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首页> 外文会议>2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference >Autonomously sensing loneliness and its interactions with personality traits using smartphones
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Autonomously sensing loneliness and its interactions with personality traits using smartphones

机译：使用智能手机自主感知孤独感及其与人格特质的相互作用

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摘要

One in five Americans is lonely and loneliness disproportionately affects senior citizens and international students. In this paper, we propose Socialoscope, a smartphone app that passively senses user loneliness from their communication and interaction patterns (e.g. calls, SMS, browsing patterns and social media usage), while factoring in different personality types. Data was gathered from 9 international students over 2 weeks to train machine learning classifiers for loneliness. Using smartphone-sensed data, we show that of the big 5 personality traits, extraversion and emotional stability features were strongly correlated with smartphone-sensed loneliness. We synthesized machine learning classifiers that classified user smartphone interaction and communication features into ranges of loneliness with an accuracy of 98%, while factoring in user personality types.
机译：五分之一的美国人感到孤独，孤独感严重影响了老年人和国际学生。在本文中，我们提出了Socialoscope，这是一款智能手机应用程序，可以从用户的沟通和互动模式（例如通话，短信，浏览模式和社交媒体使用情况）中被动感知用户的孤独感，同时考虑不同的性格类型。在2个星期内从9名国际学生那里收集了数据，以训练机器学习分类器的孤独感。使用智能手机感测的数据，我们发现在5大人格特质中，外向性和情绪稳定特征与智能手机感测的孤独感密切相关。我们综合了机器学习分类器，该分类器将用户智能手机的交互和通信功能分为孤独性范围，准确度达到98％，同时考虑了用户个性类型。

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