University of Twente Student Theses
Enhancing IMU-based smartphone context detection using Transformer
Le, T.A.D (2022) Enhancing IMU-based smartphone context detection using Transformer.
|
PDF
1MB |
| Abstract: | Deep learning models like Long Short Term Memory (LSTM) or Convolutional Neural Network (CNN) have yielded great results in the field of understanding smartphone context in recent years. In this paper, we propose using another deep learning model based on Transformer to tackle the same task with IMU sensors data. We will start by reviewing an overview of existing approaches for smartphone context detection. A new sensor-based dataset on smartphone context and step recognition is collected. Extensive experiments have been conducted to compare the accuracy of the Transformer model to other deep learning models like LSTM or CNN. Furthermore, the Transformer model used for smartphone context understanding also improves the efficiency of step counting. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/91686 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page