Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition
Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7936 |
| _version_ | 1797576981092499456 |
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| author | Patricia Franco Felipe Condon José M. Martínez Mohamed A. Ahmed |
| author_facet | Patricia Franco Felipe Condon José M. Martínez Mohamed A. Ahmed |
| author_sort | Patricia Franco |
| collection | DOAJ |
| description | Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from ’hospital-centric’ services to ’home-centric’ services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient’s health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited. |
| first_indexed | 2024-03-10T22:01:27Z |
| format | Article |
| id | doaj.art-75cb8e8f88fc46d78dcdee291a182126 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:27Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-75cb8e8f88fc46d78dcdee291a1821262023-11-19T12:56:20ZengMDPI AGSensors1424-82202023-09-012318793610.3390/s23187936Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity RecognitionPatricia Franco0Felipe Condon1José M. Martínez2Mohamed A. Ahmed3Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileSeniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from ’hospital-centric’ services to ’home-centric’ services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient’s health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited.https://www.mdpi.com/1424-8220/23/18/7936activity detectionappliance recognitionload monitoringmachine learningnon-obtrusivenessremote healthcare |
| spellingShingle | Patricia Franco Felipe Condon José M. Martínez Mohamed A. Ahmed Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition Sensors activity detection appliance recognition load monitoring machine learning non-obtrusiveness remote healthcare |
| title | Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition |
| title_full | Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition |
| title_fullStr | Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition |
| title_full_unstemmed | Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition |
| title_short | Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition |
| title_sort | enabling remote elderly care design and implementation of a smart energy data system with activity recognition |
| topic | activity detection appliance recognition load monitoring machine learning non-obtrusiveness remote healthcare |
| url | https://www.mdpi.com/1424-8220/23/18/7936 |
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