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Feature learning for Human Activity Recognition using Convolutional Neural Networks: A case study for Inertial Measurement Unit and audio data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cruciani, Federico Vafeiadis, Anastasios Nugent, Chris D. Cleland, Ian McCullagh, Paul Votis, Konstantinos Giakoumis, Dimitrios Tzovaras, Dimitrios Chen, Liming Hamzaoui, Raouf |
| Copyright Year | 2020 |
| Abstract | 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. |
| Starting Page | 18 |
| Ending Page | 32 |
| Page Count | 15 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/s42486-020-00026-2 |
| Volume Number | 2 |
| Alternate Webpage(s) | https://pure.ulster.ac.uk/ws/portalfiles/portal/78430176/final_published_version.pdf |
| Alternate Webpage(s) | https://doi.org/10.1007/s42486-020-00026-2 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
