Stream Reasoning approach on Human Behavior for Medication Risks Detection using LARS Framework

Human activity detection and analysis is essential in many areas, including healthcare, smart settings, and surveillance. Traditional techniques fail to efficiently evaluate streaming data, as the need for real-Time analysis rises. In this study, we introduce a novel approach to identify potential medical risks by examining the behaviors of an elderly individual. Our approach analyzes human activities using rule-based common sense reasoning over data streams. We utilize LARS (Logic-Based Framework for Analyzing Reasoning over Streams), a system that permits the use of specific time operators, to perform the reasoning. This enables us to more effectively handle and reason about real-Time data. It also provides incremental processing of streaming data, which improves results in terms of risk detection. We evaluate the effectiveness of our approach by doing comprehensive testing on synthetic data, which acts as a simulation of real-Time recorded human actions, to assess the efficacy of our reasoning model. Our suggested approach provides real-Time monitoring of human behavior, demonstrating its usefulness and need and giving an invaluable option for the precise detection of potential medical dangers among elderly people.