Human activities recognition in smart living environment
Smartphones with embedded sensors can track users' movements and corresponding activities in real-time. This serves as the foundation for the recognition of human activity in smart environments where users can accurately monitor their lifestyle and physical health. There are various different w...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166757 |
| _version_ | 1826123903120965632 |
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| author | Teh, Min Yang |
| author2 | Soh Yeng Chai |
| author_facet | Soh Yeng Chai Teh, Min Yang |
| author_sort | Teh, Min Yang |
| collection | NTU |
| description | Smartphones with embedded sensors can track users' movements and corresponding activities in real-time. This serves as the foundation for the recognition of human activity in smart environments where users can accurately monitor their lifestyle and physical health. There are various different well-known machine learning techniques that can process this sensor data to derive high-level information about users, including their locations, activities they are engaging in, and the associated energy devices they are using.
Through an integration of these sensor data from mobile devices, this project aims to build an integrated information system addressing the analysis of basic and complex behaviors done by the users in a smart environment and their usage of energy-related devices. Long Short-Term Memory (LSTM), one of the more well-known machine learning approaches, is used in this project. With the model built using LSTM, an Android application is developed to verify the model's accuracy in real-time. As a part of Human Activity Recognition (HAR), an audio classification is also implemented, where the model is able to classify the sound generated by the different activities performed. |
| first_indexed | 2024-10-01T06:12:16Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166757 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:12:16Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1667572023-07-07T16:16:53Z Human activities recognition in smart living environment Teh, Min Yang Soh Yeng Chai School of Electrical and Electronic Engineering EYCSOH@ntu.edu.sg Engineering::Electrical and electronic engineering Smartphones with embedded sensors can track users' movements and corresponding activities in real-time. This serves as the foundation for the recognition of human activity in smart environments where users can accurately monitor their lifestyle and physical health. There are various different well-known machine learning techniques that can process this sensor data to derive high-level information about users, including their locations, activities they are engaging in, and the associated energy devices they are using. Through an integration of these sensor data from mobile devices, this project aims to build an integrated information system addressing the analysis of basic and complex behaviors done by the users in a smart environment and their usage of energy-related devices. Long Short-Term Memory (LSTM), one of the more well-known machine learning approaches, is used in this project. With the model built using LSTM, an Android application is developed to verify the model's accuracy in real-time. As a part of Human Activity Recognition (HAR), an audio classification is also implemented, where the model is able to classify the sound generated by the different activities performed. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-10T06:34:05Z 2023-05-10T06:34:05Z 2023 Final Year Project (FYP) Teh, M. Y. (2023). Human activities recognition in smart living environment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166757 https://hdl.handle.net/10356/166757 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Teh, Min Yang Human activities recognition in smart living environment |
| title | Human activities recognition in smart living environment |
| title_full | Human activities recognition in smart living environment |
| title_fullStr | Human activities recognition in smart living environment |
| title_full_unstemmed | Human activities recognition in smart living environment |
| title_short | Human activities recognition in smart living environment |
| title_sort | human activities recognition in smart living environment |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/166757 |
| work_keys_str_mv | AT tehminyang humanactivitiesrecognitioninsmartlivingenvironment |