Multimodal Sensor Human Behavior Recognition with Contextual Inference
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Solution Overview
Problem
Conventional behavior recognition technologies in robots rely on one-off recognition using current data, failing to consider context and environment changes, leading to instability and low reliability, especially in dynamic home environments.
Innovation Solution
A human behavior recognition apparatus and method that utilizes a multimodal sensor unit to generate image, sound, and IoT information, extracting contextual information to infer behavior intentions and update behavior patterns, improving recognition accuracy and reliability by considering past and subsequent actions and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional one-off recognition technology is used, then the system is simple and fast, but the recognition reliability deteriorates in dynamic environments
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing contextual information (environmental data, historical behavior patterns, sensor data) before recognition is needed. This pre-prepared contextual foundation enables more reliable recognition decisions when actions are evaluated, without requiring complex real-time processing during the actual recognition moment.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring recognition results and using them to update behavior patterns and contextual information. Recognition outcomes feed back into the system to refine future predictions, creating a closed-loop that improves reliability over time while managing complexity through iterative learning rather than static complex rules.
2Measurement precision
If contextual information and behavior patterns are continuously updated, then recognition accuracy improves, but information processing time increases
Solution Approach 1:
Contextual information and behavior patterns are updated and stored in advance during periods when processing is not critical. Historical data, environmental contexts, and behavior patterns are pre-processed and archived so that during actual recognition events, the system can quickly retrieve and compare against pre-prepared reference data rather than processing everything in real-time.
Solution Approach 2:
The system applies different processing qualities to different types of information. Frequently accessed behavior patterns and critical contextual factors receive higher processing priority and more detailed analysis, while less critical data is processed at lower fidelity. This selective processing maintains accuracy for key recognition tasks while reducing overall processing time through differentiated quality levels.
Data Source
AI summary
Disclosed herein are a human behavior recognition apparatus and method. The human behavior recognition apparatus includes a multimodal sensor unit for generating at least one of image information, sound information, location information, and Internet-of-Things (IoT) information of a person using a multimodal sensor, a contextual information extraction unit for extracting contextual information for recognizing actions of the person from the at least one piece of generated information, a human behavior recognition unit for generating behavior recognition information by recognizing the actions of the person using the contextual information and recognizing a final action of the person using the behavior recognition information and behavior intention information, and a behavior intention inference unit for generating the behavior intention information based on context of action occurrence related to each of the actions of the person included in the behavior recognition information.