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Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities.

Yeji Woo1, Sungjin Hwang1, Seungwoo Oh1

  • 1Department of Computer Science, Hanyang University, Seoul, South Korea.

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|January 3, 2026
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Summary
This summary is machine-generated.

This study introduces a new human activity recognition (HAR) dataset for individuals with mobility disabilities, achieving high accuracy in recognizing diverse mobility aid usage.

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Rehabilitation Technology

Background:

  • Human activity recognition (HAR) has advanced significantly for able-bodied individuals, yet lacks datasets for those with mobility disabilities.
  • Existing HAR technologies do not adequately address the unique activities and needs of pedestrians with mobility impairments.
  • The absence of specialized datasets hinders the development of assistive technologies for this population.

Purpose of the Study:

  • To compile a comprehensive human activity recognition (HAR) dataset for individuals with and without mobility disabilities.
  • To include diverse mobility aids such as crutches, walkers, manual wheelchairs, and electric wheelchairs in the dataset.
  • To facilitate research and development of HAR technologies tailored for people with mobility impairments.

Main Methods:

  • Collected sensor data from smartphones and smartwatches from 120 participants, including those with and without mobility disabilities.
  • Developed and analyzed recognition tasks for various pedestrian activities, including stationary, walking, and mobility aid usage.
  • Evaluated the impact of different sensor combinations, classification models, and evaluation methodologies, including user-independent assessments.

Main Results:

  • Achieved high classification accuracies: 99.64% for random evaluation and 98.79% for user-independent evaluation.
  • Demonstrated the effectiveness of combining smartphone and smartwatch sensor data for improved HAR.
  • Identified optimal sensor combinations and classification models for recognizing activities of individuals with mobility disabilities.

Conclusions:

  • The developed HAR dataset is a valuable resource for advancing research on mobility-impaired individuals.
  • This work can stimulate new applications and technologies to support people with mobility disabilities.
  • The findings highlight the potential of HAR to improve accessibility and independence for diverse user groups.