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To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real‐world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long‐term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network‐based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions. [ABSTRACT FROM AUTHOR]

In South Korea, military conscription lies at the core of the construction of notions of manhood and gender order as a whole. In a divided country, the hegemonic meaning of military service as a crucial marker of adult manhood remained unquestioned. However, since the implementation of full-blown neoliberalism, there has been a shift in societal gender norms and this in turn has changed the way in which militarized masculinities are constructed and enacted at the individual level. Drawn from qualitative interviews with South Korean former conscripts, this article examines the tension between militarized and neoliberal masculine ideals and how hegemonic constructions of militarized masculinities are challenged in it. [ABSTRACT FROM AUTHOR]