A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends
Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/7/3276 |
| _version_ | 1797440553184395264 |
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| author | Mohamed Esmail Karar Hazem Ibrahim Shehata Omar Reyad |
| author_facet | Mohamed Esmail Karar Hazem Ibrahim Shehata Omar Reyad |
| author_sort | Mohamed Esmail Karar |
| collection | DOAJ |
| description | Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly. |
| first_indexed | 2024-03-09T12:09:51Z |
| format | Article |
| id | doaj.art-a77af4e641ba489b991e77606e6341d0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:09:51Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a77af4e641ba489b991e77606e6341d02023-11-30T22:53:30ZengMDPI AGApplied Sciences2076-34172022-03-01127327610.3390/app12073276A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future TrendsMohamed Esmail Karar0Hazem Ibrahim Shehata1Omar Reyad2College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaCollege of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaCollege of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaRemote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.https://www.mdpi.com/2076-3417/12/7/3276artificial intelligenceinternet of thingsfall detectionwearable sensorsold people |
| spellingShingle | Mohamed Esmail Karar Hazem Ibrahim Shehata Omar Reyad A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends Applied Sciences artificial intelligence internet of things fall detection wearable sensors old people |
| title | A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends |
| title_full | A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends |
| title_fullStr | A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends |
| title_full_unstemmed | A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends |
| title_short | A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends |
| title_sort | survey of iot based fall detection for aiding elderly care sensors methods challenges and future trends |
| topic | artificial intelligence internet of things fall detection wearable sensors old people |
| url | https://www.mdpi.com/2076-3417/12/7/3276 |
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