Accurate Estimation of Upper Limb Orthosis Wear Time Using Miniature Temperature Loggers

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OBJECTIVE: To propose and validate a new method for estimating upper limb orthosis wear time using miniature temperature loggers attached to locations on the upper body. DESIGN: Observational study.

SUBJECTS: Fifteen healthy participants.

METHODS: Four temperature loggers were attached to the arm and chest with straps. Participants were asked to remove and re-attach the straps at specified time-points. The labelled temperature data obtained were used to train a decision tree classification algorithm to estimate wear time. The final performance (mean error and 95% confidence interval) of the trained classifier and the wear time estimation were assessed with a hold-out data-set.

RESULTS: The trained algorithm can correctly classify unseen temperature data with a mean classification error between 1.1% and 3.1% for the arm, and between 1.8% and 4.0% for the chest, depending on the sampling time of the temperature logger. This resulted in mean wear time errors between 0.5% and 8.3% for the arm, and 0.13% and 13.0% for the chest.

CONCLUSION: The proposed method based on a classifier can accurately estimate upper limb orthosis wear time. This method could enable healthcare professionals to gain insight into the wear time of any upper limb orthosis.

Original languageEnglish
Article numberjrm00277
Number of pages10
JournalJournal of rehabilitation medicine
Early online date11 Feb 2022
Publication statusPublished - 1 Apr 2022

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