Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training.

Facial expressivity analysis in Parkinson’s disease (PD) has received considerable attention due to the characteristic symptom of hypomimia, which is defined as the reduction or loss of spontaneous facial movements and emotional facial expressions. Hypomimia has been linked to a detrimental impact on close relationships and psychological well-being. This investigation presents the preliminary findings of a quantitative analysis of facial expressivity in a longitudinal pilot cohort of nine Mexican subjects diagnosed with PD, evaluated during two sessions: at baseline and following eight weeks of clinical follow-up under stable medication conditions. In each session, participants were instructed to mimic eight specific facial expressions while their faces were recorded with a high-definition camera. Facial expressivity was quantified by extracting dynamic features (Euclidean distances) between key landmarks from video recordings using the MediaPipe FaceMesh framework (utilizing 468 3D landmarks, excluding the iris). The landmark coordinates were normalized for translation, scale (via intercanthal distance), and rotation. Our quantitative results showed that the normalized landmark distances had a higher offset (representing the resting baseline) and a wider peak-to-peak amplitude at the baseline evaluation compared to the eight-week follow-up, indicating a significant reduction in facial mobility and expressivity. These physiological findings concurred with a decrease in the participants’ clinical status, as assessed by the Unified Parkinson’s Disease Rating Scale (UPDRS) Activities of Daily Living (ADL) and Motor Examination Scores (MES). To demonstrate the discriminative power of the extracted dynamic features, a Support Vector Machine (SVM) classifier was evaluated using a Leave-One-Subject-Out (LOSO) cross-validation scheme to prevent data leakage. The SVM model successfully distinguished between baseline and follow-up sessions with an exploratory participant-independent accuracy of 71.4% (F1-score of 70.6%). This pilot study demonstrates the feasibility of video-based, non-invasive digital tools to track subtle longitudinal changes in hypomimia, highlighting their potential as complementary, objective instruments for disease monitoring and clinical follow-up.