期刊
CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION
卷 2, 期 1, 页码 18-32出版社
SPRINGERNATURE
DOI: 10.1007/s42486-020-00026-2
关键词
Convolutional Neural Networks; Deep learning; Human Activity Recognition; Free-living
类别
资金
- ACROSSING project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 [676157]
The 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.
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