Fig. 1

Examples of movement behavior images used as input for convolutional autoencoders. Panel (A) displays the movement behavior profile images created from accelerometer activity counts per minute during valid measurement periods over the course of 7 measurement. Panel (B) displays the reconstructed movement behavior profile images from the learned latent variable using convolutional autoencoders. Participants with four or more valid days were considered eligible for inclusion in our study with each valid day was defined as ≥ 10 h of monitor wear time. Accelerometer outputs (counts per minute [cpm]) were classified using previously validated cut-points as either sedentary (< 100 cpm), light-intensity physical activity (100–1951 cpm), or moderate-to-vigorous physical activity (MVPA, ≥ 1952 cpm). Note that all the axes’ labels and grid lines were removed from the images when creating movement behavior images for training the convolutional autoencoders. One movement behavior image was created for each participant