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

Mobile Health (mhealth) (2355) mHealth for Wellness, Behavior Change and Prevention (2523) Wearable Devices and Sensors (733) Fitness Trackers and Smart Pedometers/Accelerometers (513)

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

Classification Accuracies of Physical Activities Using Smartphone Motion Sensors

Classification Accuracies of Physical Activities Using Smartphone Motion Sensors

Classification Accuracies of Physical Activities Using Smartphone Motion Sensors

Authors of this article:

Wanmin Wu 1 ;   Sanjoy Dasgupta 2 ;   Ernesto E. Ramirez 1 ;   Carlyn Peterson 1 ;   Gregory J Norman 1
Article Authors Cited by (222) Tweetations (19) Metrics

Journals

  1. Cheng X, Fang L, Yang L. Mobile Big Data Based Network Intelligence. IEEE Internet of Things Journal 2018;5(6):4365 View
  2. Pande A, Mohapatra P, Nicorici A, Han J. 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 View
  3. Park E, Chang H, Nam H. Use of Machine Leaning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Journal of Medical Internet Research 2017;19(4):e120 View
  4. Crizer M, Kazarian G, Fleischman A, Lonner J, Maltenfort M, Chen A. Stepping Toward Objective Outcomes: A Prospective Analysis of Step Count After Total Joint Arthroplasty. The Journal of Arthroplasty 2017;32(9):S162 View
  5. Keefe R, Zimbelman E, Wempe A. 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 View
  6. Payne H, Lister C, West J, Bernhardt J. Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature. JMIR mHealth and uHealth 2015;3(1):e20 View
  7. 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 View
  8. Chen Y, Shen C. Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access 2017;5:3095 View
  9. 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 View
  10. Majumder S, Deen M. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164 View
  11. Bort-Roig J, Gilson N, Puig-Ribera A, Contreras R, Trost S. Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review. Sports Medicine 2014;44(5):671 View
  12. 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 View
  13. Winand M, Ng A, Byers T. Pokémon “Go” but for how long?: a qualitative analysis of motivation to play and sustainability of physical activity behaviour in young adults using mobile augmented reality. Managing Sport and Leisure 2022;27(5):421 View
  14. San-Segundo R, Montero J, Barra-Chicote R, Fernández F, Pardo J. Feature extraction from smartphone inertial signals for human activity segmentation. Signal Processing 2016;120:359 View
  15. Wan S, Qi L, Xu X, Tong C, Gu Z. Deep Learning Models for Real-time Human Activity Recognition with Smartphones. Mobile Networks and Applications 2020;25(2):743 View
  16. Shoaib M, Bosch S, Incel O, Scholten H, Havinga P. Fusion of Smartphone Motion Sensors for Physical Activity Recognition. Sensors 2014;14(6):10146 View
  17. Jim H, Hoogland A, Brownstein N, Barata A, Dicker A, Knoop H, Gonzalez B, Perkins R, Rollison D, Gilbert S, Nanda R, Berglund A, Mitchell R, Johnstone P. Innovations in research and clinical care using patient‐generated health data. CA: A Cancer Journal for Clinicians 2020;70(3):182 View
  18. Mateos-Angulo A, Galán-Mercant A, Cuesta-Vargas A. Kinematic Mobile Drop Jump Analysis at Different Heights Based on a Smartphone Inertial Sensor. Journal of Human Kinetics 2020;73(1):57 View
  19. Hobert M, Maetzler W, Aminian K, Chiari L. Technical and clinical view on ambulatory assessment in Parkinson's disease. Acta Neurologica Scandinavica 2014;130(3):139 View
  20. 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 View
  21. Lokare N, Zhong B, Lobaton E. Activity-Aware Physiological Response Prediction Using Wearable Sensors. Inventions 2017;2(4):32 View
  22. Miller M, Meier E, Lombardi N, Leffingwell T. Theories of behaviour change and personalised feedback interventions for college student drinking. Addiction Research & Theory 2015;23(4):322 View
  23. Acampora G, Minopoli G, Musella F, Staffa M. Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks. Electronics 2020;9(3):409 View
  24. 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 View
  25. 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 View
  26. Ebrahimi M, Aghagolzadeh P, Shamabadi N, Tahmasebi A, Alsharifi M, Adelson D, Hemmatzadeh F, Ebrahimie E, Tompkins S. Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein. PLoS ONE 2014;9(5):e96984 View
  27. 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 View
  28. Xu S, Tang Q, Jin L, Pan Z. A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones. Sensors 2019;19(10):2307 View
  29. Lendner N, Wells E, Lavi I, Kwok Y, Ho P, Wollstein R. Utility of the iPhone 4 Gyroscope Application in the Measurement of Wrist Motion. HAND 2019;14(3):352 View
  30. Jain A, Kanhangad V. Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors. IEEE Sensors Journal 2018;18(3):1169 View
  31. 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 View
  32. 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 View
  33. Goyal S, Morita P, Lewis G, Yu C, Seto E, Cafazzo J. 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 View
  34. San-Segundo R, Lorenzo-Trueba J, Martínez-González B, Pardo J. Segmenting human activities based on HMMs using smartphone inertial sensors. Pervasive and Mobile Computing 2016;30:84 View
  35. Guo S, Xiong H, Zheng X, Zhou Y. Activity Recognition and Semantic Description for Indoor Mobile Localization. Sensors 2017;17(3):649 View
  36. Zhou B, Yang J, Li Q. Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network. Sensors 2019;19(3):621 View
  37. Vallabh P, Malekian R. Fall detection monitoring systems: a comprehensive review. Journal of Ambient Intelligence and Humanized Computing 2018;9(6):1809 View
  38. 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 View
  39. Arif M, Bilal M, Kattan A, Ahamed S. Better Physical Activity Classification using Smartphone Acceleration Sensor. Journal of Medical Systems 2014;38(9) View
  40. Sheng B, Moosman O, Del Pozo-Cruz B, Del Pozo-Cruz J, Alfonso-Rosa R, Zhang Y. A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification. Measurement 2020;154:107480 View
  41. 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 View
  42. Fanning J, Mullen S, McAuley E. Increasing Physical Activity With Mobile Devices: A Meta-Analysis. Journal of Medical Internet Research 2012;14(6):e161 View
  43. Nurmi J, Knittle K, Ginchev T, Khattak F, Helf C, Zwickl P, Castellano-Tejedor C, Lusilla-Palacios P, Costa-Requena J, Ravaja N, Haukkala A. Engaging Users in the Behavior Change Process With Digitalized Motivational Interviewing and Gamification: Development and Feasibility Testing of the Precious App. JMIR mHealth and uHealth 2020;8(1):e12884 View
  44. Sullivan A, Lachman M. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Frontiers in Public Health 2017;4 View
  45. 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 View
  46. Xia S, Wei P, Vega J, Jiang X. SPINDLES+: An adaptive and personalized system for leg shake detection. Smart Health 2018;9-10:204 View
  47. Lisiński , Wareńczak , Hejdysz , Sip , Gośliński , Owczarek , Jonak , Goślińska . Mobile Applications in Evaluations of Knee Joint Kinematics: A Pilot Study. Sensors 2019;19(17):3675 View
  48. Ronao C, Cho S. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications 2016;59:235 View
  49. 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 View
  50. Ozcan K, Velipasalar S. Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices. IEEE Embedded Systems Letters 2016;8(1):6 View
  51. Wan N, Wen M, Fan J, Tavake-Pasi O, 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 View
  52. Dowd K, Szeklicki R, Minetto M, Murphy M, Polito A, Ghigo E, van der Ploeg H, Ekelund U, Maciaszek J, Stemplewski R, Tomczak M, Donnelly A. 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) View
  53. 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 View
  54. Choi S, Moon J, Park H, Choi S. User Identification from Gait Analysis Using Multi-Modal Sensors in Smart Insole. Sensors 2019;19(17):3785 View
  55. 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 View
  56. 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 View
  57. Wang Q, Egelandsdal B, Amdam G, Almli V, Oostindjer M. Diet and Physical Activity Apps: Perceived Effectiveness by App Users. JMIR mHealth and uHealth 2016;4(2):e33 View
  58. 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 View
  59. Saez Y, Baldominos A, Isasi P. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition. Sensors 2016;17(12):66 View
  60. Yang X, Ma L, Zhao X, Kankanhalli A. Factors influencing user’s adherence to physical activity applications: A scoping literature review and future directions. International Journal of Medical Informatics 2020;134:104039 View
  61. Zhou X, Yu W, Sullivan W. Making pervasive sensing possible: Effective travel mode sensing based on smartphones. Computers, Environment and Urban Systems 2016;58:52 View
  62. 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 View
  63. Hassan M, Uddin M, Mohamed A, Almogren A. A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems 2018;81:307 View
  64. 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 View
  65. 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 View
  66. Bagot K, Matthews S, Mason M, Squeglia L, 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 View
  67. Faria G, Polese J, Ribeiro-Samora G, Scianni A, Faria C, Teixeira-Salmela L. 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 View
  68. 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 View
  69. 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 View
  70. Donaire-Gonzalez D, de Nazelle A, Seto E, Mendez M, Nieuwenhuijsen M, Jerrett M. Comparison of Physical Activity Measures Using Mobile Phone-Based CalFit and Actigraph. Journal of Medical Internet Research 2013;15(6):e111 View
  71. Panchal U, Ajmani H, Sait S. Flooding Level Classification by Gait Analysis of Smartphone Sensor Data. IEEE Access 2019;7:181678 View
  72. Sansano E, Montoliu R, Belmonte Fernández Ó. A study of deep neural networks for human activity recognition. Computational Intelligence 2020;36(3):1113 View
  73. 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 View
  74. 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 View
  75. Shoaib M, Bosch S, Incel O, Scholten H, Havinga P. A Survey of Online Activity Recognition Using Mobile Phones. Sensors 2015;15(1):2059 View
  76. 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 View
  77. Gao M, Zöllner J. Sparse Contextual Task Learning and Classification to Assist Mobile Robot Teleoperation with Introspective Estimation. Journal of Intelligent & Robotic Systems 2019;93(3-4):571 View
  78. Reyes-Ortiz J, Oneto L, Samà A, Parra X, Anguita D. Transition-Aware Human Activity Recognition Using Smartphones. Neurocomputing 2016;171:754 View
  79. Lister C, West J, Cannon B, Sax T, Brodegard D. Just a Fad? Gamification in Health and Fitness Apps. JMIR Serious Games 2014;2(2):e9 View
  80. Bardus M, Smith J, 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 View
  81. Shen C, Chen Y, Yang G, Guan X. Toward Hand-Dominated Activity Recognition Systems With Wristband-Interaction Behavior Analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020;50(7):2501 View
  82. San-Segundo R, Montero J, Moreno-Pimentel J, Pardo J. HMM Adaptation for Improving a Human Activity Recognition System. Algorithms 2016;9(3):60 View
  83. Khan U, Khan I, Din A, Jadoon W, Jadoon R, Khan M, Khan F, Khan A. Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors. Scientific Programming 2020;2020:1 View
  84. Ozcan K, Velipasalar S, Varshney P. Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure. IEEE Transactions on Human-Machine Systems 2016:1 View
  85. Bort-Roig J, Puig-Ribera A, Contreras R, Chirveches-Pérez E, Martori J, Gilson N, 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 View
  86. Zhuo S, Sherlock L, Dobbie G, Koh Y, Russello G, Lottridge D. Real-time Smartphone Activity Classification Using Inertial Sensors—Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking. Sensors 2020;20(3):655 View
  87. 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 View
  88. 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 View
  89. Vanhelst J, Béghin L, Duhamel A, De Henauw S, Ruiz J, 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 View
  90. 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 View
  91. Pires I, Teixeira M, Pombo N, Garcia N, 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 View
  92. 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 View
  93. 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 View
  94. Trowbridge M, Pickell S, Pyke C, Jutte D. Building Healthy Communities: Establishing Health And Wellness Metrics For Use Within The Real Estate Industry. Health Affairs 2014;33(11):1923 View
  95. Chen J, Tan H, Pan Z. Experimental validation of smartphones for measuring human-induced loads. Smart Structures and Systems 2016;18(3):625 View
  96. Abbaspour S, Fotouhi F, Sedaghatbaf A, Fotouhi H, Vahabi M, Linden M. A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition. Sensors 2020;20(19):5707 View
  97. Sevil M, Rashid M, Maloney Z, Hajizadeh I, Samadi S, Askari M, Hobbs N, Brandt R, Park M, Quinn L, Cinar A. Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems. IEEE Sensors Journal 2020;20(21):12859 View
  98. Ebner M, Fetzer T, Bullmann M, Deinzer F, Grzegorzek M. Recognition of Typical Locomotion Activities Based on the Sensor Data of a Smartphone in Pocket or Hand. Sensors 2020;20(22):6559 View
  99. Moorman V, King M. Angular orientations derived from a portable media device to assess postural stability during quiet standing in the horse. Equine Veterinary Education 2022;34(5) View
  100. Merry K, Macdonald E, MacPherson M, Aziz O, Park E, Ryan M, Sparrey C. Classifying sitting, standing, and walking using plantar force data. Medical & Biological Engineering & Computing 2021;59(1):257 View
  101. Gholamrezaii M, AlModarresi S. A time-efficient convolutional neural network model in human activity recognition. Multimedia Tools and Applications 2021;80(13):19361 View
  102. Anagnostis A, Benos L, Tsaopoulos D, Tagarakis A, Tsolakis N, Bochtis D. Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture. Applied Sciences 2021;11(5):2188 View
  103. Lawless S, Moorman V, Hendrickson D, Mama K. Comparison of sedation quality and safety of detomidine and romifidine as a continuous rate infusion for standing elective laparoscopic ovariectomy in mares. Veterinary Surgery 2021;50(5):990 View
  104. Domel A, Raymond S, Giordano C, Liu Y, Yousefsani S, Fanton M, Cecchi N, Vovk O, Pirozzi I, Kight A, Avery B, Boumis A, Fetters T, Jandu S, Mehring W, Monga S, Mouchawar N, Rangel I, Rice E, Roy P, Sami S, Singh H, Wu L, Kuo C, Zeineh M, Grant G, Camarillo D. A new open-access platform for measuring and sharing mTBI data. Scientific Reports 2021;11(1) View
  105. Alajaji A, Gerych W, Buquicchio L, Chandrasekaran K, Mansoor H, Agu E, Rundensteiner E. Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild Contexts. IEEE Pervasive Computing 2021;20(1):50 View
  106. Arieyanto H, Chowanda A. Classification of Wing Chun Basic Hand Movement using Virtual Reality for Wing Chun Training Simulation System. Advances in Science, Technology and Engineering Systems Journal 2020;6(1):250 View
  107. Akagi J, Morris T, Moon B, Chen X, Peterson C. Gesture commands for controlling high-level UAV behavior. SN Applied Sciences 2021;3(6) View
  108. Márquez-Sánchez S, Campero-Jurado I, Robles-Camarillo D, Rodríguez S, Corchado-Rodríguez J. BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces. Sensors 2021;21(10):3372 View
  109. Zimbelman E, Keefe R, Chen C. Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations. PLOS ONE 2021;16(5):e0250624 View
  110. Dedeyne L, Wullems J, Dupont J, Tournoy J, Gielen E, Verschueren S. Exploring Machine Learning Models Based on Accelerometer Sensor Alone or Combined With Gyroscope to Classify Home-Based Exercises and Physical Behavior in (Pre)sarcopenic Older Adults. Journal for the Measurement of Physical Behaviour 2021;4(2):174 View
  111. Yue Z, Luo J, Husheng F, Shao F, Ranzhi Z. Study on the Identification Method of Human Upper Limb Flag Movements Based on Inception-ResNet Double Stream Network. IEEE Access 2021;9:85764 View
  112. Tang X, Yu S, Chu J, Fan H. Damaged/missing proximity sensor induces screen mistouch when answering calls: Prediction of smartphone answering status by posture data. Journal of Intelligent & Fuzzy Systems 2021;41(1):1963 View
  113. Hirawat A, Taterh S, Sharma T. A public domain dataset to recognize driver entry into and exit from a car using smartphone sensors. International Journal of System Assurance Engineering and Management 2021 View
  114. Kavuncuoğlu E, Uzunhisarcıklı E, Barshan B, Özdemir A. Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning. Digital Signal Processing 2022;126:103365 View
  115. Russell J, Bergmann J, Nagaraja V. Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks. Sensors 2022;22(7):2603 View
  116. Alfuraih A, Alqarni M, Alhuthaili H, Mubaraki M, Alotaibi N, Almusalim F. Reproducibility and feasibility of a handheld ultrasound device compared to a standard ultrasound machine in muscle thickness measurements. Australasian Journal of Ultrasound in Medicine 2023;26(1):13 View
  117. Liu R, Menhas R, Dai J, Saqib Z, Peng X. Fitness Apps, Live Streaming Workout Classes, and Virtual Reality Fitness for Physical Activity During the COVID-19 Lockdown: An Empirical Study. Frontiers in Public Health 2022;10 View
  118. Varshney N, Bakariya B, Kushwaha A, Khare M. Human activity recognition by combining external features with accelerometer sensor data using deep learning network model. Multimedia Tools and Applications 2022;81(24):34633 View
  119. Jebelli H, Choi B, Lee S. Application of Wearable Biosensors to Construction Sites. II: Assessing Workers’ Physical Demand. Journal of Construction Engineering and Management 2019;145(12) View
  120. Luo Y, Guo C, Su J, Guo W, Zhang Q. Learning-Based Complex Motion Patterns Recognition for Pedestrian Dead Reckoning. IEEE Sensors Journal 2021;21(4):4280 View
  121. Ahmad I, Khusro S, Alam I, Khan I, Niazi B, Khan Z. Towards a Low-Cost Teacher Orchestration Using Ubiquitous Computing Devices for Detecting Student’s Engagement. Wireless Communications and Mobile Computing 2022;2022:1 View
  122. Qiu S, Zhao H, Jiang N, Wang Z, Liu L, An Y, Zhao H, Miao X, Liu R, Fortino G. Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Information Fusion 2022;80:241 View
  123. Bernaś M, Płaczek B, Lewandowski M. Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes. Sensors 2022;22(23):9451 View
  124. Bourahmoune K, Ishac K, Amagasa T. Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion. Sensors 2022;22(14):5337 View
  125. Ganesan A, Paul A, Seo H, Samikannu R. Elderly People Activity Recognition in Smart Grid Monitoring Environment. Mathematical Problems in Engineering 2022;2022:1 View
  126. Elfghi M, Dunne D, Jones J, Gibson I, Flaherty G, McEvoy J, Sultan S, Jordan F, Tawfick W. Mobile health technologies to improve walking distance in people with intermittent claudication. Cochrane Database of Systematic Reviews 2021;2021(8) View
  127. Abdullah S, Choudhury T. Sensing Technologies for Monitoring Serious Mental Illnesses. IEEE MultiMedia 2018;25(1):61 View
  128. Li Y, Wang L. Human Activity Recognition Based on Residual Network and BiLSTM. Sensors 2022;22(2):635 View
  129. Khan I, Khusro S, Ullah I. Identifying the walking patterns of visually impaired people by extending white cane with smartphone sensors. Multimedia Tools and Applications 2023;82(17):27005 View
  130. Han P, Ping L, Ling G, Yin O, How K. Stacked deep analytic model for human activity recognition on a UCI HAR database. F1000Research 2021;10:1046 View
  131. Pavliuk O, Mishchuk M, Strauss C. Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform. Algorithms 2023;16(2):77 View
  132. Liu Y, Hung P, Iqbal F, Fung B. Automatic Fall Risk Detection Based on Imbalanced Data. IEEE Access 2021;9:163594 View
  133. He J, Zhang Q, Wang L, Pei L. Weakly Supervised Human Activity Recognition From Wearable Sensors by Recurrent Attention Learning. IEEE Sensors Journal 2019;19(6):2287 View
  134. Straczkiewicz M, James P, Onnela J. A systematic review of smartphone-based human activity recognition methods for health research. npj Digital Medicine 2021;4(1) View
  135. Bisson A, Sorrentino V, Lachman M. Walking and Daily Affect Among Sedentary Older Adults Measured Using the StepMATE App: Pilot Randomized Controlled Trial. JMIR mHealth and uHealth 2021;9(12):e27208 View
  136. Lone K, Hussain L, Saeed S, Aslam A, Maqbool A, Butt F. Detecting basic human activities and postural transition using robust machine learning techniques by applying dimensionality reduction methods. Waves in Random and Complex Media 2021:1 View
  137. Nurmi J, Knittle K, Naughton F, Sutton S, Ginchev T, Khattak F, Castellano-Tejedor C, Lusilla-Palacios P, Ravaja N, Haukkala A. Biofeedback and Digitalized Motivational Interviewing to Increase Daily Physical Activity: Series of Factorial N-of-1 Randomized Controlled Trials Piloting the Precious App. JMIR Formative Research 2023;7:e34232 View
  138. Zhao Q, Li G, Cai J, Zhou M, Feng L. A Tutorial on Internet of Behaviors: Concept, Architecture, Technology, Applications, and Challenges. IEEE Communications Surveys & Tutorials 2023;25(2):1227 View
  139. Kumar P, Chauhan S, Awasthi L. Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions. Archives of Computational Methods in Engineering 2023 View
  140. Huafeng G, Changcheng X, Shiqiang C. Wearable sensors for human activity recognition based on a self-attention CNN-BiLSTM model. Sensor Review 2023;43(5/6):347 View

Books/Policy Documents

  1. Oniga S, Pop-Sitar P. Hybrid Artificial Intelligent Systems. View
  2. Lu Y, Velipasalar S. Embedded, Cyber-Physical, and IoT Systems. View
  3. Rovniak L, King A. Walking. View
  4. Yu Z, Huang L, Guo H, Xu H. Knowledge Science, Engineering and Management. View
  5. Shoaib M, Incel O, Scholten H, Havinga P. Mobile Computing, Applications, and Services. View
  6. Liu B, Koc A. Encyclopedia of Mobile Phone Behavior. View
  7. Zhao Y, Li Q, Farha F, Zhu T, Chen L, Ning H. Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. View
  8. Jiang X, Lu Y, Lu Z, Zhou H. Web and Big Data. View
  9. Piwek L, Joinson A. Behavior Change Research and Theory. View
  10. Oneto L, Ortiz J, Anguita D. Adaptive Mobile Computing. View
  11. Sadouk L, Gadi T. Lecture Notes in Real-Time Intelligent Systems. View
  12. Cheng X, Fang L, Yang L, Cui S. Mobile Big Data. View
  13. Gaur S, Gupta G. ICDSMLA 2019. View
  14. Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, Holzinger A. Machine Learning and Knowledge Extraction. View
  15. Ghorrati Z, Matson E. Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection. View
  16. Zhao Z, Sun Z, Huang L, Guo H, Wang J, Xu H. Wireless Algorithms, Systems, and Applications. View
  17. Lehsan K, Bootkrajang J. Intelligent Data Engineering and Automated Learning – IDEAL 2017. View
  18. Tushar A, Kabir M, Ahmed S. Signal Processing Techniques for Computational Health Informatics. View
  19. Gajjala K, Kothamachu Ramesh A, Nakano K, Chakraborty B. Intelligence Science III. View
  20. Reyes Ortiz J. Smartphone-Based Human Activity Recognition. View
  21. Reyes Ortiz J. Smartphone-Based Human Activity Recognition. View
  22. Ciattaglia G, Senigagliesi L, Gambi E. IoT Technologies for HealthCare. View
  23. Liu Y, Hung P. Encyclopedia of Computer Graphics and Games. View
  24. Bouton-Bessac E, Meegahapola L, Gatica-Perez D. Pervasive Computing Technologies for Healthcare. View