ArticleScientific data2020
A database of human gait performance on irregular and uneven surfaces collected by wearable sensors.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Increased Gait Variability while Texting and Walking with Added Cognitive Loads in Challenging Environments.Journal of musculoskeletal & neuronal interactions · 2026Article
- Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone.Sensors (Basel, Switzerland) · 2026Article
- A Novel Dataset for Gait Activity Recognition in Real-World Environments.Sensors (Basel, Switzerland) · 2026Article
- Outdoor Walking Classification Based on Inertial Measurement Unit and Foot Pressure Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors.Scientific data · 2025Article
- The COMPWALK-ACL: A Dataset of Multi-pace IMU Gait Kinematics in Adolescents, Adults, and ACL Injured Patients.Scientific data · 2025Article
- A Dataset of Clinical Gait Signals with Wearable Sensors from Healthy, Neurological, and Orthopedic Cohorts.Scientific data · 2025Article
- Non-Linear Gait Dynamics Are Affected by Commonly Occurring Outdoor Surfaces and Sex in Healthy Adults.Sensors (Basel, Switzerland) · 2025Article
- Comprehensive human locomotion and electromyography dataset: Gait120.Scientific data · 2025Article
- A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults.Scientific data · 2024Article
- The NACOB multi-surface walking dataset.Scientific data · 2024Article
- Recent Advances in Self-Powered Wearable Flexible Sensors for Human Gaits Analysis.Nanomaterials (Basel, Switzerland) · 2024Review
- A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables.Scientific data · 2024Article
- Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones.JMIR mHealth and uHealth · 2024Article
- A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.Scientific data · 2023Article
- NONAN GaitPrint: An IMU gait database of healthy young adults.Scientific data · 2023Article
- Metaverse Wearables for Immersive Digital Healthcare: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Towards Environment-Aware Fall Risk Assessment: Classifying Walking Surface Conditions Using IMU-Based Gait Data and Deep Learning.Brain sciences · 2023Article
- Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces.Scientific reports · 2023Article
- A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults.Scientific data · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Gait analysis has traditionally relied on laborious and lab-based methods. Data from wearable sensors, such as Inertial Measurement Units (IMU), can be analyzed with machine learning to perform gait analysis in real-world environments. This database provides data from thirty participants (fifteen males and fifteen females, 23.5 ± 4.2 years, 169.3 ± 21.5 cm, 70.9 ± 13.9 kg) who wore six IMUs while walking on nine outdoor surfaces with self-selected speed (16.4 ± 4.2 seconds per trial). This is the first publicly available database focused on capturing gait patterns of typical real-world environments, such as grade (up-, down-, and cross-slopes), regularity (paved, uneven stone, grass), and stair negotiation (up and down). As such, the database contains data with only subtle differences between conditions, allowing for the development of robust analysis techniques capable of detecting small, but significant changes in gait mechanics. With analysis code provided, we anticipate that this database will provide a foundation for research that explores machine learning applications for mobile sensing and real-time recognition of subtle gait adaptations.
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Registered trials
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.