AI-based Device for Fall Impact Reduction in Elder People

Falls are the leading cause of injury and hospitalization among older adults, often with a lifelong disability and reduced quality of life. Even preventative measures-frequent use of handrails and walking aids-only go so far in preventing injury once a fall occurs. This paper reports on an AI-powered wearable designed with an emergency airbag deployment system focused on reducing fall-injury impacts. The device is equipped with an accelerometer and gyroscope and is driven by a sophisticated fall detection algorithm powered by machine learning, all controlled by a compact ESP8266 microcontroller. It tirelessly tracks the user's movement, acceleration, and orientation, which allows it to differentiate between ordinary activities and falls. Upon detection of a fall, the device springs into action and inflates an airbag deployment mechanism in just a few milliseconds, softening the impact for the user, significantly reducing the chances of fractures and major injuries. The AI model was fine-tuned to optimize performance on the low-power ESP8266 to make the device light, efficient, and comfortable to wear for long periods. This novel fall impact-reducing technology improves safety while fostering independence and confidence among older users, hopefully providing an answer to the ever-increasing challenge of fall-related injuries in aging populations.