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Journal of Medical Internet Research

Citing this Article

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Published on 05.10.12 in Vol 14, No 5 (2012): Sep-Oct

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

Works citing "Classification Accuracies of Physical Activities Using Smartphone Motion Sensors"

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.2208):

(note that this is only a small subset of citations)

  1. Pande A, Mohapatra P, Nicorici A, Han JJ. Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy. JMIR Rehabilitation and Assistive Technologies 2016;3(2):e7
    CrossRef
  2. López-Nava I, Muñoz-Meléndez A, Pérez Sanpablo A, Alessi Montero A, Quiñones Urióstegui I, Núñez Carrera L. Estimation of temporal gait parameters using Bayesian models on acceleration signals. Computer Methods in Biomechanics and Biomedical Engineering 2016;19(4):396
    CrossRef
  3. Guo H, Huang H, Huang L, Sun Y. Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones. Sensors 2016;16(8):1314
    CrossRef
  4. Zhou X, Yu W, Sullivan WC. Making pervasive sensing possible: Effective travel mode sensing based on smartphones. Computers, Environment and Urban Systems 2016;58:52
    CrossRef
  5. Sun Z, Tang S, Huang H, Zhu Z, Guo H, Sun Y, Huang L. SOS: Real-time and accurate physical assault detection using smartphone. Peer-to-Peer Networking and Applications 2016;
    CrossRef
  6. Goyal S, Morita P, Lewis GF, Yu C, Seto E, Cafazzo JA. The Systematic Design of a Behavioural Mobile Health Application for the Self-Management of Type 2 Diabetes. Canadian Journal of Diabetes 2016;40(1):95
    CrossRef
  7. Wang Q, Egelandsdal B, Amdam GV, Almli VL, Oostindjer M. Diet and Physical Activity Apps: Perceived Effectiveness by App Users. JMIR mHealth and uHealth 2016;4(2):e33
    CrossRef
  8. Ciman M, Donini M, Gaggi O, Aiolli F. Stairstep recognition and counting in a serious Game for increasing users’ physical activity. Personal and Ubiquitous Computing 2016;
    CrossRef
  9. Reyes-Ortiz J, Oneto L, Samà A, Parra X, Anguita D. Transition-Aware Human Activity Recognition Using Smartphones. Neurocomputing 2016;171:754
    CrossRef
  10. Wang A, Chen G, Yang J, Zhao S, Chang C. A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone. IEEE Sensors Journal 2016;16(11):4566
    CrossRef
  11. San-Segundo R, Montero J, Moreno-Pimentel J, Pardo J. HMM Adaptation for Improving a Human Activity Recognition System. Algorithms 2016;9(3):60
    CrossRef
  12. Ronao CA, Cho S. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications 2016;59:235
    CrossRef
  13. San-Segundo R, Montero JM, Barra-Chicote R, Fernández F, Pardo JM. Feature extraction from smartphone inertial signals for human activity segmentation. Signal Processing 2016;120:359
    CrossRef
  14. San-Segundo R, Lorenzo-Trueba J, Martínez-González B, Pardo JM. Segmenting human activities based on HMMs using smartphone inertial sensors. Pervasive and Mobile Computing 2016;30:84
    CrossRef
  15. Ozcan K, Velipasalar S. Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices. IEEE Embedded Systems Letters 2016;8(1):6
    CrossRef
  16. Payne HE, Lister C, West JH, Bernhardt JM. Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature. JMIR mHealth and uHealth 2015;3(1):e20
    CrossRef
  17. Miller MB, Meier E, Lombardi N, Leffingwell TR. Theories of behaviour change and personalised feedback interventions for college student drinking. Addiction Research & Theory 2015;23(4):322
    CrossRef
  18. Pernek I, Kurillo G, Stiglic G, Bajcsy R. Recognizing the intensity of strength training exercises with wearable sensors. Journal of Biomedical Informatics 2015;58:145
    CrossRef
  19. Mimura K, Kishino H, Karino G, Nitta E, Senoo A, Ikegami K, Kunikata T, Yamanouchi H, Nakamura S, Sato K, Koshiba M. Potential of a smartphone as a stress-free sensor of daily human behaviour. Behavioural Brain Research 2015;276:181
    CrossRef
  20. Shoaib M, Bosch S, Incel O, Scholten H, Havinga P. A Survey of Online Activity Recognition Using Mobile Phones. Sensors 2015;15(1):2059
    CrossRef
  21. Liu C, Chan C. An Accumulated Activity Effective Index for Promoting Physical Activity: A Design and Development Study in a Mobile and Pervasive Health Context. JMIR Research Protocols 2015;4(1):e5
    CrossRef
  22. Park S, Kim M, Bae H, Cha Y. The Reliability and Validity of Hip Range of Motion Measurement using a Smart phone Operative Patient. Journal of the Korean Society of Physical Medicine 2015;10(2):1
    CrossRef
  23. Shoaib M, Bosch S, Incel O, Scholten H, Havinga P. Fusion of Smartphone Motion Sensors for Physical Activity Recognition. Sensors 2014;14(6):10146
    CrossRef
  24. Hobert MA, Maetzler W, Aminian K, Chiari L. Technical and clinical view on ambulatory assessment in Parkinson's disease. Acta Neurologica Scandinavica 2014;130(3):139
    CrossRef
  25. Arif M, Bilal M, Kattan A, Ahamed SI. Better Physical Activity Classification using Smartphone Acceleration Sensor. Journal of Medical Systems 2014;38(9)
    CrossRef
  26. Lister C, West JH, Cannon B, Sax T, Brodegard D. Just a Fad? Gamification in Health and Fitness Apps. JMIR Serious Games 2014;2(2):e9
    CrossRef
  27. Stöggl T, Holst A, Jonasson A, Andersson E, Wunsch T, Norström C, Holmberg H. Automatic Classification of the Sub-Techniques (Gears) Used in Cross-Country Ski Skating Employing a Mobile Phone. Sensors 2014;14(11):20589
    CrossRef
  28. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review. Sports Medicine 2014;44(5):671
    CrossRef
  29. Smolders R, De Boever P. Perspectives for environment and health research in Horizon 2020: Dark ages or golden era?. International Journal of Hygiene and Environmental Health 2014;217(8):891
    CrossRef
  30. Gietzelt M, Wolf K, Kohlmann M, Marschollek M, Haux R. Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field. Methods of Information in Medicine 2013;52(4):319
    CrossRef
  31. Donaire-Gonzalez D, de Nazelle A, Seto E, Mendez M, Nieuwenhuijsen MJ, Jerrett M. Comparison of Physical Activity Measures Using Mobile Phone-Based CalFit and Actigraph. Journal of Medical Internet Research 2013;15(6):e111
    CrossRef
  32. Graham D, Suzuki A, Reitz C, Saxena A, Kuo J, Tetsworth K. Measurement of rotational deformity: using a smartphone application is more accurate than conventional methods. ANZ Journal of Surgery 2013;83(12):937
    CrossRef
  33. Fanning J, Mullen SP, McAuley E. Increasing Physical Activity With Mobile Devices: A Meta-Analysis. Journal of Medical Internet Research 2012;14(6):e161
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.2208)

:
  1. Zhao Z, Sun Z, Huang L, Guo H, Wang J, Xu H. Wireless Algorithms, Systems, and Applications. 2016. Chapter 17:186
    CrossRef
  2. Liu B, Koc AB. Encyclopedia of Mobile Phone Behavior. 2015. chapter 35:410
    CrossRef