Article

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.
CI - Copyright (c) 2016 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS