Feature learning for Human Activity Recognition using Convolutional Neural Networks
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Published:2020-01-24
Issue:1
Volume:2
Page:18-32
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ISSN:2524-521X
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Container-title:CCF Transactions on Pervasive Computing and Interaction
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language:en
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Short-container-title:CCF Trans. Pervasive Comp. Interact.
Author:
Cruciani FedericoORCID, Vafeiadis AnastasiosORCID, Nugent ChrisORCID, Cleland IanORCID, McCullagh PaulORCID, Votis Konstantinos, Giakoumis Dimitrios, Tzovaras Dimitrios, Chen LimingORCID, Hamzaoui RaoufORCID
Abstract
AbstractThe use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.
Funder
H2020 Marie Sklodowska-Curie Actions
Publisher
Springer Science and Business Media LLC
Subject
General Engineering
Reference41 articles.
1. Abadi, M., Agarwal, A., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/, software available from tensorflow.org (2015) 2. Abdel-Hamid, O., Ar, Mohamed, Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(10), 1533–1545 (2014) 3. Alsina-Pagès, R., Navarro, J., Alías, F., Hervás, M.: homesound: Real-time audio event detection based on high performance computing for behaviour and surveillance remote monitoring. Sensors 17(4), 854 (2017) 4. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (2013) 5. Baldominos, A., Cervantes, A., Saez, Y., Isasi, P.: A comparison of machine learning and deep learning techniques for activity recognition using mobile devices. Sensors 19(3), 521 (2019). https://doi.org/10.3390/s19030521
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