Key result
AI-based wearable SafeBand improves fall and anomaly detection reliability vs conventional approaches while minimizing false alarms.
Why the study?
There is an increasing need for reliable and continuous health monitoring solutions for elderly individuals, patients, and other vulnerable populations.
An AI-based wearable device integrating physiological monitoring and fall detection demonstrates improved reliability and fewer false alarms compared to conventional threshold-based systems.
Answers are grounded in this paper and cite their sources.
May enhance safety monitoring in at-risk cardiac patients; extends evidence for multimodal AI wearables beyond conventional systems.
The increasing need for reliable and continuous health monitoring solutions for elderly individuals, patients, and other vulnerable populations has driven the development of intelligent wearable technologies. This study presents the design and implementation of an AI Based SafeBand, a compact and affordable wearable device aimed at enhancing personal safety, health monitoring, and independent living. The primary purpose of the study is to develop a smart wearable system capable of real-time physiological monitoring, accurate fall detection, and timely emergency alert generation using artificial intelligence. The proposed system is built on an Arduino Nano platform and integrates essential sensors, including the MAX30102 sensor for heart rate and blood oxygen saturation (SpO₂) monitoring and the MPU6050 sensor for motion tracking and fall detection. Sensor data are continuously collected and processed using an AI-based anomaly detection model, which enables the system to identify abnormal physiological patterns, sudden falls, or unusual movements with greater accuracy compared to conventional threshold-based approaches. Additionally, a manual SOS button allows users to instantly request help during emergencies. In the event of detected anomalies or manual activation, the SIM800L GSM module transmits SMS alerts containing critical information to predefined emergency contacts, ensuring rapid caregiver or family response. A built-in buzzer provides immediate audio feedback to confirm alert activation and system responsiveness. Experimental observations demonstrate that the AI-driven approach significantly improves detection reliability while minimizing false alarms. In conclusion, the AI Based SafeBand effectively combines artificial intelligence, IoT communication, embedded systems, and wearable technology to deliver a proactive and dependable safety solution. The system reduces emergency response time, supports remote caregiving, and promotes independence among elderly users, making it a practical and scalable solution for modern healthcare monitoring applications.
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Asim et al. (2026) studied this question. An AI-based wearable SafeBand integrating heart rate, SpO2, and motion sensors significantly improved fall and anomaly detection reliability while minimizing false alarms for vulnerable populations.
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