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

Mobile Health (mhealth) (2972) mHealth for Wellness, Behavior Change and Prevention (2911) Wearable Devices and Sensors (900) Fitness Trackers and Smart Pedometers/Accelerometers (568)

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 Wu1 ;   Sanjoy Dasgupta2 ;   Ernesto E. Ramirez1 ;   Carlyn Peterson1 ;   Gregory J Norman1
Article Authors Cited by (244) Tweetations (15) Metrics

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  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
  141. Zhu J, Wu Y, Lin S, Duan S, Wang X, Fang Y. Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis. Journal of Affective Disorders 2024;350:590 View
  142. Caro-Alvaro S, Garcia-Lopez E, Brun-Guajardo A, Garcia-Cabot A, Mavri A. Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps. Sensors 2024;24(3):1004 View
  143. 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 2024;2024(2) View
  144. Imanzadeh S, Tanha J, Jalili M. Ensemble of deep learning techniques to human activity recognition using smart phone signals. Multimedia Tools and Applications 2024 View
  145. De Ramón Fernández A, Ruiz Fernández D, García Jaén M, Cortell-Tormo J. Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study. JMIR Medical Informatics 2024;12:e57097 View
  146. Alam M, Hasnine I, Bahadur E, Masum A, Urbano M, Vergara M, Uddin J, Ashraf I, Samad M. DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network. Journal of Big Data 2024;11(1) View
  147. Sharen H, Jani Anbarasi L, Rukmani P, Gandomi A, Neeraja R, Narendra M. WISNet: A deep neural network based human activity recognition system. Expert Systems with Applications 2024;258:124999 View
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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
  25. Liu Y, Hung P. Encyclopedia of Computer Graphics and Games. View
  26. Prabha B, Nagaraj J, Hemanth A, Viswanath A, Gadde B, Suravarapu S. Advances in Data-Driven Computing and Intelligent Systems. View
  27. Mekruksavanich S, Jitpattanakul A. Computational Science and Its Applications – ICCSA 2024. View
  28. Singh O. Modeling and Optimization of Signals Using Machine Learning Techniques. View