Details:

Alternative Title:
Appl Ergon
Personal Author:
Lim, Sol ; D’Souza, Clive
Lim, Sol ; D’Souza, Clive Less -
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.
Subjects:
[+]
Adult Algorithms Article Bayes Theorem Biomechanical Phenomena Gait Gait Kinematics Humans Inertial Sensors Lifting Load Carriage Load Classification Male Models, Statistical Pelvis Posture Proof Of Concept Study Thorax Wearable Electronic Devices Weight-Bearing Young Adult
Source:
Appl Ergon. 76:1-11
Pubmed ID:
30642513
Pubmed Central ID:
PMC7079201
Document Type:
Journal Article
Funding:
90IF0094/ACL/ACL HHS/United States ; T42 OH008455/OH/NIOSH CDC HHS/United States
90IF0094/ACL/ACL HHS/United States ; T42 OH008455/OH/NIOSH CDC HHS/United States Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:252bd7023490a9b9383729f29d539039593994d8c27633d7deafcad71e9a6ab6
Download URL:
https://stacks.cdc.gov/view/cdc/86014/cdc_86014_DS1.pdf
File Type:

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