conditions.Scientific data · 2025Article
- Learning based lower limb joint kinematic estimation using open source IMU data.Scientific reports · 2025Article
- A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.Sensors (Basel, Switzerland) · 2025Review
- Using artificial intelligence and radiomics to analyze imaging features of neurodegenerative diseases.Frontiers in neurology · 2025Article
- A Motion Capture Dataset on Human Sitting to Walking Transitions.Scientific data · 2024Article
- A comprehensive review of gait analysis using deep learning approaches in criminal investigation.PeerJ. Computer science · 2024Article
- A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors.Scientific data · 2023Article
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Authors and funding
5 authors.
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Abstract
Gait datasets are often limited by a lack of diversity in terms of the participants, appearance, viewing angle, environments, annotations, and availability. We present a primary gait dataset comprising 1,560 annotated casual walks from 64 participants, in both indoor and outdoor real-world environments. We used two digital cameras and a wearable digital goniometer to capture visual as well as motion signal gait-data respectively. Traditional methods of gait identification are often affected by the viewing angle and appearance of the participant therefore, this dataset mainly considers the diversity in various aspects (e.g., participants' attributes, background variations, and view angles). The dataset is captured from 8 viewing angles in 45° increments along-with alternative appearances for each participant, for example, via a change of clothing. The dataset provides 3,120 videos, containing approximately 748,800 image frames with detailed annotations including approximately 56,160,000 bodily keypoint annotations, identifying 75 keypoints per video frame, and approximately 1,026,480 motion data points captured from a digital goniometer for three limb segments (thigh, upper arm, and head).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.