Abstract
Objective
To improve measurement of upper extremity (UE) use in the community by evaluating
the feasibility of using body-worn sensor data and machine learning models to distinguish
productive prehensile and bimanual UE activity use from extraneous movements associated
with walking.
Design
Comparison of machine learning classification models with criterion standard of manually
scored videos of performance in UE prosthesis users.
Setting
Rehabilitation hospital training apartment.
Participants
Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age
and hand dominance (N=18).
Interventions
Participants were filmed executing a series of functional activities; a trained observer
annotated each frame to indicate either UE movement directed at functional activity
or walking. Synchronized data from an inertial sensor attached to the dominant wrist
were similarly classified as indicating either a functional use or walking. These
data were used to train 3 classification models to predict the functional versus walking
state given the associated sensor information. Models were trained over 4 trials:
on UE amputees and controls and both within subject and across subject. Model performance
was also examined with and without preprocessing (centering) in the across-subject
trials.
Main Outcome Measure
Percent correct classification.
Results
With the exception of the amputee/across-subject trial, at least 1 model classified
>95% of test data correctly for all trial types. The top performer in the amputee/across-subject
trial classified 85% of test examples correctly.
Conclusions
We have demonstrated that computationally lightweight classification models can use
inertial data collected from wrist-worn sensors to reliably distinguish prosthetic
UE movements during functional use from walking-associated movement. This approach
has promise in objectively measuring real-world UE use of prosthetic limbs and may
be helpful in clinical trials and in measuring response to treatment of other UE pathologies.
Keywords
List of abbreviations:
ADL (activities of daily living), IMU (inertial measurement unit), UE (upper extremity)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: October 02, 2015
Footnotes
Supported by the MITRE Corporation of America, Veterans Affairs Rehabilitation Research and Development Merit Awards (award nos. A-6408 and A-6767).
Disclosures: none.
Identification
Copyright
© 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.
