Multi-Sensor Panel Data Extraction via Temporal Synchronization
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Solution Overview
Problem
Existing systems face challenges in effectively extracting and synchronizing data from multiple sensors, such as video cameras, audio microphones, and behavioral sensors, to identify specific events and evaluate participants in panel discussions, particularly in distinguishing between multiple individuals and capturing relevant behavioral data.
Innovation Solution
A system comprising video cameras, audio microphones, and behavioral sensors that synchronize recorded data to identify specific participants, extract biometric behavioral data, and store it in participant profiles, using depth sensors to measure distance and sense electromagnetic waves, enabling the evaluation of participants in panel discussions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple sensors (video cameras, audio microphones, behavioral sensors) are used to capture panel discussion data, then the quantity and quality of behavioral data is improved, but the device complexity and data synchronization difficulty increase
Solution Approach 1:
The system segments the multi-sensor data processing into distinct modules: video cameras capture visual data, audio microphones capture speech data, and behavioral sensors capture biometric data. Each sensor type processes its data independently before integration, reducing the complexity of handling all data simultaneously while maintaining comprehensive data collection.
Solution Approach 2:
The patent introduces a data synchronization and integration system that acts as an intermediary between multiple sensors. This intermediary component receives data streams from various sensors, synchronizes them temporally and spatially, and integrates them into a unified participant profile, thereby managing the complexity introduced by multiple sensors.
2Measurement precision
If depth sensors are used to measure distance and sense electromagnetic waves, then the measurement precision of behavioral data is improved, but the device complexity increases
Solution Approach 1:
The depth sensors in the system serve multiple functions: they measure distance from participants, detect electromagnetic waves for behavioral analysis, and provide spatial orientation data. By making these sensors multi-functional, the system achieves high measurement precision without proportionally increasing device complexity, as the same hardware performs multiple measurement tasks.
3Reliability
If data from multiple sensors is synchronized and analyzed to identify specific participants, then the reliability of participant identification is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data from multiple sensors as it is collected, rather than waiting until all data is gathered. Participant profiles are continuously updated with synchronized data streams, allowing for real-time identification and reducing the total processing time while maintaining high reliability through continuous verification.
4Productivity
If biometric behavioral data is extracted and stored in participant profiles, then the productivity of participant evaluation is improved, but the loss of information privacy increases
Solution Approach 1:
The system applies local quality by storing different types of biometric behavioral data in separate, organized sections within participant profiles. Sensitive information is segregated from general behavioral data, allowing for selective access and processing. This structured organization enables efficient evaluation productivity while maintaining privacy through controlled information exposure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system efficiently extracts and synchronizes data to evaluate participant behavior, providing insights into suitability for jobs, teamwork potential, and engagement, by analyzing video, audio, and behavioral data, enhancing the assessment of candidates and team dynamics.
Implementation Method 1
The behavioral data sensors can sense electromagnetic waves in the non-visible electromagnetic spectrum
Data Source
AI summary
A system and method are presented for combining visual recordings from a camera, audio recordings from a microphone, and behavioral data recordings from behavioral sensors, during a panel discussion. Cameras and other sensors can be assigned to specific individuals or can be used to create recordings from multiple individuals simultaneously. Separate recordings are combined and time synchronized, and portions of the synchronized data are then associated with the specific individuals in the panel discussion. Interactions between participants are determined by examining the individually assigned portions of the time synchronized recordings. Events are identified in the interactions and then recorded as separate event data associated with individuals.