ArticleScientific data2022
A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- A Dataset of Clinical Gait Signals with Wearable Sensors from Healthy, Neurological, and Orthopedic Cohorts.Scientific data · 2025Article
- Comprehensive human locomotion and electromyography dataset: Gait120.Scientific data · 2025Article
- A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults.Scientific data · 2025Article
- Reduced field of view alters scanning behaviour.Virtual reality · 2025Article
- On utilizing gaze behavior to predict movement transitions during natural human walking on different terrains.PloS one · 2025Article
- Foot kinematics and kinetics data for different static foot posture collected using a multi-segment foot model.Scientific data · 2024Article
- A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults.Scientific data · 2024Article
- NONAN GaitPrint: An IMU gait database of healthy young adults.Scientific data · 2023Article
- Challenges and advances in the use of wearable sensors for lower extremity biomechanics.Journal of biomechanics · 2023Article
- A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment.Scientific data · 2022Article
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Authors and funding
2 authors.
Funding
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Abstract
Human gait data have traditionally been recorded in controlled laboratory environments focusing on single aspects in isolation. In contrast, the database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 ± 7.64 cm, 72.9 ± 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction. We anticipate the data set to provide a foundation for research that considers natural everyday walk scenarios with transitional motions and the interaction between gait and gaze during walking.
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Registered trials
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