Human Activity Recognition

References

[1] Chen, L., Nugent, C. D., & Oliveira, G. M. B. (2011). Activity Recognition in Pervasive Intelligent Environments.

[2] Mishra, S. K., & De, T. (2019). Deep Learning for Human Activity Recognition.

[3] Roggen, D., Calatroni, A., & Rossi, M. (2013). Human Activity Recognition: Using Wearable Sensors and Smartphones.

[4] Myllymäki, P., & Suykens, P. J. H. (2018). Human Activity Recognition and Prediction.

[5] Augusto, J. C., & Cook, M. (2019). Machine Learning for Human Activity Recognition.

[6] Dey, N., Ashour, A. S., & Fong, S. J. (2020). Wearable Sensors and Health Informatics.

[7] Grewal, S., Butler, D. P., & Donndelinger, J. A. C. (2019). Data Science for Healthcare.

[8] Liu, B., Usama, M., & Liu, A. X. (2016). Privacy in Location-Based Applications.

[9] Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists.

[10] Bishop, C. M. (2006). Pattern Recognition and Machine Learning.

[11] Krumm, J. (2009). Ubiquitous Computing Fundamentals.

[12] Xiang, T., Hospedales, T., & Gong, S. (2011). Human Activity Recognition: Towards Natural Interaction and Experience in Video.

[13] Barfield, W., & Caudell, T. (2001). Fundamentals of Wearable Computers and Augmented Reality.

[14] Mehmood, I., Bilal, M., Anwar, S., & Rho, S. (2017). Human Activity Recognition: A Review.

[15] Thuemmler, C. (2017). Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare.

[16] Langs, G., & Rother, C. (2019). Machine Learning in Medicine: Part Three.

[17] Xie, S., Chen, W., Gu, S., & Du, R. (2017). Wearable Robotics: Systems and Applications.

[18] Namjoshi, P. (2020). Machine Learning for Healthcare: A Comprehensive Guide for Practitioners.

[19] Augusto, J. C. (2013). Ambient Intelligence Perspectives.

[20] Salah, A. A., Gevers, T., & Sebe, N. (2013). Human Behavior Understanding.

[21] Poslad, S. (2011). Ubiquitous Computing: Smart Devices, Environments and Interactions.

[22] Sonntag, D., & Peleg, M. (2014). Data Mining in Biomedicine: Using Ontologies.

[23] Sazonov, E., & Neuman, M. R. (2014). Wearable Sensors: Fundamentals, Implementation and Applications.

[24] Shan,S., & Chellappa, R. (2019). Human-Centric Machine Learning.

@article{1705380,
      author = {Pramodh Yadav M, Purvika S, Chatur S},
      title = {Human Activity Recognition},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {277-283},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705380.pdf},
      abstract = {The Human Activity Recognition (HAR) system is a prominent research area that aims to develop intelligent algorithms and systems capable of automatically identifying and classifying human activities based on sensor data. This project report provides a thorough exploration of the key aspects involved in the design, implementation, and evaluation of a Human Activity Recognition system.
The report begins with a comprehensive review of the existing literature, covering the fundamental concepts, methodologies, and recent advancements in HAR. It discusses various sensor modalities commonly used for activity recognition, including accelerometer, gyroscope, and magnetometer data. Special attention is given to machine learning and deep learning techniques employed in the recognition process. The project involves the development of a prototype HAR system using state-of-the-art techniques. The implementation utilizes a dataset representative of diverse human activities to train and test the system. The chosen methodology is presented in detail, highlighting the selection and preprocessing of sensor data, feature extraction, and the training of machine learning or deep learning models.},
      month = {January},
  }