A PROACTIVE WEARABLE SYSTEM TO DETECT AND PREVENT DROWNING USING MACHINE LEARNING AND MULTI-SENSOR INTEGRATION

Drowning remains a serious public safety issue, especially among young and inexperienced swimmers[1]. This paper presents SaveSplash, a comprehensive real-time drowning detection system composed of three main components: a wearable device, a Raspberry Pibased hydrophone alarm system, and a FlutterFlow mobile interface [2]. The wearable detects irregular movement and emits a 100 Hz distress signal. A hydrophone connected to a Raspberry Pi listens for this signal and triggers an alarm while logging events to Firebase[3]. The mobile interface displays alerts and educational resources for users and lifeguards. Two experiments evaluated the system’s accuracy in detecting distress signals and motion events, showing high precision and reliability under controlled conditions. Compared to existing methodologies, SaveSplash offers a more scalable, responsive, and comfortable solution without relying on vision systems or invasive biometric tracking. It presents a promising approach to reducing water-related accidents through accessible, adaptive, and real-time monitoring technology.