ArticleIEEE transactions on instrumentation and measurement2025
A Wearable Gait Monitoring System for 17 Gait Parameters Based on Computer Vision.
Article in IEEE transactions on instrumentation and measurement, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Wearable Monitor to Detect Tripping During Daily Life in Children with Intoeing Gait.Sensors (Basel, Switzerland) · 2025Article
- Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
We demonstrate a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including step length, step time, stride velocity, among others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additionally, a force sensitive resistor (FSR) affixed to the heel of the shoe, combined with a custom-designed algorithm, is utilized to measure temporal gait parameters. Through testing on multiple participants and comparison with a gait mat, our gait monitoring system exhibited excellent performance, with all 17 gait parameters exceeding 93.61% in measurement accuracy. The system also demonstrated a low drift of 4.89% during long walking sessions. A gait identification task conducted on participants using a trained transformer model achieved 95.7% accuracy on the dataset collected by our system, indicating the potential of our system to collect long-sequence, varied gait data suitable for training current large language models (LLMs). The system is cost-effective, user-friendly, and well-suited for real-life measurements.
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