Team Engagement Assessment via Multimodal Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Geographically separated teams face challenges in assessing each other's level of engagement and attention due to the lack of a common shared point of reference for gaze and facial expressions, making it difficult to determine individual and collective engagement during tasks.
Innovation Solution
A team monitoring system that utilizes data from audio/video sensors and physiological sensors to determine user engagement based on arm/hand positions, gaze, pupil dynamics, and voice intonation, weighting individual contributions according to task priority and correlating data to assess team engagement, even when team members are not fully engaged collectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geographically separated teams use traditional monitoring methods, then individual engagement can be assessed locally, but collective engagement assessment becomes impossible due to lack of common reference frame
Solution Approach 1:
The patent combines multiple engagement indicators (gaze direction, facial expressions, speech patterns, physiological signals) from multiple team members into a unified engagement assessment framework. This merging of diverse data sources enables accurate collective engagement measurement while accounting for the geographically distributed nature of the team through synchronized temporal and contextual reference frames.
Solution Approach 2:
The patent introduces temporal synchronization and contextual mapping as additional dimensions to resolve the spatial separation issue. By aligning engagement data across different locations in time and task context, the system creates a common reference frame that enables collective engagement assessment without requiring physical proximity or shared visual fields.
2Measurement precision
If multiple engagement indicators are collected from each team member, then engagement assessment accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the complex engagement assessment task into distinct modular components: gaze tracking module, facial expression analysis module, speech pattern recognition module, and physiological signal processing module. Each module processes specific indicators independently and outputs standardized engagement metrics that can be integrated at a higher level, reducing overall system complexity while maintaining comprehensive assessment accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A team monitoring system receives data for determining user engagement for each team member. A team engagement metric is determined for the entire team based on individual user engagement correlated to discreet portions of a task. User engagement may be determined based on arm / hand positions, gaze and pupil dynamics, and voice intonation. Individual user engagement is weighted according to a task priority for that individual user at the time. The system determines a team composition based on individual user engagement during a task and team engagement during the task; even where the users have not engaged as a team during the task.