Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18403, first published .
Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

Journals

  1. Cui S, Lin Q, Gui Y, Zhang Y, Lu H, Zhao H, Wang X, Li X, Jiang F. CARE as a wearable derived feature linking circadian amplitude to human cognitive functions. npj Digital Medicine 2023;6(1) View
  2. Liu J, Zhang X, Lin T, Chen R, Zhong Y, Chen T, Wu T, Liu C, Huang A, Nguyen T, Lee E, Jeste D, Tu X. A new paradigm for high‐dimensional data: Distance‐based semiparametric feature aggregation framework via between‐subject attributes. Scandinavian Journal of Statistics 2024;51(2):672 View
  3. Reinhart L, Bischops A, Kerth J, Hagemeister M, Heinrichs B, Eickhoff S, Dukart J, Konrad K, Mayatepek E, Meissner T. Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance. Intelligence-Based Medicine 2024;9:100134 View
  4. Corponi F, Li B, Anmella G, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie S, Whalley H, Hidalgo-Mazzei D, Vergari A. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Translational Psychiatry 2024;14(1) View
  5. Veldman S, Gubbels J, Singh A, Koedijker J, Chinapaw M, Altenburg T. Correlates of Fundamental Motor Skills in the Early Years (0–4 Years): A Systematic Review. Journal of Motor Learning and Development 2024;12(1):1 View