Human activity classification using long short-term memory network
Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural net...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
SPRINGER LONDON LTD
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/90320 |
| _version_ | 1848765370027999232 |
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| author | Malshika Welhenge, Anuradhi Taparugssanagorn, A. |
| author_facet | Malshika Welhenge, Anuradhi Taparugssanagorn, A. |
| author_sort | Malshika Welhenge, Anuradhi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network. |
| first_indexed | 2025-11-14T11:34:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-90320 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:34:10Z |
| publishDate | 2019 |
| publisher | SPRINGER LONDON LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-903202023-02-28T02:23:31Z Human activity classification using long short-term memory network Malshika Welhenge, Anuradhi Taparugssanagorn, A. Science & Technology Technology Engineering, Electrical & Electronic Imaging Science & Photographic Technology Engineering Deep learning ADL LSTM RNN TRIAXIAL ACCELEROMETER FALL DETECTION Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network. 2019 Journal Article http://hdl.handle.net/20.500.11937/90320 10.1007/s11760-018-1393-7 English SPRINGER LONDON LTD restricted |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Imaging Science & Photographic Technology Engineering Deep learning ADL LSTM RNN TRIAXIAL ACCELEROMETER FALL DETECTION Malshika Welhenge, Anuradhi Taparugssanagorn, A. Human activity classification using long short-term memory network |
| title | Human activity classification using long short-term memory network |
| title_full | Human activity classification using long short-term memory network |
| title_fullStr | Human activity classification using long short-term memory network |
| title_full_unstemmed | Human activity classification using long short-term memory network |
| title_short | Human activity classification using long short-term memory network |
| title_sort | human activity classification using long short-term memory network |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Imaging Science & Photographic Technology Engineering Deep learning ADL LSTM RNN TRIAXIAL ACCELEROMETER FALL DETECTION |
| url | http://hdl.handle.net/20.500.11937/90320 |