Human Activity Recognition using Recurrent Neural Networks

Abstract

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.

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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• LaHAR: Latent Human Activity Recognition using LDA