A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks.

ABSTRACT: To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it

SUBMITTER: Syed S 

PROVIDER: S-EPMC8065130 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature