Hierarchical NeuroSymbolic Approach for Action Quality Assessment at Princeton University

Core Concepts

Introducing a neuro-symbolic paradigm for action quality assessment in diving, combining neural networks and rules-based AI to provide transparent and objective scoring.

Abstract

Introduction

Analyzing human actions from physical rehabilitation to sports coaching.
Current AQA approaches are biased due to subjective human judgments.

Existing Neural Approaches

End-to-end neural models lack transparency and objectivity.
Bias in judging affects the overall score of athletes' performance.

Neuro-Symbolic Approach

Combines neural networks with rules-based AI for fine-grained action analysis.
Deconstructs performance into interpretable symbols for detailed assessment.

Results

NS system outperforms SOTA models in action recognition and temporal segmentation.
Expert survey validates the accuracy and usefulness of the NS-AQA system.

Discussion

Extension of NS approach to other sports and skills assessment.

References

Stats

"Our system achieves state-of-the-art action recognition."
"Generated scores compare a diver’s performance relative to their peers."
"Experts chose our system 96.1% of the time over fully neural models."

Quotes

"Our NS-AQA approach employs neural networks to extract crucial symbolic information, furnishing objective data utilized for detailed error analysis."
"Our system programmatically generates detailed reports that empower athletes & coaches with actionable insights."

Key Insights Distilled From

Hierarchical NeuroSymbolic Approach for Action Quality Assessment

by Lauren Okamo... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13798.pdf

Deeper Inquiries

How can the neuro-symbolic approach be applied beyond diving?

The neuro-symbolic approach utilized in action quality assessment (AQA) for diving can be extended to various other domains and applications. For instance, this methodology can be adapted for assessing skills in sports like gymnastics, figure skating, or even complex actions such as surgical procedures. By abstracting symbols from video data using neural networks and applying rules-based analysis, the system can provide detailed and objective assessments across a range of activities. Domain expertise is crucial in determining which symbols to abstract and creating suitable rules for analysis.

What are the potential implications of reducing bias in judging through AI systems?

Reducing bias in judging through AI systems has significant implications across various fields. In sports, it ensures fair scoring based on objective criteria rather than subjective human judgments that may be influenced by personal biases. This leads to more transparent and trustworthy evaluations that reward athletes based on their actual performance rather than external factors. Moreover, minimizing bias enhances the credibility of judging processes, promotes fairness among competitors, and ultimately improves the overall integrity of competitions.

How might advancements in pose estimation technology impact the accuracy of such systems?

Advancements in pose estimation technology play a critical role in enhancing the accuracy of AQA systems that rely on analyzing human movements. Improved pose estimation algorithms enable better tracking and recognition of key body positions during actions like dives or athletic performances. With higher precision in capturing poses from video frames, these systems can provide more detailed insights into an individual's performance quality by accurately assessing elements such as form, alignment, or execution technique. As pose estimation technology evolves further, it will likely lead to even more precise and reliable assessments within AQA frameworks.