A Novel Approach to Detect Falls Using an Iot-Based Health Analysis System

Fall detection is a major issue in public healthcare, particularly for the elderly. When their physical condition deteriorates, prompt and dependable care is necessary to mitigate the negative impacts of cascade. The current system, designed by others, is incapable of detecting false falls and lacks a health analysis system. The proposed system monitors a person's activity, employs an effective quaternion algorithm to detect a deviation from typical diurnal conditioning, and then automatically transmits a request for assistance along with the case's location to the carers. These models were initially developed to distinguish fall from daily movements such as walking, moving, sitting, standing, reclining, and bending.