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首页> 外文会议>International Conference on Wearable and Implantable Body Sensor Networks >Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

机译：使用人格特质，可穿戴传感器和手机认识到学术表现，睡眠质量，压力水平和心理健康

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What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.
机译：有什么可以穿戴式传感器和智能手机的使用介绍一下学习成绩，自我报告的睡眠质量，压力和心理健康状况？要回答这个问题，我们使用移动电话，问卷调查和穿戴式传感器磨损日夜从66人参加，各30天，共计数据1,980天收集广泛的主观和客观的数据。我们分析了每日和每月的行为和生理模式和影响学习成绩（GPA）确定的因素，匹兹堡睡眠质量指数从SF-12（PSQI）评分，知觉压力量表（PSS），和心理健康综合得分（MCS），使用这些长达一个月的数据。我们还研究了所收集的数据如何准确分类的参与者分成高/低GPA，好/睡眠质量差，高/低自我报告的压力，高剂量组/低MCS使用特征选择和机器学习技术。我们发现PSQI，PSS，MCS，和GPA和人格类型之间的关联。使用从可穿戴式传感器和移动电话的目标数据分类精确度从67-92％不等。

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