Barua, Arnab (2023) Investigating effects of window length on 1D-CNN-LSTM and effectiveness of Heuristic features in solving sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer. Masters thesis, Memorial University of Newfoundland.
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Abstract
Human Activity Recognition (HAR) using smartphone sensors can offer multiple applications in different spheres. Using deep learning classifiers such as Convolutional Neural Networks (CNN), Short-Term Long Memory (LSTM), or their hybrid showed promising improvement in HAR. However, using these deep learning networks requires segmenting the input data into multiple data windows of similar length. The length of the data windows can significantly affect HAR's performance. Therefore, the influence of the window lengths needs to be investigated to choose an optimal window length. Additionally, the orientation and placement of the smartphone sensor also present significant challenges to HAR. Many approaches have been proposed to solve the orientation and placement problems. In my study, I first evaluated the effects of window length on 1D-CNN-LSTM in HAR for six activities: Lying, Sitting, Walking, and Running at 3-METs (Metabolic Equivalent of Tasks), 5-METs and 7-METs. Subsequently, I evaluated the effectiveness of the heuristic features in HAR in solving sensor orientation and sensor placement problems for three smartphone locations: Pocket, Backpack and Hand. I performed this evaluation using 1D-CNN-LSTM by using the optimal window length found in the first part.
| Item Type: | Thesis (Masters) |
|---|---|
| URI: | http://research.library.mun.ca/id/eprint/16295 |
| Item ID: | 16295 |
| Additional Information: | Includes bibliographical references (pages 94-100) -- Restricted until June 27, 2024 |
| Keywords: | Human Activity Recognition, Convolutional Neural Networks, metabolic equivalent of tasks, accelerometer sensor, sensor orientation, sensor placement, Long Short-Term Memory(LSTM) |
| Department(s): | Science, Faculty of > Computer Science |
| Date: | May 2023 |
| Date Type: | Submission |
| Digital Object Identifier (DOI): | https://doi.org/10.48336/Z88F-D095 |
| Library of Congress Subject Heading: | Human activity recognition; Neural networks (Computer science); Accelerometers; Deep learning (Machine learning); Detectors; Heuristic |
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