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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. Wang Z, Yang Z, Dong T. A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time. Sensors 2017;17(2):341
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  2. 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 2017;10(2):395
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  3. Lonini L, Gupta A, Deems-Dluhy S, Hoppe-Ludwig S, Kording K, Jayaraman A. Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models. JMIR Rehabilitation and Assistive Technologies 2017;4(2):e8
    CrossRef
  4. Cornacchia M, Ozcan K, Zheng Y, Velipasalar S. A Survey on Activity Detection and Classification Using Wearable Sensors. IEEE Sensors Journal 2017;17(2):386
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  5. Bort-Roig J, Puig-Ribera A, Contreras RS, Chirveches-Pérez E, Martori JC, Gilson ND, McKenna J. Monitoring sedentary patterns in office employees: validity of an m-health tool (Walk@Work-App) for occupational health. Gaceta Sanitaria 2017;
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  6. Vanhelst J, Béghin L, Duhamel A, De Henauw S, Ruiz JR, Kafatos A, Manios Y, Widhalm K, Mauro B, Sjöström M, Gottrand F. Physical activity awareness of European adolescents: The HELENA study. Journal of Sports Sciences 2017;:1
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  7. Guo S, Xiong H, Zheng X, Zhou Y. Activity Recognition and Semantic Description for Indoor Mobile Localization. Sensors 2017;17(3):649
    CrossRef
  8. Lendner N, Wells E, Lavi I, Kwok YY, Ho P, Wollstein R. Utility of the iPhone 4 Gyroscope Application in the Measurement of Wrist Motion. HAND 2017;:155894471773060
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  9. Chen Y, Shen C. Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access 2017;5:3095
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  10. Sullivan AN, Lachman ME. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Frontiers in Public Health 2017;4
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  11. Crizer MP, Kazarian GS, Fleischman AN, Lonner JH, Maltenfort MG, Chen AF. Stepping Toward Objective Outcomes: A Prospective Analysis of Step Count After Total Joint Arthroplasty. The Journal of Arthroplasty 2017;32(9):S162
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  12. Park E, Chang H, Nam HS. Use of Machine Leaning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Journal of Medical Internet Research 2017;19(4):e120
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  13. Della Mea V, Quattrin O, Parpinel M. A feasibility study on smartphone accelerometer-based recognition of household activities and influence of smartphone position. Informatics for Health and Social Care 2016;:1
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  14. Ozcan K, Velipasalar S, Varshney PK. Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure. IEEE Transactions on Human-Machine Systems 2016;:1
    CrossRef
  15. San-Segundo R, Montero J, Moreno-Pimentel J, Pardo J. HMM Adaptation for Improving a Human Activity Recognition System. Algorithms 2016;9(3):60
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  16. 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
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  17. Reyes-Ortiz J, Oneto L, Samà A, Parra X, Anguita D. Transition-Aware Human Activity Recognition Using Smartphones. Neurocomputing 2016;171:754
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  18. Liu C, Chan C. Exercise Performance Measurement with Smartphone Embedded Sensor for Well-Being Management. International Journal of Environmental Research and Public Health 2016;13(10):1001
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  19. 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
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  20. Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep Quality Prediction From Wearable Data Using Deep Learning. JMIR mHealth and uHealth 2016;4(4):e125
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  21. Ronao CA, Cho S. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications 2016;59:235
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  22. 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
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  23. 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
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  24. Li P, Wang Y, Tian Y, Zhou T, Li J. An Automatic User-adapted Physical Activity Classification Method Using Smartphones. IEEE Transactions on Biomedical Engineering 2016;:1
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  25. Ozcan K, Velipasalar S. Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices. IEEE Embedded Systems Letters 2016;8(1):6
    CrossRef
  26. 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;20(6):1015
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  27. 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
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  28. 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
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  29. 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
  30. 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
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  31. Chen J, Tan H, Pan Z. Experimental validation of smartphones for measuring human-induced loads. Smart Structures and Systems 2016;18(3):625
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  32. Saez Y, Baldominos A, Isasi P. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition. Sensors 2016;17(1):66
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  33. 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
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  34. 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
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  35. 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
  36. 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
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  37. 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
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  38. 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
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  39. Shoaib M, Bosch S, Incel O, Scholten H, Havinga P. A Survey of Online Activity Recognition Using Mobile Phones. Sensors 2015;15(1):2059
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  40. 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
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  41. Ebrahimi M, Aghagolzadeh P, Shamabadi N, Tahmasebi A, Alsharifi M, Adelson DL, Hemmatzadeh F, Ebrahimie E, Tompkins SM. Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein. PLoS ONE 2014;9(5):e96984
    CrossRef
  42. Arif M, Bilal M, Kattan A, Ahamed SI. Better Physical Activity Classification using Smartphone Acceleration Sensor. Journal of Medical Systems 2014;38(9)
    CrossRef
  43. 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
  44. 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
  45. 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
  46. 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
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  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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. Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, Holzinger A. Machine Learning and Knowledge Extraction. 2017. Chapter 18:267
    CrossRef
  2. Piwek L, Joinson A. Behavior Change Research and Theory. 2017. :137
    CrossRef
  3. Rovniak LS, King AC. Walking. 2017. :249
    CrossRef
  4. Oneto L, Ortiz JL, Anguita D. Adaptive Mobile Computing. 2017. :127
    CrossRef
  5. Zhao Z, Sun Z, Huang L, Guo H, Wang J, Xu H. Wireless Algorithms, Systems, and Applications. 2016. Chapter 17:186
    CrossRef
  6. Yu Z, Huang L, Guo H, Xu H. Knowledge Science, Engineering and Management. 2016. Chapter 36:453
    CrossRef
  7. Liu B, Koc AB. Encyclopedia of Mobile Phone Behavior. 2015. chapter 35:410
    CrossRef