Wearable Sensor with Artificial Intelligence for Prevention of Falls in Elderly People

Abstract

In the current economic landscape, the cost of hiring full-time caregivers for elderly individuals has become increasingly difficult for many families to bear. With healthcare expenses rising and the demand for elderly care growing due to an aging population, providing constant supervision and support is financially overwhelming for most households. In order to address this issue, we have introduced enhanced AI- IoT framework for real-time elderly people health monitoring and safety management systems. The system integrated all the IoT devices include wearable sensors, environmental sensors, sleeping sensor and smart home technologies, which includes voice assisting, smart light and smart door. These devices communicate wirelessly with a central hub, which aggregates the data and transmits it to a cloud-based platform for further analysis. The advanced AI algorithms process the collected data to detect anomalies and predict potential health issues or imminent falls. When a fall is detected, the system immediately alerts caregivers and medical personnel through mobile notifications and automated calls. The framework incorporates machine learning models that personalize health monitoring based on individual health profiles, medical histories, and lifestyle patterns. This automated system improves the accuracy of health predictions and interventions, ensuring that each user receives tailored care. This innovative approach aims to reduce healthcare costs by preventing hospitalizations and enabling early intervention. It improves the quality of life for elderly individuals by promoting independence and providing a sense of security.

Specification

Description:In the current economic landscape, the cost of hiring full-time caregivers for elderly individuals has become increasingly difficult for many families to bear. With healthcare expenses rising and the demand for elderly care growing due to an aging population, providing constant supervision and support is financially overwhelming for most households. In order to address this issue, we have introduced enhanced AI- IoT framework for real-time elderly people health monitoring and safety management systems. The system integrated all the IoT devices include wearable sensors, environmental sensors, sleeping sensor and smart home technologies, which includes voice assisting, smart light and smart door. These devices communicate wirelessly with a central hub, which aggregates the data and transmits it to a cloud-based platform for further analysis. The advanced AI algorithms process the collected data to detect anomalies and predict potential health issues or imminent falls. When a fall is detected, the system immediately alerts caregivers and medical personnel through mobile notifications and automated calls. The framework incorporates machine learning models that personalize health monitoring based on individual health profiles, medical histories, and lifestyle patterns. This automated system improves the accuracy of health predictions and interventions, ensuring that each user receives tailored care. This innovative approach aims to reduce healthcare costs by preventing hospitalizations and enabling early intervention. It improves the quality of life for elderly individuals by promoting independence and providing a sense of security. , C , Claims:1. The AI-IoT system significantly reduces the financial burden by monitoring elderly person health in continuous without human intervention.
2. The proposed system automates the overall operations of traditional elder people monitoring through advanced tools and techniques.
3. Elder people health monitoring systems can detect elderly people illness at an early stage and it will send alert message to caretaker.
4. By ensuring elder people health, family members can monitor the person health and safety in real-time.

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