Gamified Video Annotation System for Operator Engagement
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Solution Overview
Problem
Operators and subjects involved in product testing, particularly in skincare product testing, often experience fatigue and decreased engagement during video annotation processes, leading to reduced quality and quantity of annotated data, which negatively impacts AI/ML model training and product testing outcomes.
Innovation Solution
The implementation of gamified feedback mechanisms in video annotation systems, where operators and subjects receive interactive and engaging feedback, such as visual, audio, or audiovisual cues, to maintain attention and motivation during the annotation process, using computational devices to depict videos, receive inputs, and provide feedback based on gesture annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video annotation processes are used, then annotation data can be collected, but operator and subject engagement decreases over time leading to reduced data quality and increased fatigue
Solution Approach 1:
The system implements real-time feedback mechanisms where operators receive immediate responses about their annotation accuracy and progress. The computational device analyzes operator inputs and provides feedback signals that guide further annotation actions, creating a closed-loop system that maintains engagement and improves data quality over time.
Solution Approach 2:
The annotation interface dynamically adapts to operator performance and engagement levels. The system adjusts task difficulty, provides contextual hints, and modifies presentation formats based on real-time operator state, transforming a static annotation process into a dynamic interactive experience that sustains engagement.
2Measurement precision
If manual annotation processes are used, then detailed gesture data can be captured, but the quantity of annotated data decreases due to operator fatigue
Solution Approach 1:
The system introduces computational algorithms as intermediaries that assist operators in the annotation process. These algorithms pre-process video data, suggest annotations, and validate operator inputs, allowing operators to maintain high precision while processing larger volumes of data without excessive fatigue.
Solution Approach 2:
The annotation task is segmented into smaller, manageable units with automatic progression. The system divides complex gesture sequences into discrete annotatable events, allowing operators to focus on specific precision-critical moments while the system handles routine processing, thereby increasing overall throughput without sacrificing precision.
3Quantity of substance
If repetitive annotation tasks are assigned, then comprehensive dataset coverage is achieved, but operator motivation and engagement decrease
Solution Approach 1:
The system implements periodic variation in annotation tasks, alternating between different types of gestures, video segments, and annotation challenges. This periodic restructuring prevents monotony while ensuring comprehensive dataset coverage, as operators encounter diverse tasks that maintain interest throughout the annotation session.
Solution Approach 2:
The system dynamically changes task parameters such as video playback speed, annotation detail level, and feedback frequency based on operator performance and engagement metrics. These parameter adjustments transform repetitive tasks into adaptive challenges that maintain operator motivation while systematically building the annotated dataset.
Data Source
AI summary
Devices, systems, and methods for gamifying the process of annotating videos for maintaining engagement in the annotation process and increasing the quality and quantity of an annotated data set. Methods for gamifying annotation of gestures in a video comprise determining gamified feedback based on an input and presenting the gamified feedback to an operator to keep the operator engaged in the annotation process. Methods for gamifying annotation of self-perception gestures in a video by a subject are able to be performed by the subject without the requirement of an operator, in which case the method captures an aspect of the subject's experience and the gamification maintains or increases the subject's engagement with the annotation process. Annotated data sets are used to train artificial intelligence and machine learning systems for automated detection and characterization of subjects' gestures and self-perception gestures.