Stress Detection Using Machine Learning

@article{1702649,
      author = {Sayali Shelke, Shubhangi Kor, Sahil Bavaskar, Kirti Rajadnya},
      title = {Stress Detection Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {10},
      pages = {38-42},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702649.pdf},
      abstract = {A In today?s word one of the major leading factors to health problem is STRESS. The basic parameters on which stress can be identified are heart rate, galvanic skin response, body temperature, blood pressure, which provides detailed information of the state of mind of a person. These parameters varying from person to person on the basis of certain things such as their body condition, age and gender. The main goal of the system is to analyze the mental stress through physiological data using electrocardiograph in different positions and moods. Different pre-processing techniques can be used for stress detection. In feature extraction discrete wavelet transform can apply. Many classifiers like artificial neural network, support vector machine, Bayesian network, and decision tree are using to get more accurate results based on accuracy. Physiological sensors analytics  is becoming  more and more important as the availability of sensor-enabled portable, wearable,  and  implantable  devices  becomes  ubiquitous  in  the growing  Internet  of  Things  (IOT).  Physiological multi-sensor studies have been conducted successfully to detect stress. },
      keywords = {Stress Detect Parameters, Chest Pain, Rest ECG, Prevention.},
      month = {April},
  }