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A Novel Dataset for Gait Activity Recognition in Real-World Environments.

John C Mitchell1,2, Abbas A Dehghani-Sanij1, Shengquan Xie3

  • 1School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.

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Summary

This study introduces the Context-Aware Human Activity Recognition (CAHAR) dataset, the first to label both activities and terrains for improved remote gait analysis. This resource enables advanced wearable sensor models for fall risk assessment.

Keywords:
force sensorshuman activity recognitioninertial sensorsreal environmentssensor systemsterrainwearable sensorswireless sensor networks

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Area of Science:

  • Biomechanics
  • Sensor Technology
  • Machine Learning

Background:

  • Falls are a major global health concern, with gait analysis crucial for fall risk identification.
  • Wearable sensors and deep learning offer potential for remote gait analysis, enhancing data quality and automation.
  • Accurate Human Activity Recognition (HAR) and terrain classification are essential for real-world gait analysis.

Purpose of the Study:

  • To address the lack of suitable datasets for terrain classification in gait analysis.
  • To present the Context-Aware Human Activity Recognition (CAHAR) dataset, the first activity- and terrain-labeled dataset.
  • To facilitate the development of advanced classification models for remote gait analysis.

Main Methods:

  • Collected data from 20 healthy participants using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDAR.
  • Captured data across a diverse range of indoor and outdoor terrains.
  • Developed the CAHAR dataset with activity and terrain labels.

Main Results:

  • The CAHAR dataset is the first of its kind, integrating both human activity and environmental terrain information.
  • The dataset supports the development of models capable of simultaneous HAR and terrain identification.
  • Enables progress towards more sophisticated remote gait analysis using wearable sensors.

Conclusions:

  • The CAHAR dataset is a significant resource for advancing research in wearable sensor technology for gait analysis.
  • It provides a foundation for creating more accurate fall risk prediction models.
  • Facilitates the development of intelligent systems for real-world gait monitoring and fall prevention.