This paper is in the following e-collection/theme issue:

mHealth for Data Collection and Research (907) New Methods (901) Smoking Cessation (514) Wearable Devices and Sensors (811) Fitness Trackers and Smart Pedometers/Accelerometers (539) Ecological Momentary Assessment (EMA) (294)

Published on in Vol 5, No 12 (2017): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9035, first published .
Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

Authors of this article:

Casey A Cole1 Author Orcid Image ;   Dien Anshari2 Author Orcid Image ;   Victoria Lambert2 Author Orcid Image ;   James F Thrasher2 Author Orcid Image ;   Homayoun Valafar1 Author Orcid Image
Article Authors Cited by (16) Tweetations (20) Metrics

Journals

  1. Agac S, Shoaib M, Durmaz Incel O. Smoking recognition with smartwatch sensors in different postures and impact of user’s height. Journal of Ambient Intelligence and Smart Environments 2020;12(3):239 View
  2. Imtiaz M, Ramos-Garcia R, Wattal S, Tiffany S, Sazonov E. Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review. Sensors 2019;19(21):4678 View
  3. Ortis A, Caponnetto P, Polosa R, Urso S, Battiato S. A Report on Smoking Detection and Quitting Technologies. International Journal of Environmental Research and Public Health 2020;17(7):2614 View
  4. Chien T, Lin C, Fan C. Deep Learning Based Driver Smoking Behavior Detection for Driving Safety. Journal of Image and Graphics 2020;8(1):15 View
  5. Cole C, Powers S, Tomko R, Froeliger B, Valafar H. Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology. JMIR Formative Research 2021;5(2):e20464 View
  6. Can Y, Ersoy C. Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring. ACM Transactions on Internet Technology 2021;21(1):1 View
  7. Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps?. Sensors 2021;21(13):4254 View
  8. Odhiambo C, Ablonczy L, Wright P, Corbett C, Reichardt S, Valafar H. Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study. JMIR Human Factors 2023;10:e42714 View
  9. Omolaoye T, El Shahawy O, Skosana B, Boillat T, Loney T, du Plessis S. The mutagenic effect of tobacco smoke on male fertility. Environmental Science and Pollution Research 2022;29(41):62055 View
  10. Brin M, Trujillo P, Jia H, Cioe P, Huang M, Chen H, Qian X, Xu W, Schnall R. Pilot Testing of an mHealth App for Tobacco Cessation in People Living With HIV: Protocol for a Pilot Randomized Controlled Trial. JMIR Research Protocols 2023;12:e49558 View
  11. Horvath M, Pittman B, O’Malley S, Grutman A, Khan N, Gueorguieva R, Brewer J, Garrison K. Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial. Annals of Medicine 2024;56(1) View
  12. Deepak G, Sharma P, Jayachitra S, Chepur J, Srihari T, Judgi T. Privacy-preserving deep learning approaches for effective utilization of wearable health data. Measurement: Sensors 2024;33:101238 View

Books/Policy Documents

  1. Roth C, Nitschke M, Hutzler C, Koller M, Küffner R, Roßberger M, Kesdoğan D. Secure IT Systems. View
  2. Staab S, Bröning L, Luderschmidt J, Martin L. Human Aspects of IT for the Aged Population. View