User-Detection Display System for Real-Time Behavioral Feedback
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Solution Overview
Problem
Existing systems lack adequate tools for objectively measuring the severity and progress of developmental disorders such as Autism Spectrum Disorder (ASD) in young patients, particularly toddlers, and often provide poor sensitivity and specificity in treatment assessment and feedback.
Innovation Solution
A system comprising portable devices with eye-trackers, cameras, and sensors that collect multi-modal data, connected to a network server for real-time analysis and automated feedback, providing immediate reinforcement and guidance through interactive visual scenes using VR, AR, or MR, tailored to the patient's behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional assessment tools are used by treatment providers, then the assessment process is simple to implement, but the sensitivity and specificity to detect developmental disorders are poor
Solution Approach 1:
The system segments the assessment into multiple modalities (eye-tracking, facial expressions, verbal expressions, physical movements, biometric data) collected by separate sensors, allowing each modality to contribute independently to the overall detection precision while maintaining manageable system architecture
Solution Approach 2:
The portable computing device serves multiple functions: displaying visual stimuli, collecting multi-modal behavioral data through integrated sensors, processing data locally, and communicating with the server, thereby consolidating what would otherwise require multiple separate devices into one universal platform
2Measurement precision
If comprehensive multi-modal data collection is implemented, then the objective measurement capability is improved, but the device complexity and burden on patients increase
Solution Approach 1:
Multiple sensing functions (eye-tracking, facial expression analysis, motion detection, biometric monitoring) are merged into a single portable computing device with integrated sensors, reducing the number of separate devices the patient must handle while maintaining comprehensive data collection
Solution Approach 2:
The system automatically collects and processes multi-modal data without requiring active participation or operation by the patient, and the portable design allows operators to easily position and adjust the device, reducing operational burden
3Productivity
If real-time automated feedback is provided, then the treatment efficacy is enhanced, but the computational requirements and system complexity increase
Solution Approach 1:
The system implements real-time feedback by continuously analyzing behavioral data, comparing it against developmental milestones, and automatically providing corrective feedback and adjusting visual stimuli presentation, creating a closed-loop system that adapts to patient responses
Solution Approach 2:
A network-connected server acts as an intermediary between the portable computing device and the complex computational algorithms, handling heavy data processing, machine learning analysis, and treatment plan generation remotely, thereby reducing the computational burden on the portable device itself
4Loss of time
If early detection in toddlers is performed, then the treatment timing is optimized, but the measurement precision requirements are higher due to subtle behaviors
Solution Approach 1:
The system transitions from traditional single-modality assessment to multi-dimensional measurement by incorporating eye-tracking coordinates, facial expression analysis, motion patterns, and biometric data, enabling detection of subtle toddler behaviors through multiple complementary dimensions simultaneously
Data Source
AI summary
Embodiments described herein include portable devices having displays and user-detection equipment (such as eye-tracker devices, cameras, or other sensors) and computer systems including such portable devices for displaying interactive visual scenes to users, such user-detection equipment for collecting detection data (such as eye-tracking data and/or other multi-modal data such as facial expressions, verbal expression, and/or physical movements), and network-connected servers for processing the detection data to determine real-time user behavior for interactions with the users for immediate feedback and reinforcement.