AI Video Analysis for Personalized Behavioral Skills Scoring
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Solution Overview
Problem
Existing technologies fail to provide personalized and actionable insights for improving behavioral skills, as they focus on general feedback and lack the use of artificial intelligence to diagnose specific gaps and facilitate targeted practice.
Innovation Solution
A system utilizing machine learning models to analyze communication attributes, non-verbal cues, and sentiments from user content, such as selfie videos, to identify and score behavioral skills, providing data-driven insights and recommendations for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard material and general feedback are used for behavioral skills development, then accessibility and availability are improved, but personalization and actionable insights deteriorate
Solution Approach 1:
The system creates virtual copies of coaching interactions through AI-powered video analysis, where machine learning models simulate the diagnostic capabilities of human coaches by analyzing communication attributes, non-verbal cues, and sentiments in user-submitted videos, making personalized feedback scalable and accessible to all users
Solution Approach 2:
The system transforms qualitative behavioral observations into quantitative parameters by measuring specific communication attributes (speech rate, pauses, volume), non-verbal cues (smile duration, eye contact frequency), and sentiment scores, enabling precise personalization of feedback while maintaining scalability through automated analysis
2Measurement precision
If 1:1 personal coaching sessions are used, then personalization and actionable insights are improved, but scalability and productivity deteriorate
Solution Approach 1:
The system enables users to self-upload videos and receive automated personalized feedback through AI analysis, eliminating the need for continuous human coach involvement while maintaining high personalization levels through machine learning models that analyze individual user patterns and provide targeted improvement recommendations
Solution Approach 2:
The system replaces the mechanical system of human coach-time with automated machine learning analysis, where algorithms process video content to identify communication attributes, non-verbal cues, and sentiments, substituting human diagnostic capability with scalable computational analysis that can serve unlimited users simultaneously
3Measurement precision
If communication attributes and non-verbal cues are analyzed, then measurement precision is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The system segments the complex task of behavioral analysis into distinct components: communication attributes analysis (speech rate, pauses, volume), non-verbal cues analysis (smile gestures, eye contact), and sentiment analysis, with separate machine learning models handling each segment, reducing overall system complexity while maintaining comprehensive measurement precision
Data Source
AI summary
Disclosed herein is a method of facilitating improving behavioral skills of users, in accordance with some embodiments. Accordingly, the method comprises receiving, using a communication device, a request from a user device. Further, the method comprises obtaining, using a processing device, a content based on the request. Further, the method comprises analyzing, using the processing device, the content using machine learning models. Further, the machine learning models are configured for determining the communication attributes. Further, the method comprises identifying, using the processing device, a behavioral skill from behavioral skills based on the determining. Further, the method comprises generating, using the processing device, a score corresponding to each of the behavioral skill based on the determining of the communication attributes and the identifying. Further, the method comprises transmitting, using the communication device, the score to the device. Further, the method comprises storing, using a storage device, the score.