Presentation Video Gesture Analysis Linked to Speech Content
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Solution Overview
Problem
Existing systems struggle to efficiently analyze and explore the correlation between gestures and speech content in presentation videos, as they are often tedious and time-consuming, and focus only on limited types of gestures without considering the dynamic nature and complex correlation to speech content.
Innovation Solution
A visual analytics system with four coordinated views (exploration, relation, video, and dynamic views) to facilitate gesture-based and content-based exploration, using heatmaps, timelines, and human stick-figure glyphs to analyze and visualize the spatial and temporal distributions of gestures in relation to speech content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking and exploration of gesture usage in presentation videos is performed, then detailed analysis of gestures can be obtained, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis of gestures with an automated computer vision system that uses machine learning algorithms to detect, track, and classify gestures in presentation videos. The system automatically identifies hand movements, categorizes them into gesture types, and correlates them with speech content, eliminating the need for tedious manual frame-by-frame analysis while maintaining high measurement precision.
Solution Approach 2:
The patent introduces an intermediate automated analysis layer between the raw video data and the final gesture insights. This intermediary system processes videos by extracting visual features, detecting key points, tracking movements, and generating structured gesture annotations that correlate with audio transcripts, thereby bridging the gap between raw data and actionable insights without requiring manual intervention.
2Productivity
If existing gesture recognition tools are used, then automatic gesture recognition can be achieved, but they focus only on limited types of gestures without regard to verbal content
Solution Approach 1:
The patent creates a universal gesture analysis system that handles multiple gesture types (pointing, open hand, closed hand, hand-to-hand, hand-to-face) and integrates them with verbal content analysis. The system is designed to be multi-functional, accommodating various gesture categories while simultaneously correlating them with speech transcripts, thereby achieving both high automation and broad adaptability to different gesture types and contexts.
Solution Approach 2:
The patent implements a dynamic gesture recognition system that adapts to different gesture types and contexts rather than using a static, limited classification. The system dynamically adjusts its analysis based on the detected gesture category, temporal patterns, and correlation with verbal content, allowing it to handle diverse gesture types effectively while maintaining automated processing.
3Loss of information
If coaches analyze good presentation videos to provide examples for improvement, then practical gesture guidance can be obtained, but the process is time-consuming and lacks systematic analysis
Solution Approach 1:
The patent performs preliminary automated analysis of presentation videos to pre-extract gesture patterns, temporal distributions, and correlations with speech content before coaches need to review them. The system generates structured reports highlighting effective gesture usage, repetitive patterns, and alignment with verbal content, allowing coaches to quickly review pre-processed insights rather than manually analyzing entire videos from scratch.
Solution Approach 2:
The patent creates simplified copies or representations of complex gesture patterns by generating visual summaries, heatmaps, and temporal visualizations that capture essential gesture characteristics. These copied representations preserve the key information about gesture usage while reducing the complexity of original video data, enabling coaches to efficiently review and provide guidance based on condensed visual evidence.
Data Source
AI summary
The invention relates to a computer implemented method and system for analyzing a video. The method comprises the steps of receiving, via a receiving module, a video data comprising a series of images showing a subject; extracting, via an extracting module, a transcript derived from an audio data associated with the video data; aligning, via an aligning module, the series of images of the video data with the transcript derived from the audio data associated with the video data based on timestamps derived from the video data; analyzing, via an analyzing module, gestures of the subject from the series of images, comprising the steps of: identifying a plurality of reference points from each of the series of images showing the subject; segmenting the series of images in accordance with one or more selected texts comprising the transcript; identifying a defined gesture type for each of the segmented images based on the identified plurality of reference points; and processing, via a processing module, the defined gesture types in correlation with respective one or more selected texts comprising the transcript.