A Holistic Approach to Elderly Safety: Sensor Fusion, Fall Detection, and Privacy-Preserving Techniques

Abstract

In light of the rising need for elderly care, this paper introduces a comprehensive integrated system centered on ensuring the safety of solitary senior individuals, leveraging the capabilities of the Internet of Things (IoT), deep learning, and sensor technology. We explore both wearable and non-wearable sensors for continuous monitoring. Recognizing the challenges associated with wearable devices - such as battery constraints, user discomfort, and potential inaccuracies - we highlight the benefits of visual object-based fall detection using environmental sensors. These include visual cameras and depth sensors optimally placed within living spaces to bypass the limitations of wearable devices and elevate monitoring precision.

To address potential privacy concerns from ongoing video monitoring, we utilize advanced methods like human skeleton extraction and reversible visual data-hiding schemes. By camouflaging visual data, our proposed method ensures the content remains undetected by conventional means. This data-hiding scheme for videos and images encrypts media in a way that, to the general observer, it appears as random noise, yet it can be securely stored and transferred across platforms. Moreover, our encryption technique draws inspiration from water wave patterns and utilizes circular patterns derived from images, making the chance of brute force decryption nearly impossible. As a result, the system transforms human visuals into anonymous skeletal structures and encrypts visual data, safeguarding both privacy and data integrity.

In essence, our holistic system marries technological advancements with the principles of humane care, striving for a harmonious blend of comfort, precise monitoring, and rigorous privacy preservation.