Assisting Aging Adults With Memory Issues: An AI-Driven Video Monitoring Based Approach

Memory challenges among the elderly pose significant risks to safety and independent living. There is a growing need for innovative solutions to support daily living and provide real-time assistance. This paper introduces techniques devised to address these challenges by using real-time monitoring and action-based video filtering with a focus on evaluating the system’s scalability, processing efficiency, and detection accuracy through a set of real-world video datasets and experiments based on videos of safety-critical use cases. By integrating a scalable distributed architecture with advanced technologies such as Apache Spark and Apache Kafka, the system achieves efficient near real-time analysis of video streams. Event-based filtering offers meaningful insights to aid independent living, supporting caregivers and healthcare professionals.