Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

Parkinson’s disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and how easily patients adapt to their walking environments. PD is progressive in nature, and if undetected and untreated, the symptoms grow worse over time. Fortunately, PD can be detected early using gait features since the loss of motor control results in gait impairment. In general, techniques for capturing gait can be categorized as computer-vision-based or sensor-based. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, the subjects’ consciousness of worn sensors and being actively monitored may further impact their motor function. Recent advances in computer vision have enabled the tracking of body parts in videos in a markerless motion capture scenario via human pose estimation (HPE). Although markerless motion capture has been studied in comparison with gold-standard motion-capture techniques, it is yet to be evaluated in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. First, we perform HPE on the subjects using AlphaPose. Then, we extract and analyse eight features, from which five features are systematically selected, achieving up to 93% accuracy, 96% precision, and 92% recall in arbitrary views.

Traditional methods of analyzing badminton matches, such as video movement analysis, are time-consuming, prone to errors, and rely heavily on manual annotation. This creates challenges in accurately and efficiently classifying badminton actions and player poses. This paper aims to develop an accurate time series classification method for badminton poses using landmark tracking. The proposed method integrates Long Short-Term Memory (LSTM) networks with landmark tracking to classify badminton poses in a time series, addressing the limitations of traditional video analysis techniques. The dataset consists of 30 respondents performing three distinct activities—lob, smash, and serve—under two conditions: good and bad execution. The approach combines LSTM networks with landmark tracking data, utilizing intra-class variation from a multi-view dataset to enhance pose classification accuracy. The LSTM model achieved high accuracy in classifying badminton poses, successfully detecting serves, lobs, and smashes in real-time with over 90% accuracy. Additionally, the system improved match analysis, achieving 85% accuracy in detection and classification, demonstrating the effectiveness of combining landmark tracking with machine learning for sports analysis. This study underscores the importance of pose estimation in badminton analysis, particularly through landmark tracking, which significantly improves the accuracy of classifying player poses and contributes to the advancement of automated sports analysis.

Jurnal yang didedikasikan untuk pertukaran hasil penelitian berkualitas tinggi di semua aspek Informatika, Teknologi Informasi, dan Ilmu Data

Gerakan squat dianggap sebagai latihan yang sangat penting dalam kebugaran dan rehabilitasi, membutuhkan koordinasi serta postur tubuh yang benar. Pelaksanaan yang keliru, terutama oleh mereka yang baru mulai, dapat meningkatkan kemungkinan terjadinya cedera. Untuk mengatasi masalah ini, ada kebutuhan akan sistem otomatis yang dapat mengklasifikasikan keterampilan pengguna berdasarkan pose tubuh saat melakukan squat. Penelitian ini menggunakan teknologi visi komputer untuk mendeteksi dan menilai kualitas gerakan. Salah satu masalah utama dalam sistem evaluasi squat yang ada adalah kemampuannya yang terbatas dalam menilai tingkat keterampilan; banyak di antaranya hanya memberikan umpan balik dalam bentuk benar atau salah. Maka, sangat penting untuk mengembangkan sistem klasifikasi yang dapat membedakan antara pemula dan atlet berpengalaman, terutama dalam mendukung program latihan yang lebih adaptif dan memberikan informasi. Salah satu solusi yang diusulkan adalah mengintegrasikan MediaPipe sebagai alat untuk ekstraksi pose dan menggunakan algoritma Support Vector Machine (SVM) sebagai metode klasifikasi. Data video dari 40 pengguna diubah menjadi koordinat pose, yang kemudian digunakan untuk melatih model SVM dengan kernel RBF. Hasil dari pengujian menunjukkan tingkat akurasi yang baik, sebesar 99,1%, yang menunjukkan seberapa efektif sistem ini dalam secara otomatis dan akurat mengidentifikasi tingkat keterampilan.

In this study, we analyze mucociliary transport (MCT) by measuring the magnitude and identifying regions of ciliary beats using high-frame-rate microscopic videos. Our methodology, integrating dense optical flow (DOF), connected component labeling (CCL), Butterworth filter, and Fast Fourier Transform (FFT), captures ciliary movement and magnitude. We focus on region extraction, quantification of ciliary activity, and classification of power and recovery strokes in ciliary beat frequency (CBF), which are crucial for evaluating MCT efficiency. Our approach was able to extract the ciliary region semi-automatically, obtain the CBF, and visualize the ciliary movement in each frame. Despite dataset challenges and limited ground truth, our approach shows a promising result for ciliary dynamics research and medical diagnostics. We hope for future open-source datasets with ground-truth ciliary beat patterns to enable developing and evaluating automated ciliary analysis techniques, leading to improved assessment.