1. Revolutionizing patient care: An automated guided wheelchair integrated with health data analytics for enhanced mobility and real-time health monitoring.
- Author
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Lim, C. K. and Lau, C. Y.
- Subjects
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DATA analytics , *MEDICAL personnel , *OXYGEN in the blood , *HOSPITAL patients , *BODY temperature - Abstract
The global shortage of healthcare workers is a significant barrier to delivering prompt and efficient healthcare services to the growing patient population in hospitals. Concurrently, the number of patients requiring wheelchairs is increasing, placing additional strain on healthcare workers. Many hospitals still rely on traditional manually operated wheelchairs, leading to patient dissatisfaction due to long waits for assistance. In response, a solution has been suggested: an automated wheelchair equipped with a health data analytics system. This wheelchair employs a color line navigation system, using color sensors to follow pre-set paths. It includes safety features such as obstacle detection and monitors vital health data (e.g., body temperature, pulse, blood oxygen level) in real time via a graphical interface. Health data analysis uses a Long Short-Term Memory (LSTM) model in MATLAB to identify potential health issues early by predicting patient health trends. A working prototype has been developed, incorporating these sensors and algorithms, and thoroughly tested. The prototype has been refined to address lighting issues, achieving up to 93% accuracy in color navigation under various lighting conditions. It can detect obstacles and stop within 0.24 seconds, and health data is effectively monitored and predicted using an LSTM model optimized in MATLAB. This system demonstrates the potential to improve patient mobility and health monitoring in hospitals. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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