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

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

Blog Article

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 esab miniarc rogue 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 was icon airflite goggles placed back on again.We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.

9962, and an F1 score of 0.9981, outperforming all evaluated algorithms.Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.