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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. Prabha P A, R S, R S, T G S. Recurrent Neural Network for Human Action Recognition using Star Skeletonization. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2019;:335
    CrossRef
  2. Romeo A, Edney S, Plotnikoff R, Curtis R, Ryan J, Sanders I, Crozier A, Maher C. Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2019;21(3):e12053
    CrossRef
  3. Xu S, Tang Q, Jin L, Pan Z. A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones. Sensors 2019;19(10):2307
    CrossRef
  4. 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 2019;14(3):352
    CrossRef
  5. Faria GS, Polese JC, Ribeiro-Samora GA, Scianni AA, Faria CD, Teixeira-Salmela LF. Validity of the accelerometer and smartphone application in estimating energy expenditure in individuals with chronic stroke. Brazilian Journal of Physical Therapy 2019;23(3):236
    CrossRef
  6. Zhou B, Yang J, Li Q. Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network. Sensors 2019;19(3):621
    CrossRef
  7. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth 2019;7(8):e11966
    CrossRef
  8. Majumder S, Deen MJ. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164
    CrossRef
  9. Keefe RF, Zimbelman EG, Wempe AM. Use of smartphone sensors to quantify the productive cycle elements of hand fallers on industrial cable logging operations. International Journal of Forest Engineering 2019;30(2):132
    CrossRef
  10. Wang Y, Cang S, Yu H. A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications 2019;137:167
    CrossRef
  11. Choi S, Moon J, Park H, Choi ST. User Identification from Gait Analysis Using Multi-Modal Sensors in Smart Insole. Sensors 2019;19(17):3785
    CrossRef
  12. Gao M, Zöllner JM. Sparse Contextual Task Learning and Classification to Assist Mobile Robot Teleoperation with Introspective Estimation. Journal of Intelligent & Robotic Systems 2019;93(3-4):571
    CrossRef
  13. Xia S, Wei P, Vega JM, Jiang X. SPINDLES+: An adaptive and personalized system for leg shake detection. Smart Health 2018;9-10:204
    CrossRef
  14. Bagot K, Matthews S, Mason M, Squeglia LM, Fowler J, Gray K, Herting M, May A, Colrain I, Godino J, Tapert S, Brown S, Patrick K. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience 2018;32:121
    CrossRef
  15. Hassan MM, Uddin MZ, Mohamed A, Almogren A. A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems 2018;81:307
    CrossRef
  16. Vallabh P, Malekian R. Fall detection monitoring systems: a comprehensive review. Journal of Ambient Intelligence and Humanized Computing 2018;9(6):1809
    CrossRef
  17. Wan N, Wen M, Fan JX, Tavake-Pasi OF, McCormick S, Elliott K, Nicolosi E. Physical Activity Barriers and Facilitators Among US Pacific Islanders and the Feasibility of Using Mobile Technologies for Intervention: A Focus Group Study With Tongan Americans. Journal of Physical Activity and Health 2018;15(4):287
    CrossRef
  18. Dowd KP, Szeklicki R, Minetto MA, Murphy MH, Polito A, Ghigo E, van der Ploeg H, Ekelund U, Maciaszek J, Stemplewski R, Tomczak M, Donnelly AE. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. International Journal of Behavioral Nutrition and Physical Activity 2018;15(1)
    CrossRef
  19. Cheng X, Fang L, Yang L. Mobile Big Data Based Network Intelligence. IEEE Internet of Things Journal 2018;5(6):4365
    CrossRef
  20. Jain A, Kanhangad V. Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors. IEEE Sensors Journal 2018;18(3):1169
    CrossRef
  21. 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 2018;32(6):563
    CrossRef
  22. Saha J, Chowdhury C, Biswas S. Two phase ensemble classifier for smartphone based human activity recognition independent of hardware configuration and usage behaviour. Microsystem Technologies 2018;24(6):2737
    CrossRef
  23. Saha J, Chowdhury C, Roy Chowdhury I, Biswas S, Aslam N. An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †. Information 2018;9(4):94
    CrossRef
  24. 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 2018;36(5):558
    CrossRef
  25. Pires IM, Teixeira MC, Pombo N, Garcia NM, Flórez-Revuelta F, Spinsante S, Goleva R, Zdravevski E. Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions. The Open Bioinformatics Journal 2018;11(1):61
    CrossRef
  26. Lawanont W, Inoue M, Mongkolnam P, Nukoolkit C. Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept. IEEJ Transactions on Electrical and Electronic Engineering 2018;13(10):1501
    CrossRef
  27. 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 2017;42(4):321
    CrossRef
  28. 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
    CrossRef
  29. 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
    CrossRef
  30. Cheng X, Fang L, Yang L, Cui S. Mobile Big Data: The Fuel for Data-Driven Wireless. IEEE Internet of Things Journal 2017;4(5):1489
    CrossRef
  31. 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
    CrossRef
  32. 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
  33. 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
    CrossRef
  34. Chen Y, Shen C. Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access 2017;5:3095
    CrossRef
  35. 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
    CrossRef
  36. Guo S, Xiong H, Zheng X, Zhou Y. Activity Recognition and Semantic Description for Indoor Mobile Localization. Sensors 2017;17(3):649
    CrossRef
  37. Lokare N, Zhong B, Lobaton E. Activity-Aware Physiological Response Prediction Using Wearable Sensors. Inventions 2017;2(4):32
    CrossRef
  38. 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
    CrossRef
  39. 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
  40. 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
    CrossRef
  41. 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
  42. Reyes-Ortiz J, Oneto L, Samà A, Parra X, Anguita D. Transition-Aware Human Activity Recognition Using Smartphones. Neurocomputing 2016;171:754
    CrossRef
  43. 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
  44. 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
    CrossRef
  45. 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
    CrossRef
  46. Saez Y, Baldominos A, Isasi P. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition. Sensors 2016;17(12):66
    CrossRef
  47. 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
  48. 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
  49. Chen J, Tan H, Pan Z. Experimental validation of smartphones for measuring human-induced loads. Smart Structures and Systems 2016;18(3):625
    CrossRef
  50. 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
  51. 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
    CrossRef
  52. Ozcan K, Velipasalar S. Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices. IEEE Embedded Systems Letters 2016;8(1):6
    CrossRef
  53. 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
  54. Ronao CA, Cho S. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications 2016;59:235
    CrossRef
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. Jung-Min Lee , Gregory J. Welk , Timothy R. Derrick , Young-Won Kim , 권이석 . Feasibility of Calibrating Smartphone to Access Physical Activity. The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science 2015;17(3):49
    CrossRef
  61. 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
  62. 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
  63. 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
  64. Bardus M, Smith JR, Samaha L, Abraham C. Mobile Phone and Web 2.0 Technologies for Weight Management: A Systematic Scoping Review. Journal of Medical Internet Research 2015;17(11):e259
    CrossRef
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. Arif M, Bilal M, Kattan A, Ahamed SI. Better Physical Activity Classification using Smartphone Acceleration Sensor. Journal of Medical Systems 2014;38(9)
    CrossRef
  73. 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
  74. Kim K, Lee M. Image Obfuscation in the User-Friendly Sensitive Area with the Use of a Sensor for Smart Devices and Image Processing Techniques. International Journal of Distributed Sensor Networks 2014;10(5):797353
    CrossRef
  75. 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
  76. Trowbridge MJ, Pickell SG, Pyke CR, Jutte DP. Building Healthy Communities: Establishing Health And Wellness Metrics For Use Within The Real Estate Industry. Health Affairs 2014;33(11):1923
    CrossRef
  77. 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
  78. 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
  79. 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(04):319
    CrossRef
  80. 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
  81. 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. Lu Y, Velipasalar S. Embedded, Cyber-Physical, and IoT Systems. 2020. Chapter 7:149
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  2. Sadouk L, Gadi T. Lecture Notes in Real-Time Intelligent Systems. 2019. Chapter 43:485
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  3. Ghorrati Z, Matson ET. Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection. 2019. Chapter 8:90
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  4. Shoaib M, Incel OD, Scholten H, Havinga P. Mobile Computing, Applications, and Services. 2018. Chapter 7:106
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  5. Jiang X, Lu Y, Lu Z, Zhou H. Web and Big Data. 2018. Chapter 10:101
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  6. Cheng X, Fang L, Yang L, Cui S. Mobile Big Data. 2018. Chapter 5:51
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  7. Rovniak LS, King AC. Walking. 2017. :249
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  8. Piwek L, Joinson A. Behavior Change Research and Theory. 2017. :137
    CrossRef
  9. Oneto L, Ortiz JL, Anguita D. Adaptive Mobile Computing. 2017. :127
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  10. Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, Holzinger A. Machine Learning and Knowledge Extraction. 2017. Chapter 18:267
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  11. Lehsan K, Bootkrajang J. Intelligent Data Engineering and Automated Learning – IDEAL 2017. 2017. Chapter 5:36
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  12. Yu Z, Huang L, Guo H, Xu H. Knowledge Science, Engineering and Management. 2016. Chapter 36:453
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  13. Zhao Z, Sun Z, Huang L, Guo H, Wang J, Xu H. Wireless Algorithms, Systems, and Applications. 2016. Chapter 17:186
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  14. Liu B, Koc AB. Encyclopedia of Mobile Phone Behavior. 2015. chapter 35:410
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  15. Oniga S, Pop-Sitar P. Hybrid Artificial Intelligent Systems. 2013. Chapter 52:520
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