Human Activity Recognition Using Recurrent Neural Networks

• Published in: Machine Learning and Knowledge Extraction Vol. 10410; pp. 267 - 274
• Main Authors: Singh, Deepika, Merdivan, Erinc, Psychoula, Ismini, Kropf, Johannes, Hanke, Sten, Geist, Matthieu, Holzinger, Andreas
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Ambient assisted living; Deep learning; Human activity recognition; LSTM; Machine learning; Sensors
• ISBN: 3319668072; 9783319668079
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-66808-6_18

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.