Conference Paper2020

hActNET: An Improved Neural Network based Method in Recognizing Human Activities

Atiqul Islam Chowdhury, Mohsena Ashraf, Ashraful Islam, Eshtiak Ahmed, Md Saroar Jaman, Mohammad Masudur Rahman

2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

IEEE, pp. 1–6

CCDS Authors

References

  1. 1.Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz. (2013). A public domain dataset for human activity recognition using smartphones. RECERCAT (Consorci de Serveis Universitaris de Catalunya), 437–442[link]
  2. 2.Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, Uzoma Rita Alo. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233–261[10.1016/j.eswa.2018.03.056]
  3. 3.Mohammed Mehedi Hassan, Md. Zia Uddin, Amr Mohamed, Ahmad Almogren. (2017). A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems, 81, 307–313[10.1016/j.future.2017.11.029]
  4. 4.Abdulmajid Murad, Jae-Young Pyun. (2017). Deep Recurrent Neural Networks for Human Activity Recognition. Sensors, 17(11), 2556[10.3390/s17112556]
  5. 5.Muhammad Shoaib, Stephan Bosch, Özlem Durmaz İncel, Hans Scholten, Paul Havinga. (2016). Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Sensors, 16(4), 426[10.3390/s16040426]
  6. 6.Song-Mi Lee, Sang Min Yoon, Heeryon Cho. (2017). Human activity recognition from accelerometer data using Convolutional Neural Network. , 131–134[10.1109/bigcomp.2017.7881728]
  7. 7.Aiguo Wang, Guilin Chen, Jing Yang, Shenghui Zhao, Chih‐Yung Chang. (2016). A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone. IEEE Sensors Journal, 16(11), 4566–4578[10.1109/jsen.2016.2545708]
  8. 8.Shugang Zhang, Zhiqiang Wei, Jie Nie, Lei Huang, Shuang Wang, Zhen Li. (2017). A Review on Human Activity Recognition Using Vision-Based Method. Journal of Healthcare Engineering, 2017, 1–31[10.1155/2017/3090343]
  9. 9.Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, Marcin Grzegorzek. (2018). Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors, 18(2), 679[10.3390/s18020679]
  10. 10.Yufei Chen, Chao Shen. (2017). Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access, 5, 3095–3110[10.1109/access.2017.2676168]
  11. 11.Allah Bux Sargano, Xiaofeng Wang, Plamen Angelov, Zulfiqar Habib. (2017). Human action recognition using transfer learning with deep representations. , 463–469[10.1109/ijcnn.2017.7965890]
  12. 12.Eshtiak Ahmed, Ashraful Islam, Farhana Sarker, Mohammad Nurul Huda, Khondaker Abdullah-Al-Mamun. (2016). A road to independent living with smart homes for people with disabilities. , 15, 472–477[10.1109/iciev.2016.7760048]
hActNET: An Improved Neural Network based Method in Recognizing Human Activities | CCDS