Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care
Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/3/1736 |
| _version_ | 1797623093519187968 |
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| author | Dorothy Bai Mu-Chieh Ho Bhekumuzi M. Mathunjwa Yeh-Liang Hsu |
| author_facet | Dorothy Bai Mu-Chieh Ho Bhekumuzi M. Mathunjwa Yeh-Liang Hsu |
| author_sort | Dorothy Bai |
| collection | DOAJ |
| description | Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users’ bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents’ on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients’ sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers’ main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users’ personalized sleep-related data. |
| first_indexed | 2024-03-11T09:23:47Z |
| format | Article |
| id | doaj.art-08ae5ebfbb984576b4a9f54ae0f8343e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:23:47Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-08ae5ebfbb984576b4a9f54ae0f8343e2023-11-16T18:05:26ZengMDPI AGSensors1424-82202023-02-01233173610.3390/s23031736Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision CareDorothy Bai0Mu-Chieh Ho1Bhekumuzi M. Mathunjwa2Yeh-Liang Hsu3School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, TaiwanGerontechnology Research Center, Yuan Ze University, Taoyuan 320, TaiwanGerontechnology Research Center, Yuan Ze University, Taoyuan 320, TaiwanGerontechnology Research Center, Yuan Ze University, Taoyuan 320, TaiwanBed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users’ bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents’ on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients’ sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers’ main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users’ personalized sleep-related data.https://www.mdpi.com/1424-8220/23/3/1736motion-sensing mattresspatient fallmachine learningcare interventionprecision care |
| spellingShingle | Dorothy Bai Mu-Chieh Ho Bhekumuzi M. Mathunjwa Yeh-Liang Hsu Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care Sensors motion-sensing mattress patient fall machine learning care intervention precision care |
| title | Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care |
| title_full | Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care |
| title_fullStr | Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care |
| title_full_unstemmed | Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care |
| title_short | Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care |
| title_sort | deriving multiple layer information from a motion sensing mattress for precision care |
| topic | motion-sensing mattress patient fall machine learning care intervention precision care |
| url | https://www.mdpi.com/1424-8220/23/3/1736 |
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