Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42714, first published .
Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study

Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study

Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study

Journals

  1. Smith A, Azeem M, Odhiambo C, Wright P, Diktas H, Upton S, Martin C, Froeliger B, Corbett C, Valafar H. Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step. Sensors 2024;24(14):4542 View
  2. Amirthalingam P, Alatawi Y, Chellamani N, Shanmuganathan M, Ali M, Alqifari S, Mani V, Dhanasekaran M, Alqahtani A, Alanazi M, Aljabri A. Sea Horse Optimization–Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition. Sensors 2024;24(16):5224 View
  3. Skaramagkas V, Kyprakis I, Karanasiou G, Fotiadis D, Tsiknakis M. A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data. IEEE Open Journal of Engineering in Medicine and Biology 2025;6:261 View
  4. Castrillón Isaza K, Giraldo Restrepo J, García Uribe J. Riesgos y oportunidades de la inteligencia artificial en el cuidado de enfermería: una revisión de alcance. Trilogía Ciencia Tecnología Sociedad 2025;17(35):e3272 View
  5. Aldurdunji M. Management of sleep disturbance related to Alzheimer disease and dementia: An updated review of ClinicalTrials.gov. Medicine 2025;104(32):e43725 View
  6. Gu B, Kim H, Kim H, Yoo J. Advancements in Wearable Sensor Technologies for Health Monitoring: A Systematic Review of Clinical Applications, Rehabilitation, and Disease Risk Assessment (Preprint). JMIR mHealth and uHealth 2025 View