Voice Activity Projection (VAP) has been actively studied to enable natural turn-taking in spoken dialogue systems, relying primarily on acoustic features. Visual cues such as head movements are also known to contribute to turn-taking prediction; however, camera-based approaches are affected by placement and lighting conditions and are not always reliably available to dialogue systems. As a camera-independent approach for directly capturing head motion, earable devices offer a promising solution. In this study, we propose Sensor-Augmented VAP, a framework that integrates in-ear inertial measurement unit (IMU) signals with a pre-trained VAP model via a lightweight residual fusion module. To validate our proposed method, we collected a dataset pairing conversational audio with in-ear IMU data, comprising 12 dyadic Japanese dialogues recorded using microphones and earbuds. Experiments in speaker-independent and speaker-dependent settings demonstrate that IMU fusion consistently improves weighted F1 score for shift detection and reduces VAP loss over the audio-only baseline. These results confirm that head-motion cues are effective for enhancing turn-taking prediction.

Sustained input of lifelog data is critical for conversational health applications, where algorithms and AI advise users based on recorded lifelog data such as meals, exercise, and sleep. However, an effective method for presenting information to encourage this continuity has not been identified. This study developed three intervention methods for prompting lifelog entries: (a) wording adjustment based on individual characteristics, (b) timing adjustment based on physical activity, and (c) a combination of these adjustments. An empirical experiment with 422 participants was conducted to evaluate the effects.

This study presents SatisFace, a novel approach to measuring customer satisfaction in amusement parks by analyzing facial expressions at entry and exit points. Recognizing satisfaction's subjectivity, SatisFace assesses facial Action Unit (AU) changes, tailoring assessments to individual user characteristics. Data were collected through an experiment with 200 participants at Soleil Hill, using questionnaires and OpenFace for facial analysis. A gradient-boosting machine learning model revealed a significant correlation between changes in facial expressions and satisfaction levels.

Data binding in front-end user interface web development allows for the UI to update automatically in sync with data, removing complexity from development and simplifying programming. However, data binding often causes website performance to degrade due to its increased complexity. In this paper, we propose "Visible Anchors" to solve the performance degradation caused by data binding. We present a novel web front-end compiler called FaST that builds upon this idea. We also compare the rendering speed of websites built by existing methods and the FaST compiler. The evaluation result revealed that the websites built by the FaST compiler are rendered at a minimum 17.1 times faster than the ones built by the existing methods. Most notably, FaST achieves all of this without any additional cost to the developer.