A Database of Human Gait Performance on Irregular and Uneven Surfaces Collected by Wearable Sensors

Details

• Personal Author:
  Coppola SM ; Dennerlein, Jack T. ; Dixon PC ; Hu B ; Li S ; Luo Y
• Description:
  Gait analysis has traditionally relied on laborious and lab-based methods. Data from wearable sensors, such as Inertial Measurement Units (IMU), can be analyzed with machine learning to perform gait analysis in real-world environments. This database provides data from thirty participants (fifteen males and fifteen females, 23.5 +/- 4.2 years, 169.3 +/- 21.5 cm, 70.9 +/- 13.9 kg) who wore six IMUs while walking on nine outdoor surfaces with self-selected speed (16.4 +/- 4.2 seconds per trial). This is the first publicly available database focused on capturing gait patterns of typical real-world environments, such as grade (up-, down-, and cross-slopes), regularity (paved, uneven stone, grass), and stair negotiation (up and down). As such, the database contains data with only subtle differences between conditions, allowing for the development of robust analysis techniques capable of detecting small, but significant changes in gait mechanics. With analysis code provided, we anticipate that this database will provide a foundation for research that explores machine learning applications for mobile sensing and real-time recognition of subtle gait adaptations. [Description provided by NIOSH]
• Subjects:
  Environment Environmental Exposure Men Occupational Health Safety Women
• Keywords:
  Environmental Exposure Environmental Factors Humans Sensors Surface Properties Walking Surfaces Wearable Technologies
• ISSN:
  2052-4463
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Florida ; Maryland ; Massachusetts ; OSHA Region 1 ; OSHA Region 3 ; OSHA Region 4
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  7
• NIOSHTIC Number:
  nn:20060234
• Citation:
  Sci Data 2020 Jul; 7:219
• Contact Point Address:
  Boyi Hu, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL
• Email:
  boyhu@ise.ufl.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  Harvard School of Public Health
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Scientific Data
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:823dc8ad2596ef11ea95df0d9707d09b775f519ad8b4e173410d6f85857293c552aa644d5bd38bc028e3947c713dae6c29db63f05cdf2067ba4d157354dd9402
• Download URL:
  https://stacks.cdc.gov/view/cdc/224199/cdc_224199_DS1.pdf
• File Type:
  [PDF - 1.62 MB ]