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

Wearable Devices and Sensors (903) mHealth for Data Collection and Research (981) mHealth for Wellness, Behavior Change and Prevention (2922) Fitness Trackers and Smart Pedometers/Accelerometers (571)

Published on in Vol 7, No 2 (2019): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11270, first published .
Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study

Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study

Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study

Authors of this article:

Anis Davoudi1 Author Orcid Image ;   Amal Asiri Wanigatunga2 Author Orcid Image ;   Matin Kheirkhahan3 Author Orcid Image ;   Duane Benjamin Corbett4 Author Orcid Image ;   Tonatiuh Mendoza5 Author Orcid Image ;   Manoj Battula3 Author Orcid Image ;   Sanjay Ranka3 Author Orcid Image ;   Roger Benton Fillingim6 Author Orcid Image ;   Todd Matthew Manini4 Author Orcid Image ;   Parisa Rashidi1 Author Orcid Image
Article Authors Cited by (21) Tweetations (1) Metrics

Journals

  1. Xu Z, Diao S, Teng J, Chen Z, Feng X, Cai X, Yuan X, Zhang H, Li J, Zhang Z. Breed identification of meat using machine learning and breed tag SNPs. Food Control 2021;125:107971 View
  2. Yunusova A, Lai J, Rivera A, Hu S, Labbaf S, Rahmani A, Dutt N, Jain R, Borelli J. Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study. JMIR Research Protocols 2021;10(3):e25775 View
  3. Davoudi A, Mardini M, Nelson D, Albinali F, Ranka S, Rashidi P, Manini T. The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study. JMIR mHealth and uHealth 2021;9(5):e23681 View
  4. Mardini M, Bai C, Wanigatunga A, Saldana S, Casanova R, Manini T. Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning. Sensors 2021;21(10):3352 View
  5. Dong L, Qu Y. Body activity grading strategy for cervical rehabilitation training. Computer Methods in Biomechanics and Biomedical Engineering 2023;26(12):1489 View
  6. Li Q, Liu Y, Zhu J, Chen Z, Liu L, Yang S, Zhu G, Zhu B, Li J, Jin R, Tao J, Chen L. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. JMIR mHealth and uHealth 2021;9(9):e24402 View
  7. Gao Z, Liu W, McDonough D, Zeng N, Lee J. The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities. Journal of Clinical Medicine 2021;10(24):5951 View
  8. Bai C, Chen Y, Wolach A, Anthony L, Mardini M. Using Smartwatches to Detect Face Touching. Sensors 2021;21(19):6528 View
  9. Yanmaz L, Okur S, Ersoz U, Senocak M, Turgut F. Accuracy of Heart Rate Measurements of Three Smartwatch Models in Dogs. Topics in Companion Animal Medicine 2022;49:100654 View
  10. d’Angelis O, Di Biase L, Vollero L, Merone M. IoT architecture for continuous long term monitoring: Parkinson’s Disease case study. Internet of Things 2022;20:100614 View
  11. Bai C, Wanigatunga A, Saldana S, Casanova R, Manini T, Mardini M. Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?. Sensors 2022;22(8):3061 View
  12. Alibašić H. Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis. FinTech 2023;2(3):430 View
  13. Kim J, Choi J, Kim H, Lee T, Ha J, Lee S, Park J, Jeon G, Cho S. Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis. JMIR mHealth and uHealth 2023;11:e50663 View
  14. Sánchez-Sánchez M, Campos-Asensio C, Arias-Rivera S. Workloads of intensive care nurses. Validity of their estimation using mobile applications and comparison with Nursing Activities Score. Systematised review of the literature. Enfermería Intensiva (English ed.) 2024;35(3):229 View
  15. Sánchez-Sánchez M, Campos-Asensio C, Arias-Rivera S. Cargas de trabajo de las enfermeras en cuidados intensivos. Validez de su estimación mediante aplicaciones para dispositivos móviles y comparación con nursing activities score. Revisión sistematizada de la literatura. Enfermería Intensiva 2024;35(3):229 View
  16. Demrozi F, Pravadelli G, Bihorac A, Rashidi P. Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey. IEEE Access 2020;8:210816 View
  17. White J, Finnegan O, Tindall N, Nelakuditi S, Brown D, Pate R, Welk G, de Zambotti M, Ghosal R, Wang Y, Burkart S, Adams E, Chandrashekhar M, Armstrong B, Beets M, Weaver R, Yamada Y. Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table. PLOS ONE 2024;19(3):e0286898 View
  18. Armstrong B, Weaver R, McAninch J, Smith M, Parker H, Lane A, Wang Y, Pate R, Rahman M, Matolak D, Chandrashekhar M. Development and Calibration of a PATCH Device for Monitoring Children’s Heart Rate and Acceleration. Medicine & Science in Sports & Exercise 2024 View

Books/Policy Documents

  1. Richter A, Kühtreiber P, Reinhardt D. Privacy and Identity Management. Between Data Protection and Security. View