Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50035, first published .

Journals

  1. Brand Y, Kluge F, Palmerini L, Paraschiv-Ionescu A, Becker C, Cereatti A, Maetzler W, Sharrack B, Vereijken B, Yarnall A, Rochester L, Del Din S, Muller A, Buchman A, Hausdorff J, Perlman O. Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults. Scientific Reports 2024;14(1) View
  2. Decker J, Boborzi L, Schniepp R, Jahn K, Wuehr M. Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning. Sensors 2024;24(19):6442 View