Human Activity Recognition via Hybrid Deep Learning Based Model

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is…

• NR
  National Research Foundation
• MO
  Ministry of Trade, Industry and Energy

  Awards: P0016038, Project No. P0016038
• KI
  Korea Institute for Advancement of Technology

  Award: P0016038
• NR
  National Research Foundation of Korea

  Award: NRF-2017S1A5B6053101