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<p>Human activity recognition (HAR) provided several benefits to people without disabilities. Researchers have provided public HAR datasets and developed technologies based on these datasets. However, the activities of pedestrians with mobility disabilities have not been actively investigated because related datasets do not exist. In this study, we compile an HAR dataset comprising basic activities for people with and without mobility disabilities (including activity categories of still, walking, crutches, walkers, manual wheelchairs, and electric wheelchairs). Our dataset contains sensor data from smart devices (smartphones and smartwatches) collected from 120 participants. We also provide baseline analyses of our dataset: (1) recognition tasks according to the pedestrian activities, (2) impact of sensor combinations, (3) classification models, (4) evaluation methods, and (5) combining smartphone and smartwatch sensors. The results indicate that the classification accuracies are 99.64% for the random evaluation and 98.79% for the user-independent evaluation using the best combinations. We hope that this study will expand HAR research for people with mobility disabilities and renew enthusiasm for subsequent applications related to this topic.</p>

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Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月3日

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  • Yeji Woo,
  • Sungjin Hwang,
  • Seungwoo Oh,
  • Myungwon Kang,
  • Sungyoon Lee,
  • Jieun Kim,
  • Jaehyuk Cha,
  • Kwanguk Kenny Kim

摘要

Human activity recognition (HAR) provided several benefits to people without disabilities. Researchers have provided public HAR datasets and developed technologies based on these datasets. However, the activities of pedestrians with mobility disabilities have not been actively investigated because related datasets do not exist. In this study, we compile an HAR dataset comprising basic activities for people with and without mobility disabilities (including activity categories of still, walking, crutches, walkers, manual wheelchairs, and electric wheelchairs). Our dataset contains sensor data from smart devices (smartphones and smartwatches) collected from 120 participants. We also provide baseline analyses of our dataset: (1) recognition tasks according to the pedestrian activities, (2) impact of sensor combinations, (3) classification models, (4) evaluation methods, and (5) combining smartphone and smartwatch sensors. The results indicate that the classification accuracies are 99.64% for the random evaluation and 98.79% for the user-independent evaluation using the best combinations. We hope that this study will expand HAR research for people with mobility disabilities and renew enthusiasm for subsequent applications related to this topic.