Wearable cameras provide an efficient and convenient way to record our lives, supporting real-time documentation and analysis across various domains. Recent research has explored diverse methods for temporal segmentation, which aim to transform unstructured video data into structured events. This transformation facilitates deeper video understanding, optimizes computational resources, and improves the accessibility and interpretability of video content for both machines and humans. However, unlike conventional videos, wearable camera recordings present unique challenges. These include highly unstable camera perspectives, diverse activities across various environments, and flexible duration. As a result, traditional temporal segmentation methods often fail to return effective results. This paper introduces SmartSeg, an unsupervised, non-parametric approach for segmenting wearable camera videos without labeled data. By capturing the fundamental meanings of the video, SmartSeg aggregates the video through the Temporal Self-Similarity Metric encoder and groups sequences of frames into coherent events through clustering techniques. We evaluated SmartSeg on three diverse datasets. We achieved a 50% increase in Mean-over-Frames(MoF) compared to the state-of-the-art on one egocentric dataset. We conducted a real-world case study on nursing simulations, demonstrating SmartSeg’s ability to effectively segment complex, noisy interactions with diverse activity transitions. The results highlight SmartSeg’s robustness in handling long, unstructured, and visually challenging wearable camera videos, establishing it as a promising tool for real-world video temporal segmentation tasks.

Through profiling learners’ music usage in everyday learning settings and depicting their learning experience when studying with a music app powered by a large-scale and real-world music library, this study revealed preliminary observations on how background music impacts learning under varying task load, and manifested intriguing patterns of learners’ music usage and music preferences in various task load conditions. Specifically, we piloted a three-day field experiment in students’ everyday learning environment. During the experiment, participants performed learning tasks with music in the background and completed a set of online surveys before and after each learning session. Our results suggested that learners’ self-selected, real-life background music could enhance their learning effectiveness, while the beneficial effect of background music was more apparent when the learning task was less mentally or temporally demanding. Towards a closer look at the characteristics of preferable music pieces under various task load conditions, our findings showed that music preferred by participants under high versus low temporal demand differs in a number of characteristics, including speechiness, acousticness, danceability, and energy. This study further reveals the effects of background music on learning under varying task load levels and provides implications for context-aware background music selection when designing musically enriched learning environments.

Empirical evidence of how background music benefits or hinders learning becomes the crux of optimizing music recommendation in educational settings. This study aims to further probe the underlying mechanism through an experiment in naturalistic setting. 30 participants were recruited to join a field experiment which was conducted in their own study places for one week. During the experiment, participants were asked to conduct learning sessions with music in the background and collect music tracks they deemed suitable for learning using a novel mobile-based music discovery application. A set of participant-related, context-related, and music-related data were collected via a pre-experiment questionnaire, surveys popped up in the music app, and the logging system of the music app. Preliminary results reveal correlations between certain music characteristics and learners' task engagement and perceived task performance. This study is expected to provide evidence for understanding cognitive and emotional dimensions of background music during learning, as well as implications for the role of personalization in the selection of background music for facilitating learning.

It is a common phenomenon for students to listen to background music while studying. However, there are mixed and inconclusive Kindings in the literature, leaving it unclear whether and in which circumstances background music can facilitate or hinder learning. This paper reports a study investigating the effects of Kive different types of background audio (four types of music and one environmental sound) on reading comprehension. An experiment was conducted with 33 graduate students, where a series of cognitive, metacognitive, affective variables and physiological signals were collected and analyzed. Preliminary results show that there were differences on these variables across different music types. This study contributes to the understanding and optimizing of background music for facilitating learning.