| dc.description.abstract | Background.
Functional capacity evaluations (FCEs) are used to determine a worker’s capacity for
return to work (RTW) or for job matching purposes. FCEs are completed using a subjective
approach, where a trained evaluator will determine the capacity of a worker by monitoring
for changes in movement patterns as the worker completes manual material handling tasks. It
is well established that movement patterns change as workers become fatigued, explaining
why evaluators are trained to watch for such changes; however, the current subjective
approach used in FCEs assumes that everyone changes in the same way. In part due to the
subjectivity of capacity determinations, the predictive validity and reliability of FCEs to
produce accurate RTW outcomes has been questioned (Reneman, 2003). Therefore, an
objective and personalized approach to detecting the onset of fatigue is needed. Machine
learning may provide such an approach, specifically using an outlier detector algorithm.
Objective.
To determine if one-class support vector machines (OCSVM), an outlier detection
machine learning algorithm, can be utilized to objectively identify fatigue during repetitive
lifting on a subject-specific basis.
Methods.
Fourteen participants completed a repetitive lifting protocol for 60 minutes or until
volitional fatigue. Whole-body kinematics were recorded using a 3D motion capture system
(Vicon, Oxford, UK). Ratings of perceived exertion (RPE) and heart rate (HR) were recorded
after every 15 lifts. A whole-body kinematic model of each participant was created in
Visual3D, where trajectory data from15 landmark locations were exported for each lift. For
each participant, lifts were separated into a training set and multiple test sets. The training set
consisted of approximately the first 35% of lifts, and the test sets were subsequent sets of 15
lifts. Principal component analysis (PCA) was used as a data reduction and feature extraction
method and applied to the training set. The PC scores from the training set data were used as
features in a OCSVM. Test set data were projected back onto the training set principal
component (PC) feature space. Test set PC scores were then classified against the decision
boundary defined by the OCSVM. The percentage of PC scores from each test set that were
beyond the boundary (“outliers”) was calculated. Spearman’s rank correlation (ρ), a nonparametric test,
was used to assess the association between RPE, HR and the percentage of outliers in each test set.
Results.
Significant positive associations between RPE and the percentage of outliers were
detected in seven of the ten participants who were likely fatigued based on their RPE. Only
two of eight participants who were likely fatigued based on their HR had significant positive
associations. All participants who were not likely to be fatigued had no significant
association between either RPE or HR and the percentage of outliers. The OCSVM did
however reveal changes in movement patterns from baseline for some participants who did
not fatigue.
Conclusion.
The application of OCSVMs identified significant changes in movement patterns
from baseline in those who experienced fatigue from a repetitive lifting protocol. Although
no significant associations were identified in those who were not fatigued, the OCSVM still
identified movement pattern changes. These results show support for use of an outlier
detection tool to aid in FCE assessments to potentially reduce subjectivity, supporting
improved RTW decision making. | en |
