KD-Judge: A Knowledge-Driven Automated Judge Framework for Functional Fitness Movements on Edge Devices

2026-04-21

AI Summary

This work addresses the long-standing reliance on subjective human judgment in evaluating repetition counts for functional fitness exercises, which lacks transparency, interpretability, and rule compliance. To overcome this limitation, the study proposes the first explicit rule-driven automated assessment framework. It leverages large language models with chain-of-thought prompting to translate unstructured rulebooks into machine-executable logic, integrates pose-guided kinematic reasoning for deterministic adjudication, and incorporates retrieval-augmented generation alongside a dual-strategy caching mechanism to enhance computational efficiency. Evaluated on the CFRep dataset, the system achieves accurate repetition-level assessment. With caching enabled, it attains speedups of up to 3.36× (on-demand playback) and 15.91× (live streaming) on a Jetson AGX Xavier platform, achieving super-real-time performance (RTF < 1) and enabling efficient, transparent, and scalable deployment on edge devices.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Computer Vision: Motion & Tracking

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Functional fitness movements are widely used in training, competition, and health-oriented exercise programs, yet consistently enforcing repetition (rep) standards remains challenging due to subjective human judgment, time constraints, and evolving rules. Existing AI-based approaches mainly rely on learned scoring or reference-based comparisons and lack explicit rule-based, limiting transparency and deterministic rep-level validation. To address these limitations, we propose KD-Judge, a novel knowledge-driven automated judging framework for functional fitness movements. It converts unstructured rulebook standards into executable, machine-readable representations using an LLM-based retrieval-augmented generation and chain-of-thought rule-structuring pipeline. The structured rules are then incorporated by a deterministic rule-based judging system with pose-guided kinematic reasoning to assess rep validity and temporal boundaries. To improve efficiency on edge devices, including a high-performance desktop and the resource-constrained Jetson AGX Xavier, we introduce a dual strategy caching mechanism that can be selectively applied to reduce redundant and unnecessary computation. Experiments demonstrate reliable rule-structuring performance and accurate rep-level assessment, with judgment evaluation conducted on the CFRep dataset, achieving faster-than-real-time execution (real-time factor (RTF) < 1). When the proposed caching strategy is enabled, the system achieves up to 3.36x and 15.91x speedups on resource-constrained edge device compared to the non-caching baseline for pre-recorded and live-streaming scenarios, respectively. These results show that KD-Judge enables transparent, efficient, and scalable rule-grounded rep-level analysis that can complement human judging in practice.

Problem

• functional fitness
• automated judging
• rule-based validation
• repetition standard
• edge devices

Innovation

• knowledge-driven judging
• rule structuring
• pose-guided kinematic reasoning
• edge computing
• caching mechanism