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Speed estimation from a tri-axial accelerometer using neural networks.

Yoonseon Song1, Seungchul Shin, Seunghwan Kim

  • 1Electronics and Telecommunications Research Institute, Daejeon, 305-700, Korea. yssong@etri.re.kr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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This study presents a novel method for estimating walking and running speed using chest-mounted accelerometer data. The approach accurately calculates speed by analyzing stride time and length via neural networks.

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Accurate speed estimation is crucial for performance analysis and health monitoring.
  • Existing methods often require specialized equipment or controlled environments.
  • Wearable sensors offer a promising avenue for unobtrusive, real-world speed tracking.

Purpose of the Study:

  • To develop and validate a novel method for estimating human locomotion speed using tri-axial accelerometer data.
  • To differentiate between walking and running gaits using machine learning.
  • To calculate stride length and time for accurate speed determination.

Main Methods:

  • Human body accelerations were measured on the chest using a tri-axial accelerometer.
  • Acceleration signals were segmented into strides to measure stride time.

Related Experiment Videos

  • Two neural networks were employed: one to classify gait (walk/run) and another to estimate stride length.
  • Speed was calculated by dividing estimated stride length by measured stride time.

    • Main Results:

    • The developed method demonstrated good agreement between actual and estimated speeds.
    • A high linear correlation coefficient (r=0.9874) was achieved, indicating strong accuracy.
    • The method was successfully validated on untrained subjects and applied to real-world field and track data.

    Conclusions:

    • Chest-mounted accelerometers combined with neural networks provide an accurate and feasible method for speed estimation.
    • This approach offers a non-invasive and potentially cost-effective solution for gait analysis.
    • The findings support the use of wearable sensor technology for real-time locomotion monitoring.