Falling and Drowning Detection Framework Using Smartphone Sensors

Abstract

Advancements in health monitoring using smartphone sensor technologies have made it possible to quantify the functional performance and deviations in an individual's routine. Falling and drowning are significant unnatural causes of silent accidental deaths, which require an ambient approach to be detected. This paper presents the novel ambient assistive framework Falling and Drowning Detection (FaDD) for falling and drowning detection. FaDD perceives input from smartphone sensors, such as accelerometer, gyroscope, magnetometer, and GPS, that provide accurate readings of the movement of an individual's body. FaDD hierarchically recognizes the falling and drowning actions by applying the machine learning model. The approach activates embedding, in a smartphone application, to notify emergency alerts to various stakeholders (i.e., guardian, rescue, and close circle community) about drowning of an individual. FaDD detects falling, drowning, and routine actions with good accuracy of 98%. Furthermore, the FaDD framework enhances coordination to provide more efficient and reliable healthcare services to people.