Data-Driven IoT Smart Refrigerators for Medication Storage with Deep Learning Analytics
U. Kavitha, Manjula Pattnaik, S. Jayaprakash, S. A. Yuvaraj, Geetha Ponnaian, Balasubramanian Meenakshi · 2024
The healthcare industry is one of the industries where the usage of smart refrigerators powered by the Internet of Things (IoT) is increasingly rapidly. To improve the performance of IoT smart refrigerators, this research proposes an efficient method that uses deep learning (DL) analytics, particularly Long Short-Term Memory (LSTM) networks. The refrigerator can assess and forecast medicine use trends from past data using LSTM networks, which allows for optimum storage conditions and timely refills. The system design incorporates IoT sensors to track humidity, temperature, and door status in real-time to view the fridge's surroundings fully. LSTM networks analyze the data gathered from these sensors to understand how people use their medications over time. Because of this, the fridge can calculate how much medicine will be needed in the future, notify users if there is a risk of running out, and keep medications at their most effective storage temperatures. The proposed system uses data-driven analytics to make medication management suggestions to each patient's unique profile and treatment regimen. The technology changes medicine storage settings on the fly to accommodate changing patient demands using LSTM networks' sequential data modeling capabilities. Experiment results show that the suggested method improves patient care by decreasing pharmaceutical waste and increasing medication adherence. One potential solution for smart medicine storage in healthcare settings is the integration of DL analytics with IoT smart freezers. This might lead to the development of data-driven pharmaceutical management systems.