Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

Gina Sprint; Diane J Cook; Douglas L Weeks; Vladimir Borisov

doi:10.1109/ACCESS.2015.2468213

Back

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

Journal article

Open access

Peer reviewed

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

Gina Sprint, Diane J Cook, Douglas L Weeks and Vladimir Borisov

IEEE access, Vol.3, pp.1350-1366

2015

DOI: https://doi.org/10.1109/ACCESS.2015.2468213

Handle:

https://hdl.handle.net/2376/114846

PMCID: PMC4819996

PMID: 27054054

Files and links (1)

url

https://doi.org/10.1109/ACCESS.2015.2468213View

Published (Version of record) Open

Abstract

prediction

wearable sensors

machine learning

signal processing

rehabilitation monitoring

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a 7-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported.

Metrics

5 Record Views

Details

Title: Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors
Creators: Gina Sprint - Department of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163 USA
Diane J Cook - Department of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163 USA
Douglas L Weeks - St. Luke’s Rehabilitation Institute, Spokane, WA, 99202 USA
Vladimir Borisov - Voiland School of Chemical and Bioengineering, Washington State University, Pullman, WA, 99163 USA
Publication Details: IEEE access, Vol.3, pp.1350-1366
Academic Unit: Electrical Engineering and Computer Science, School of
Identifiers: 99900548068401842
Language: English
Resource Type: Journal article