In this article, we explore computer vision approaches to detect abnormal
head pose during e-learning sessions and we introduce a study on the effects of
mobile phone usage during these sessions. We utilize behavioral data collected
from 120 learners monitored while participating in a MOOC learning sessions.
Our study focuses on the influence of phone-usage events on behavior and
physiological responses, specifically attention, heart rate, and meditation,
before, during, and after phone usage. Additionally, we propose an approach for
estimating head pose events using images taken by the webcam during the MOOC
learning sessions to detect phone-usage events. Our hypothesis suggests that
head posture undergoes significant changes when learners interact with a mobile
phone, contrasting with the typical behavior seen when learners face a computer
during e-learning sessions. We propose an approach designed to detect
deviations in head posture from the average observed during a learner's
session, operating as a semi-supervised method. This system flags events
indicating alterations in head posture for subsequent human review and
selection of mobile phone usage occurrences with a sensitivity over 90%.

研究中使用计算机视觉技术检测在线学习过程中异常的头部姿态，并介绍了手机使用对学习过程的影响的研究。通过收集来自 120 位学习者在 MOOC 学习过程中的行为数据，研究聚焦于手机使用事件对行为和生理反应（特别是注意力、心率和冥想）的影响，并提出了一种通过摄像头图像来估计头部姿态事件以检测手机使用事件的方法。本研究的假设是，当学习者与手机交互时，头部姿态会发生显著变化，与学习者面对计算机时的典型行为形成对比。我们提出了一种半监督方法，用于检测学习者会话期间头部姿态与平均姿态的偏差，并对发现的头部姿态异常事件进行标记，敏感度超过 90%。

在线学习中检测分散注意力的生物特征和行为建模

Biometrics and Behavioral Modelling for Detecting Distractions in Online  Learning

The increasing use of electronic forms of communication presents new
opportunities in the study of mental health, including the ability to
investigate the manifestations of psychiatric diseases unobtrusively and in the
setting of patients' daily lives. A pilot study to explore the possible
connections between bipolar affective disorder and mobile phone usage was
conducted. In this study, participants were provided a mobile phone to use as
their primary phone. This phone was loaded with a custom keyboard that
collected metadata consisting of keypress entry time and accelerometer
movement. Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We propose an
end-to-end deep architecture based on late fusion, named DeepMood, to model the
multi-view metadata for the prediction of mood scores. Experimental results
show that 90.31% prediction accuracy on the depression score can be achieved
based on session-level mobile phone typing dynamics which is typically less
than one minute. It demonstrates the feasibility of using mobile phone metadata
to infer mood disturbance and severity.

通过一项与双相情感障碍的移动电话使用可能存在的关联探究，提出了 DeepMood 这一基于后期融合的端到端深度架构，可以通过对手机键入动态的会话级分析，来预测情绪分数，精度可达 90.31%，这表明使用移动电话元数据来推断情绪扰乱和严重程度的可行性。

DeepMood：用于情感检测的手机打字动态建模

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

Mobile phone usage provides a wealth of information, which can be used to
better understand the demographic structure of a population. In this paper we
focus on the population of Mexican mobile phone users. Our first contribution
is an observational study of mobile phone usage according to gender and age
groups. We were able to detect significant differences in phone usage among
different subgroups of the population. Our second contribution is to provide a
novel methodology to predict demographic features (namely age and gender) of
unlabeled users by leveraging individual calling patterns, as well as the
structure of the communication graph. We provide details of the methodology and
show experimental results on a real world dataset that involves millions of
users.

本文针对墨西哥手机用户人口的性别和年龄分组进行了观察研究，并提供了一种使用通话模式和通讯图的新型预测方法，可用于预测未标记用户的年龄和性别特征。