Modular Event-Driven Architecture for Person-Object Interaction Monitoring
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Solution Overview
Problem
Existing automation systems for people-centric environments struggle to effectively monitor and detect person-object interactions, particularly in detecting anomalies that require remedial action, and lack scalability and adaptability across various applications.
Innovation Solution
A modular, scalable, and decentralized system and software architecture that tracks person-object interactions by receiving event data and storage content data to determine added or removed objects from storage entities, enabling robust monitoring and quick anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a modular, scalable, and decentralized system architecture is implemented, then adaptability and versatility across various applications are improved, but device complexity increases
Solution Approach 1:
The system is divided into independent modular components including event sources, event processors, and anomaly detectors that can be selectively deployed and configured for different applications. Each module performs a specific function and can be scaled independently, enabling the system to adapt to various people-centric environments without requiring complete system redesign.
2Measurement precision
If continuous monitoring of person-object interactions is implemented, then detection precision of anomalies is improved, but use of energy and computational resources increases
Solution Approach 1:
The system pre-processes event data by filtering and categorizing interactions before detailed analysis. Event sources continuously monitor interactions and pre-identify potential anomalies based on basic criteria, allowing the system to maintain high detection precision while reducing the computational burden on downstream processing components.
Solution Approach 2:
The system applies different levels of monitoring intensity to different interaction types. High-priority interactions requiring anomaly detection receive full processing resources, while routine interactions use minimal processing. This selective approach maintains detection precision for critical events while reducing overall energy consumption.
3Reliability
If robust monitoring of person-object interactions is implemented, then reliability of anomaly detection is improved, but device complexity and system resource requirements increase
Solution Approach 1:
The system implements feedback loops where anomaly detection results are fed back to event sources and processors. Detected anomalies trigger additional verification steps and cross-checking mechanisms that improve reliability. The feedback mechanism allows the system to adapt its monitoring intensity based on detected patterns, maintaining high reliability without requiring permanently high system complexity.
Data Source
AI summary
A method, processing system, and tracking system for monitoring person-object interactions in an environment is disclosed. In particular, software architecture is provided for processing tracking and event information provided by independent trackers to identify basic interactions between the people in the environment and objects or storage entities in the environment. Based on the identified person-object interactions, the software architecture can associate individual persons with object and storage entities, detect and infer outcomes of their basic interactions, infer higher-level interactions, and detect any anomalous interactions. The software architecture is advantageously highly modular, scalable, and decentralized, and is designed to be substantially domain agnostic, such that it can be used to automate a wide variety of human-centric applications that involve humans interacting with objects.