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    王丰, 王亚沙, 王江涛, 熊昊一, 赵俊峰, 张大庆. 基于智能手机感知数据的心理压力评估方法[J]. 计算机研究与发展, 2019, 56(3): 611-622. DOI: 10.7544/issn1000-1239.2019.20170809
    引用本文: 王丰, 王亚沙, 王江涛, 熊昊一, 赵俊峰, 张大庆. 基于智能手机感知数据的心理压力评估方法[J]. 计算机研究与发展, 2019, 56(3): 611-622. DOI: 10.7544/issn1000-1239.2019.20170809
    Wang Feng, Wang Yasha, Wang Jiangtao, Xiong Haoyi, Zhao Junfeng, Zhang Daqing. Mental Stress Assessment Approach Based on Smartphone Sensing Data[J]. Journal of Computer Research and Development, 2019, 56(3): 611-622. DOI: 10.7544/issn1000-1239.2019.20170809
    Citation: Wang Feng, Wang Yasha, Wang Jiangtao, Xiong Haoyi, Zhao Junfeng, Zhang Daqing. Mental Stress Assessment Approach Based on Smartphone Sensing Data[J]. Journal of Computer Research and Development, 2019, 56(3): 611-622. DOI: 10.7544/issn1000-1239.2019.20170809

    基于智能手机感知数据的心理压力评估方法

    Mental Stress Assessment Approach Based on Smartphone Sensing Data

    • 摘要: 较大的心理压力对大学生的心理和生理均会产生危害.心理压力往往在前期容易被人忽视,从而导致严重的问题.因此,如果能较早发现心理压力,并进行合理干预,有益于人的身心健康.传统心理压力检测方法以问卷调查和借助专业设备的评估为主,但都存在成本较高,且对被评估对象侵扰较大等不足.另一方面,随着智能手机的快速普及,通过手机中内置的位置、声音、加速度等多种传感器感知用户的行为习惯,并基于感知数据评估用户心理压力成为一种低成本、低侵扰的心理压力评估手段.在此背景下,针对基于智能手机感知数据分析,对评估大学生心理压力的方法展开了研究,从感知数据中提取合理的特征,提出了一种更高效的心理压力评估方法.首先,讨论了如何从原始的手机感知数据提取出合理的特征;其次,介绍将心理压力评估转化为分类问题,并使用半监督学习方法构造分类模型;最后,在开放数据集StudentLife上对上述模型进行实验验证.实现结果表明:该方法在心理压力检测精确度和召回率等方面均优于基线方法.

       

      Abstract: Mental stress is harmful on individuals’ physical and mental well-being. It is often easy to be overlooked in the early stage, leading to serious problems. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones with various embedded sensors, which can capture users’ context data contains movement, sound, location and so on, it is an alternative way to access users’ behavior by smartphones, which is less intrusive. This paper proposes an automatic and non-intrusive stress detection approach based on mobile sensing data captured by smartphones. By extracting reasonable features from the perceived data, a more efficient psychological stress assessment method is proposed. First, we generate lots of features represent users’ behavior and explore the correlation between mobile sensing data and stress, then identify discriminative features. Second, we further develop a semi-supervised learning based stress detection model. Specifically, we use techniques such as co-training and random forest to deal with insufficient data. Finally, we evaluate our model based on the StudentLife dataset, and the experimental results verify the advantages of our approach over other baselines.

       

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