Document Type

Article

Publication Date

2026

DOI

10.3390/biomechanics6010023

Publication Title

Biomechanics

Volume

6

Issue

1

Pages

23

Abstract

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, recovery timelines, and athlete metadata. The VLM outputs were aggregated using a consensus-based strategy and further refined by a specialized reasoning LLM to ensure coherent, transparent, and explainable diagnostic assessments. Model fine-tuning employed Low-Rank Adaptation (LoRA) and 4-bit quantization to enable efficient deployment on consumer-grade hardware. Results: Experimental evaluation demonstrated that the proposed hybrid system delivers accurate, consistent, and clinically interpretable assessments of neuromuscular states, including fatigue, injury, and recovery, directly from EMG waveform images and contextual metadata. Compared with baseline models, the fine-tuned VLM consortium exhibited substantially improved precision, consistency, and contextual awareness, while the reasoning LLM enhanced diagnostic coherence through cross-model consensus and structured reasoning, thereby supporting responsible and explainable AI-driven decision making. Conclusions: This work presents, to the authors’ knowledge, the first integration of a responsible and explainable AI-driven decision support system for H-reflex analysis. The proposed framework advances the automation and standardization of neuromuscular diagnostics and establishes a foundation for next-generation AI-assisted decision support systems in sports performance monitoring, rehabilitation, and clinical neurophysiology.

Rights

© 2026 by the Authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The raw data supporting the conclusions of this article will be made available by the authors on request."

Original Publication Citation

Bandara, E., Gore, R., Shetty, S., Mukkamala, R., Rhea, C. K., Samulski, B. S., Hass, A., Yarlagadda, A., Kaushik, S., De Silva, M., Maznychenko, A., Sokolowska, I., & De Zoysa, K. (2026). Standardization of neuromuscular reflex analysis—Role of fine-tuned vision-language model consortium and OpenAI gpt-oss reasoning LLM-enabled decision support system. Biomechanics, 6(1), Article 23. https://doi.org/10.3390/biomechanics6010023

ORCID

0000-0003-3214-5346 (Bandara), 0000-0003-4065-6146 (Gore)

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