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Sprint G, Cook DJ, Weeks DL, et al. Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors. IEEE Access. 2015;3:1350-1366doi: 10.1109/ACCESS.2015.2468213.
Sprint, G., Cook, D. J., Weeks, D. L., & Borisov, V. (2015). Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors. IEEE access : practical innovations, open solutions, 31350-1366. https://doi.org/10.1109/ACCESS.2015.2468213
Sprint, Gina, et al. "Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors." IEEE access : practical innovations, open solutions vol. 3 (2015): 1350-1366. doi: https://doi.org/10.1109/ACCESS.2015.2468213
Sprint G, Cook DJ, Weeks DL, Borisov V. Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors. IEEE Access. 2015;3:1350-1366. doi: 10.1109/ACCESS.2015.2468213. Epub 2015 Aug 26. PMID: 27054054; PMCID: PMC4819996.
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IEEE Access. 2015;3:1350-1366. doi: 10.1109/ACCESS.2015.2468213. Epub 2015 Aug 26.

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors.

IEEE access : practical innovations, open solutions

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

Affiliations

  1. Department of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163 USA.
  2. St. Luke's Rehabilitation Institute, Spokane, WA, 99202 USA.
  3. Voiland School of Chemical and Bioengineering, Washington State University, Pullman, WA, 99163 USA.

PMID: 27054054 PMCID: PMC4819996 DOI: 10.1109/ACCESS.2015.2468213

Abstract

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.

Keywords: machine learning; prediction; rehabilitation monitoring; signal processing; wearable sensors

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