Appearance-Based Gaze Estimation in the Wild

Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild

Analisis Eye-Tracking Pengalaman Pengguna pada TikTok Museum MACAN

This experimental study explores how Generation Z and Y in Indonesia interact with Museum MACAN’s TikTok content, using eye-tracking to understand engagement with cultural content on this pivotal platform. Participants undertook tasks of varying lengths (120, 300, and 180 seconds) to reflect different engagement scenarios, with metrics like Participant Quality Grade (PQG), test duration, browser dimensions, K-factor, gaze, and fixation counts assessing interaction depth. Results indicate high engagement levels, with quality ranging from good to excellent and a noted preference for focal processing, suggesting users’ concentrated attention on video content. Heatmaps confirmed video content as the primary focus area. This study highlights TikTok’s role in enhancing museum engagement and provides insights for cultural institutions to refine their social media content strategies.Media sosial telah menjadi bagian penting dalam kehidupan sehari-hari, dengan TikTok sebagai salah satu platform yang paling populer di Indonesia. Penelitian ini bertujuan untuk mengevaluasi pengalaman pengguna TikTok Museum MACAN menggunakan teknik eye-tracking. Metode kuantitatif dengan pendekatan eye-tracking digunakan dalam penelitian ini. Subjek penelitian terdiri dari pengguna Gen Z (berusia 18-23 tahun) dan Gen Y (berusia 26-34 tahun) yang merupakan followers TikTok Museum MACAN. Eksperimen dilakukan melalui serangkaian tugas yang melibatkan menelusuri TikTok Museum MACAN dan menjawab pertanyaan terkait konten museum. Durasi pada setiap tugas berbeda, tugas 1 120 detik, Tugas 2 300 detik, dan Tugas 3 180 detik. Data hasil eksperimen dianalisis berdasarkan item participant quality grade (PQG), test duration, test browser width, test browser height, K-factor mean, gaze count, fixation count, dan heatmap. Hasil penelitian menunjukkan: 1) nilai PQG seluruh partisipan 4, 5, dan 6; 2) mayoritas partisipan dalam menelusuri konten TikTok Museum MACAN membutuhkan waktu yang lebih lama; 3) lebar dan tinggi layar browser ideal rata-rata seluruh partisipan 1.416,6 x 876,8 px; 4) K-factor mean seluruh partisipan cenderung menunjukkan K>0; 5) nilai gaze count dan fixation count partisipan pada setiap tugas bervariasi; 7) heatmap sampel partisipan menunjukkan fokus perhatian cenderung pada tampilan video

Development of an Eye-Gaze Input System With High Speed and Accuracy through Target Prediction Based on Homing Eye Movements

In this study, a method to predict a target on the basis of the trajectory of eye movements and to increase the pointing speed while maintaining high predictive accuracy is proposed. First, a predictive method based on ballistic (fast) eye movements (Approach 1) was evaluated in terms of pointing speed and predictive accuracy. In Approach 1, the so-called Midas touch problem (pointing to an unintended target) occurred, particularly when a small number of samples was used to predict a target. Therefore, to overcome the poor predictive accuracy of Approach 1, we developed a new predictive method (Approach 2) using homing (slow) eye movements rather than ballistic (fast) eye movements. Approach 2 overcame the disadvantage (inaccurate prediction) of Approach 1 by shortening the pointing time while maintaining high predictive accuracy

Passive RFID-based Intelligent Gloves for Alternative and Assistive Communication - A Preliminary Study

We introduce intelligent gloves based on passive ultrahigh frequency (UHF) radio frequency identification (RFID) technology, which comprises of four antenna parts and three RFID integrated circuits (ICs). Each of the ICs (in middle finger, ring finger and small finger) have their unique IDs, which can be activated by gentle touch of thumb, and used to send a specific message, which is displayed on a computer screen. Two users tested the gloves in an office environment with M6 mercury RFID reader and a specially developed software. The achieved success rate in these preliminary tests was 100 %. We consider these results promising first steps for future wearable passive RFID-based augmentative and alternative communication (AAC) solutions.acceptedVersionPeer reviewe