Human Activity Recognition Using Recurrent Neural Networks

By Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, Andreas Holzinger. 2017

167 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

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Citations in the corpus

Explainable AI: The New 42? (2018, background): “Most of all, this would increase acceptance and trust, which is mandatory in safety-critical systems [12], and desirable in many applications (e.g., in medical robotics [19], Ambient Assisted Living [23], Enterprise decision making [4], etc.).”
What do we need to build explainable AI systems for the medical domain? (2017, background): “…convolutional neural networks (CNN) and recurrent neural networks (RNN) have demonstrated to be applicable to a wide range of practical problems, from image recognition (Simonyan and Zisserman, 2014) and image classiﬁcation (Esteva et al., 2017) to movement recognition (Singh et al., 2017).”
Causability and explainability of artificial intelligence in medicine (2019, background): “…convolutional neural networks (CNN) and recurrent neural networks (RNN) have been demonstrated to be applicable to a wide range of practical problems, from image recognition (Simonyan & Zisserman, 2014) and image classification (Esteva et al., 2017) to movement recognition (Singh et al., 2017).”
IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “A similar setup for activity recognition for ambient assisted living has been presented in [156–158] where they use data from a number of different sensors including motion, presence, water ﬂoat, temperature etc to determine various activities being performed in a home.”