ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (3): 611-622.doi: 10.7544/issn1000-1239.2019.20170809

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Mental Stress Assessment Approach Based on Smartphone Sensing Data

Wang Feng1,2,5, Wang Yasha1,3, Wang Jiangtao1,2, Xiong Haoyi4, Zhao Junfeng1,2, Zhang Daqing1,2   

  1. 1(Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing 100871); 2(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871); 3(National Research Center of Software Engineering, Peking University, Beijing 100871); 4(Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA 65409); 5(Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210018)
  • Online:2019-03-01

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.

Key words: mental stress, context awareness, feature engineering, automatic detection, machine learning

CLC Number: