Abstract :

Wearable safety solutions have become increasingly important for protecting vulnerable populations such as elderly individuals and bikers. Falls are among the leading causes of injury in elderly people, often resulting in serious health complications or hospitalization. Similarly, bikers face a high risk of accidents due to road hazards, sudden stops, or collisions, which can lead to severe injuries or fatalities. This research proposes a wearable device that can detect falls and accidents in real-time, ensuring prompt assistance. The device integrates sensors such as accelerometers, gyroscopes, and pressure sensors to monitor movement patterns continuously. Machine learning algorithms process sensor data to differentiate between normal activities and falls accurately. Once a fall or accident is detected, an alert is automatically sent to caregivers, family members, or emergency services. GPS tracking provides precise location information for immediate response. The device also monitor