Published on in Vol 8 , No 11 (2020) :November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21209, first published .
Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Journals

  1. Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, Sugiura Y, Fujita K. A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study. JMIR mHealth and uHealth 2021;9(3):e26320 View
  2. Friedrich B, Sawabe T, Hein A. Unsupervised statistical concept drift detection for behaviour abnormality detection. Applied Intelligence 2023;53(3):2527 View
  3. Jalali N, Tran N, Sen A, Morita P. Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties. JMIR Infodemiology 2022;2(1):e31813 View
  4. Sahu K, Majowicz S, Dubin J, Morita P. NextGen Public Health Surveillance and the Internet of Things (IoT). Frontiers in Public Health 2021;9 View
  5. Oetomo A, Jalali N, Costa P, Morita P. Indoor Temperatures in the 2018 Heat Wave in Quebec, Canada: Exploratory Study Using Ecobee Smart Thermostats. JMIR Formative Research 2022;6(5):e34104 View
  6. Jalali N, Sahu K, Oetomo A, Morita P. Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study. JMIR mHealth and uHealth 2022;10(4):e28811 View