Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency

Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to control the spread of infection, and maintain proactive nursing rounds. However, recruiting and retaining qualified caregivers and medical staff has long been a challenge. Therefore, using advanced technology to ensure the safety and security of residents is highly desirable. In this work, we first demonstrate the applicability of indoor tracking applications in a nursing home, such as resident and asset tracking, nursing assistant management, visitor tracking, infection control, and vital-sign monitoring. To monitor the locations of residents and staff, Bluetooth tags were used, providing real-time data for location tracking. We then conduct a series of quantitative analyses to illustrate how indoor tracking data can support the management of nursing homes, including characterizing residents' activities in daily living and assessing the performance and workload of nursing assistants. Finally, we use qualitative research to evaluate the acceptability of an indoor positioning system in the nursing home. The results show that the implemented indoor positioning applications can improve the quality of healthcare and working efficiency, thereby providing a safer and more secure living environment for residents.

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Publication Details

Journal
Sensors
Published
2024-09-20
DOI
https://doi.org/10.3390/s24186099
Citations
3
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.75

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article

Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency

Edward T.-H. Chu, Chia-Rong Lee, Min-Jing Sie, Ching-Chih Huang et al.
3 citations
Sensors
Context-Aware Activity Recognition Systems
0.75
article

Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency

Edward T.-H. Chu, Chia-Rong Lee, Min-Jing Sie, Ching-Chih Huang, Mei-Zhen Hong, Li-Tsai Lin
article en
3 citations

Abstract

Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to control the spread of infection, and maintain proactive nursing rounds. However, recruiting and retaining qualified caregivers and medical staff has long been a challenge. Therefore, using advanced technology to ensure the safety and security of residents is highly desirable. In this work, we first demonstrate the applicability of indoor tracking applications in a nursing home, such as resident and asset tracking, nursing assistant management, visitor tracking, infection control, and vital-sign monitoring. To monitor the locations of residents and staff, Bluetooth tags were used, providing real-time data for location tracking. We then conduct a series of quantitative analyses to illustrate how indoor tracking data can support the management of nursing homes, including characterizing residents' activities in daily living and assessing the performance and workload of nursing assistants. Finally, we use qualitative research to evaluate the acceptability of an indoor positioning system in the nursing home. The results show that the implemented indoor positioning applications can improve the quality of healthcare and working efficiency, thereby providing a safer and more secure living environment for residents.

SensorsVol. 24(18)
National Yunlin University of Science and Technology (TW)
Ministry of Science and Technology, Taiwan
Openalex Percentile: Top 28%
Context-Aware Activity Recognition Systems
0.75
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Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency — Edward T.-H. Chu, Chia-Rong Lee, et al. · Sensors (2024) | TGRS Research Map | TGRS