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  3. "speed": A Dataset For Human Speed Estimation.
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  3. "speed": A Dataset For Human Speed Estimation.

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"Speed": A Dataset for Human Speed Estimation.

Zainab R Bachir1, Usman Tariq1

  • 1Electrical Engineering Department, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

Sensors (Basel, Switzerland)
|October 29, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a new dataset and a deep learning model, SpeedNet, for accurate human speed estimation using wearable sensors. SpeedNet achieves real-time performance and significantly reduces errors with subject-specific adaptation.

Keywords:
accelerometerconvolutional neural networkgyroscopeinertial measurement unit

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Inertial Measurement Units (IMUs) are increasingly used for human motion analysis.
  • Accurate speed estimation is crucial for various applications, including sports science and rehabilitation.
  • Existing speed estimation techniques using IMUs have limitations in accuracy and generalizability.

Purpose of the Study:

  • To introduce a new, medium-scale dataset named the "Speed" dataset for IMU-based speed estimation.
  • To benchmark existing speed estimation methods on this dataset.
  • To develop and evaluate a deep learning model, SpeedNet, for improved subject-independent and subject-specific speed estimation.

Main Methods:

  • Collected walking/running speed data (4.0-9.5 km/h) from 33 healthy subjects using IMUs.
  • Created a dataset with accelerometer and gyroscope data, organized into subject-independent sets.
  • Implemented and benchmarked Support Vector Regression, Gaussian Process Regression, and shallow neural networks.
  • Designed and tested a deep Convolutional Neural Network (CNN) named SpeedNet.

    • Main Results:

    • SpeedNet achieved a subject-independent Root Mean Square Error (RMSE) of 0.4819 km/h.
    • Subject-specific adaptation of SpeedNet reduced the RMSE to 0.1747 km/h.
    • SpeedNet demonstrated superior accuracy and real-time performance compared to other benchmarked methods.

    Conclusions:

    • The "Speed" dataset provides a valuable resource for IMU-based speed estimation research.
    • SpeedNet offers a highly accurate and computationally efficient solution for estimating human walking/running speeds.
    • The proposed subject-specific adaptation significantly enhances the precision of speed estimation.