Georgios Bouchouras | Searcharxiv

arXiv2026

Vertical Ground Reaction Forces Waveform Flattening during Gait in Women with Knee Osteoarthritis

Background. Knee Osteoarthritis (OA) is a common chronic joint condition, and its prevalence increases with age. This study aims to examine whether flattened vertical ground reaction force (vGRF) waveforms and reduced knee range of motion (RoM) occur together during gait as compensatory strategies to maintain gait speed. Methods. Twelve women with knee OA and twelve healthy women of the same age completed the Western Ontario and McMaster University Index (WOMAC) to assess self-reported pain, stiffness, and function. The groups were divided into two groups: OA vs. control 2 limbs or left and right in the Control group. A mixed-design ANOVA was used to examine differences in vertical ground response forces (VGRFs) peaks, minimum VGRF, anterior-posterior weight acceptance (ADWA) and propulsive force (ADPO), knee RoM, and gait speeds. Results. In the OA group, the mean Peak 1 vGFR was 1.109 (SD = 0.05) for the right leg (p 0.05), while the mean min vGFFR was 0.87 (SD=0.04) for the left leg. The OA leg exhibited a mean ADWA of 0.23 0.04 kg/BW, which was significantly lower than the control group's right leg (0.28 0.09 kg/bw, p0.05). No group differences in gait velocity were detected. Conclusions. We interpret the flattening of the vFGFR waveform and the reduction in knee RoM as components of an adaptive, yet potentially maladaptive, motor strategy

q-bio.TO↗

arXiv2025

Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting

This paper explores the integration of Large Language Models (LLMs) in the engineering of a Parkinson's Disease (PD) monitoring and alerting ontology through four key methodologies: One Shot (OS) prompt techniques, Chain of Thought (CoT) prompts, X-HCOME, and SimX-HCOME+. The primary objective is to determine whether LLMs alone can create comprehensive ontologies and, if not, whether human-LLM collaboration can achieve this goal. Consequently, the paper assesses the effectiveness of LLMs in automated ontology development and the enhancement achieved through human-LLM collaboration. Initial ontology generation was performed using One Shot (OS) and Chain of Thought (CoT) prompts, demonstrating the capability of LLMs to autonomously construct ontologies for PD monitoring and alerting. However, these outputs were not comprehensive and required substantial human refinement to enhance their completeness and accuracy. X-HCOME, a hybrid ontology engineering approach that combines human expertise with LLM capabilities, showed significant improvements in ontology comprehensiveness. This methodology resulted in ontologies that are very similar to those constructed by experts. Further experimentation with SimX-HCOME+, another hybrid methodology emphasizing continuous human supervision and iterative refinement, highlighted the importance of ongoing human involvement. This approach led to the creation of more comprehensive and accurate ontologies. Overall, the paper underscores the potential of human-LLM collaboration in advancing ontology engineering, particularly in complex domains like PD. The results suggest promising directions for future research, including the development of specialized GPT models for ontology construction.

cs.AI↗