An android application to predict human activity using a deep learning LSTM model
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
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Brac University
2024
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| On-line přístup: | http://hdl.handle.net/10361/23155 |
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10361-231552024-06-05T21:04:01Z An android application to predict human activity using a deep learning LSTM model Sikder, Debabrata Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Machine learning Deep learning Human activity recognition Recurrent neural networks Long short-term memory networks This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from the PDF version of thesis. Includes bibliographical references (pages 33-35). The machine learning approach to estimate human activity using smartphone sensor data is challenging. In this project, the HAR approach is conducted based on the LSTM model and can recognize six different behaviors, i.e., Downstairs, Jogging, Sitting, Standing, Upstairs, and Walking. To achieve the best potential result, various machine learning and statistical approaches were explored. The long shortterm memory (LSTM) is a recurrent neural networks (RNNs) capable of learning long-term dependencies, especially in sequence prediction problems. This LSTM model was applied in this project, to obtain the desired result. This model shows 97% test accuracy. Finally, the model was exported and deployed in the Android application, which has an user interface that could provide a user-friendly experience. Debabrata Sikder M.Sc. in Computer Science 2024-06-05T07:57:45Z 2024-06-05T07:57:45Z 2023 2023 Project report ID 19366011 http://hdl.handle.net/10361/23155 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine learning Deep learning Human activity recognition Recurrent neural networks Long short-term memory networks |
| spellingShingle |
Machine learning Deep learning Human activity recognition Recurrent neural networks Long short-term memory networks Sikder, Debabrata An android application to predict human activity using a deep learning LSTM model |
| description |
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Sikder, Debabrata |
| format |
Project report |
| author |
Sikder, Debabrata |
| author_sort |
Sikder, Debabrata |
| title |
An android application to predict human activity using a deep learning LSTM model |
| title_short |
An android application to predict human activity using a deep learning LSTM model |
| title_full |
An android application to predict human activity using a deep learning LSTM model |
| title_fullStr |
An android application to predict human activity using a deep learning LSTM model |
| title_full_unstemmed |
An android application to predict human activity using a deep learning LSTM model |
| title_sort |
android application to predict human activity using a deep learning lstm model |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23155 |
| work_keys_str_mv |
AT sikderdebabrata anandroidapplicationtopredicthumanactivityusingadeeplearninglstmmodel AT sikderdebabrata androidapplicationtopredicthumanactivityusingadeeplearninglstmmodel |
| _version_ |
1814308916449771520 |