ArticleSensors (Basel, Switzerland)2022
The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Health & Gait: a dataset for gait-based analysis.Scientific data · 2025Article
- Gait Variability at Different Walking Speeds.Journal of functional morphology and kinesiology · 2023Article
- Spatiotemporal Gait Parameters in Adults With Premanifest and Manifest Huntington's Disease: A Systematic Review.Journal of movement disorders · 2023Article
- A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton-Raphson-Based Feature Selection Approach for Human Gait Recognition.Sensors (Basel, Switzerland) · 2023Article
- Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.Sensors (Basel, Switzerland) · 2022Article
- Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective.Sensors (Basel, Switzerland) · 2022Article
- Three dimensional analysis of hip joint reaction force using Q Hip Force (AQHF) software: Implication as a diagnostic tool.PloS one · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms.
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Registered trials
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