Novel Approach for Stress Detection Using Smartphone and E4 Device

  • Tejaswini PanureEmail author
  • Shilpa Sonawani
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


Stress reduction is important for maintaining overall human health. There are different methodologies for detecting stress including clinical tests, traditional methods and various sensors and systems developed using either a smartphone, wearable devices or sensors connected to the human body. In this paper, a novel methodology is proposed by creating a personalized model from the generalized model because stress differs from person to person for the same work profile. A generalized model for stress detection is developed from smartphone and E4 device data of all the available individuals. A generalized model is used to build a personalized model that is a person-specific model and will be build up over a time of time when enough amount of person-specific data gets collected. This proposed methodology intends to give more accuracy as two devices are used with the novel approach of model building. Various machine learning algorithms such as ANN, xgboost, and SVM are implemented with the E4 device dataset while the LASSO regression model is used for smartphone data. ANN worked best than xgboost and SVM with 93.71% accuracy. In LASSO, 0.6556 RMSE is achieved.


Stress E4 device Artificial Neural Network (ANN) LASSO regression Xgboost Support Vector Machine (SVM) 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringDr. Vishwanath Karad MIT World Peace UniversityPuneIndia

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