Statistical Prediction of Load Carriage Mode and Magnitude from Inertial Sensor Derived Gait Kinematics

Details

• Personal Author:
  D'Souza C ; Lim S
• Description:
  Load carriage induces systematic alterations in gait patterns and pelvic-thoracic coordination. Leveraging this information, the objective of this study was to develop and assess a statistical prediction algorithm that uses body-worn inertial sensor data for classifying load carrying modes and load levels. Nine men participated in an experiment carrying a hand load in four modes: one-handed right and left carry, and two-handed side and anterior carry, each at 50% and 75% of the participant's maximum acceptable weight of carry, and a no-load reference condition. Twelve gait parameters calculated from inertial sensor data for each gait cycle, including gait phase durations, torso and pelvis postural sway, and thoracic-pelvic coordination were used as predictors in a two-stage hierarchical random forest classification model with Bayesian inference. The model correctly classified 96.9% of the carrying modes and 93.1% of the load levels. Coronal thoracic-pelvic coordination and pelvis postural sway were the most relevant predictors although their relative importance differed between carrying mode and load level prediction models. This study presents an algorithmic framework for combining inertial sensing with statistical prediction with potential use for quantifying physical exposures from load carriage. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Occupational Health Safety
• Keywords:
  Author Keywords: Load Carriage Biomechanics Gait Kinematics Inertial Sensors Kinetics Load Classification Load Monitoring Physiological Function Physiological Testing Statistical Analysis
• ISSN:
  0003-6870
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Michigan ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-11
• Volume:
  76
• NIOSHTIC Number:
  nn:20055172
• Citation:
  Appl Ergon 2019 Apr; 76:1-11
• Contact Point Address:
  Sol Lim, Center for Ergonomics, Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109-2117, USA
• Email:
  solielim@umich.edu
• Federal Fiscal Year:
  2019
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Applied Ergonomics
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:e58539a72689b446f7d364adee713ec63706177e43a55a1876b9c7bdfd1beeafdf11cd266fb33cb3abc679a7c7dd3c7cea62c2956b907d594a07c4cb3a06329f
• Download URL:
  https://stacks.cdc.gov/view/cdc/217532/cdc_217532_DS1.pdf
• File Type:
  [PDF - 1.90 MB ]