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.