Interaction Visualization Distinguishing Direct and Indirect Contacts
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Solution Overview
Problem
Conventional methods for reconstructing the prior movements and interactions of individuals, such as in a hospital setting, are incomplete, time-consuming, and resource-intensive, as they fail to accurately track and visualize direct and indirect interactions with other individuals and objects, especially due to limitations in documentation and video recording.
Innovation Solution
A method and system that receive interaction data from various tracking methodologies, correlate location data to identify direct and indirect interactions, and generate an interaction visualization using a node-and-edge graph to distinguish between these interactions, allowing for comprehensive analysis and dynamic action based on selected parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional methods (documentation and video recording) are used to track interactions, then some interaction information can be obtained, but the tracking is incomplete and time-consuming
Solution Approach 1:
The patent replaces manual documentation and video recording analysis with an automated electronic tracking system that uses sensors, RFID tags, Bluetooth beacons, and computer vision algorithms to automatically detect and record interactions. This substitution of mechanical/manual methods with electronic automation eliminates the need for time-consuming manual review while capturing complete interaction data.
Solution Approach 2:
The patent introduces intermediary tracking devices such as RFID tags, Bluetooth beacons, and wearable sensors that mediate between the individuals/objects being tracked and the central processing system. These intermediaries automatically transmit location and interaction data, enabling comprehensive tracking without requiring direct observation or manual documentation.
2Loss of information
If comprehensive interaction tracking is implemented, then complete interaction data is obtained, but the system complexity increases
Solution Approach 1:
The patent divides the tracking system into separate modular components: wearable tags on individuals, sensors on objects, Bluetooth beacons in the environment, video cameras for visual verification, and a central processing system. Each component performs a specific function, and they communicate through standardized protocols, reducing overall system complexity while maintaining comprehensive tracking capability.
Solution Approach 2:
The patent designs the tracking system with universal components that can track multiple types of entities (people, objects, vehicles) using the same infrastructure of sensors, tags, and processing algorithms. The system can adapt to different interaction types and environments without requiring completely separate systems, thereby managing complexity.
3Measurement precision
If direct and indirect interactions are distinguished with high precision, then accurate interaction classification is achieved, but the measurement difficulty increases
Solution Approach 1:
The patent uses multiple measurable parameters to distinguish direct from indirect interactions: spatial proximity (distance between entities), temporal duration (length of interaction), interaction type (physical contact vs. proximity), and environmental context. By measuring and analyzing these parameters simultaneously, the system achieves accurate classification without overly complex detection methods.
Solution Approach 2:
The system continuously monitors interaction parameters and provides feedback to refine classification. Video verification and sensor data are cross-checked and adjusted based on observed patterns, allowing the system to improve its distinction between direct and indirect interactions over time while managing detection complexity through iterative optimization.
Data Source
AI summary
One embodiment provides a method, including: receiving a selection to access interaction data associated with a target; receiving a designation of an interaction parameter, wherein the designation of the interaction parameter adjusts a defining standard for a direct interaction and an indirect interaction for the interaction data; determining, using a processor and based on the interaction parameter, whether each of the interactions from the interaction data is associated with a direct interaction or an indirect interaction; and generating an interaction visualization from the interactions, wherein the interaction visualization indicates interactions of the target with the at least one entity and wherein the interaction visualization visually distinguishes direct interactions from indirect interactions. Other aspects are described and claimed.