Published on in Vol 6, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35803, first published .
Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study

Journals

  1. Park C, Rouzi M, Atique M, Finco M, Mishra R, Barba-Villalobos G, Crossman E, Amushie C, Nguyen J, Calarge C, Najafi B. Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring. Sensors 2023;23(10):4949 View
  2. Arakawa R, Ahuja K, Mak K, Thompson G, Shaaban S, Lindhiem O, Goel M. LemurDx. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(2):1 View
  3. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View
  4. Salazar de Pablo G, Iniesta R, Bellato A, Caye A, Dobrosavljevic M, Parlatini V, Garcia-Argibay M, Li L, Cabras A, Haider Ali M, Archer L, Meehan A, Suleiman H, Solmi M, Fusar-Poli P, Chang Z, Faraone S, Larsson H, Cortese S. Individualized prediction models in ADHD: a systematic review and meta-regression. Molecular Psychiatry 2024 View
  5. Cibrian F, Monteiro E, Lakes K. Digital assessments for children and adolescents with ADHD: a scoping review. Frontiers in Digital Health 2024;6 View
  6. Toki E, Zakopoulou V, Tatsis G, Pange J. Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial. JMIR Formative Research 2024;8:e53465 View

Books/Policy Documents

  1. Basic J, Uusimaa J, Salmi J. Digital Health and Wireless Solutions. View