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AI Integrated With IoT System for Continuous Elderly Fall Detection Using SmartFallSentry System

AI Integrated With IoT System for Continuous Elderly Fall Detection Using SmartFallSentry System

R. P. Ram Kumar (Gokaraju Rangaraju Institute of Engineering and Technology, India), Manasa Vanam (Gokaraju Rangaraju Institute of Engineering and Technology, India), P. Gopala Krishna (Gokaraju Rangaraju Institute of Engineering and Technology, India), and Mallikarjuna Rao Ch (Gokaraju Rangaraju Institute of Engineering and Technology, India)
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-2105-8.ch011
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Abstract

The parallel development AI and ML is a boon to humanity. It eases the safety and livelihood of people who need to be under constant observation. Artificial intelligence (AI) and machine learning (ML)-enabled healthcare systems aid in the observation of elderly people. Nowadays, elderly people are severely affected while they fall. Falling down might occur due to several reasons like high or low blood pressure, diabetes, Parkinsonism, heart attack, cardiac arrest, brain tumour, and many more. The working lifestyle of other family members leaves the elderly alone and demands observation. For observing such home alone elderly people, the family members depend on exclusive caretakers and healthcare monitoring systems (HMS). When caretakers take off, family members depend on HMS. Henceforth, we need an effective HMS comprising sensor networks, data acquisition and pre-processing, computer vision (CV) module, AI and ML module, decision making and alerting, and especially, user interface.
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2. Proposed Work

In Health Monitoring System (HMS), FDS phase for the elderly people is focussed in this paper. To detect a fall, foremost, real-time monitoring of elderly people is inevitable. Sensors, the back bone for HMS, support and ease real-time monitoring through IoT sensors and wearables, such as, door sensors, motion sensors, gyro meters and accelerometers. Subsequently, the data about the elder is acquired through IoT devices and cameras followed by pre-processing where data is cleaned, and transformed for proceeding supplementary steps as shown in Figure 1 and Figure 3.

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