Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers
The number of elderly people suffering from dementia, a senile disease, is increasing day by day due to the rapid aging of the population. As a result, social and economic costs are also gradually increasing. To prevent such monetary losses, a system that can operate at a low cost is needed to care...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9146821/ |
| _version_ | 1818433207416127488 |
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| author | Kookjin Kim Seungjin Lee Sungjoong Kim Jaekeun Kim Dongil Shin Dongkyoo Shin |
| author_facet | Kookjin Kim Seungjin Lee Sungjoong Kim Jaekeun Kim Dongil Shin Dongkyoo Shin |
| author_sort | Kookjin Kim |
| collection | DOAJ |
| description | The number of elderly people suffering from dementia, a senile disease, is increasing day by day due to the rapid aging of the population. As a result, social and economic costs are also gradually increasing. To prevent such monetary losses, a system that can operate at a low cost is needed to care for dementia patients. Therefore, this research proposes a sensor-based deviant behavior detection system that allows caregivers to easily manage dementia patients even if they are not in the same location as their dementia patients at a low cost. In this research, the autoencoder and the LSTM models were used together, because deviance behavior is difficult to obtain labeled data. The autoencoder model is a representative unsupervised learning model, which can be used to extract characteristics of data, and was used to learn characteristics of normal behavioral data. The LSTM model is used to determine the deviant behavior from output outlier data that exceeds the threshold in the autoencoder. As a result of the experiment, each model achieved more than 96% and more than 99% accuracy. This research is expected to help caregivers of dementia patients manage the elderly with dementia more inexpensively and efficiently. |
| first_indexed | 2024-12-14T16:17:25Z |
| format | Article |
| id | doaj.art-4646166e58194ae38eb09b29c705f85d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:17:25Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4646166e58194ae38eb09b29c705f85d2022-12-21T22:54:53ZengIEEEIEEE Access2169-35362020-01-01813600413601310.1109/ACCESS.2020.30116549146821Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia CaregiversKookjin Kim0https://orcid.org/0000-0001-9094-4053Seungjin Lee1Sungjoong Kim2https://orcid.org/0000-0002-7347-5045Jaekeun Kim3Dongil Shin4https://orcid.org/0000-0002-8621-715XDongkyoo Shin5https://orcid.org/0000-0002-2665-3339Department of Computer Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Engineering, Sejong University, Seoul, South KoreaThe number of elderly people suffering from dementia, a senile disease, is increasing day by day due to the rapid aging of the population. As a result, social and economic costs are also gradually increasing. To prevent such monetary losses, a system that can operate at a low cost is needed to care for dementia patients. Therefore, this research proposes a sensor-based deviant behavior detection system that allows caregivers to easily manage dementia patients even if they are not in the same location as their dementia patients at a low cost. In this research, the autoencoder and the LSTM models were used together, because deviance behavior is difficult to obtain labeled data. The autoencoder model is a representative unsupervised learning model, which can be used to extract characteristics of data, and was used to learn characteristics of normal behavioral data. The LSTM model is used to determine the deviant behavior from output outlier data that exceeds the threshold in the autoencoder. As a result of the experiment, each model achieved more than 96% and more than 99% accuracy. This research is expected to help caregivers of dementia patients manage the elderly with dementia more inexpensively and efficiently.https://ieeexplore.ieee.org/document/9146821/Deviant detectiondeep-learningautoencoderlong short-term memory models |
| spellingShingle | Kookjin Kim Seungjin Lee Sungjoong Kim Jaekeun Kim Dongil Shin Dongkyoo Shin Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers IEEE Access Deviant detection deep-learning autoencoder long short-term memory models |
| title | Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers |
| title_full | Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers |
| title_fullStr | Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers |
| title_full_unstemmed | Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers |
| title_short | Sensor-Based Deviant Behavior Detection System Using Deep Learning to Help Dementia Caregivers |
| title_sort | sensor based deviant behavior detection system using deep learning to help dementia caregivers |
| topic | Deviant detection deep-learning autoencoder long short-term memory models |
| url | https://ieeexplore.ieee.org/document/9146821/ |
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