Human Activity Recognition using Recurrent Neural Networks
This work addresses activity recognition for smart environments and ambient assisted living, but it is incremental as it applies an existing method to new data.
The paper tackled human activity recognition from smart home sensor data by applying an LSTM recurrent neural network to three real-world datasets, achieving improved accuracy and performance compared to existing methods.
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.