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This paper presents a smartphone application that supports Shotokan Karate training by analysing posture and providing real-time feedback. The app evaluates three fundamental stances (Zenkutsu Dachi, Kokutsu Dachi, and Kiba Dachi) using Google ML Kit Pose Detection to extract body landmarks and compute joint-angle and alignment features, including proxy indicators of weight shift. The app also includes conditioning exercises (squats and push-ups) and a reflex-oriented interaction task. Results from a single-participant pilot are reported as feasibility evidence only and should not be generalised. A larger validation study with at least 30 practitioners across three skill levels (beginner, intermediate, advanced) is required, together with power analysis and reliability assessment, before broader conclusions can be drawn.

Abstract

Abstract
This article investigates the ability of large language models (LLMs) to evaluate semantic relations between word pairs by examining their alignment with human-generated semantic ratings. Semantic relations represent the degree of connection (e.g., relatedness or similarity) between linguistic elements and are traditionally validated against human-annotated datasets. Due to the challenges of building such datasets and recent progress in LLMs' capacity to model humanlike understanding, we explore whether LLMs can serve as reliable substitutes for traditional human ratings. We conducted experiments using multiple LLMs from OpenAI, Google, Mistral, and Anthropic, evaluating their performance across diverse English and Portuguese semantic relations datasets. We included in the analysis PAP900, a recently published dataset of semantic relations in Portuguese, to examine the influence of prior exposure to the dataset on LLM training. The results show that the LLM predictions correlate strongly with human ratings. The findings reveal the potential of LLMs to supplement or replace traditional semantic measure algorithms and crowd-sourced human annotations in semantic tasks.