An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data

Abstract

Today's sedentary life leads to a plethora of lifestyle-related illnesses. This has led to the quest to predict diseases before they occur. In the past, research on stress prediction was carried out conventionally in a laboratory-based environment. However, recent studies are focusing on developing noninvasive ways to predict stress with the help of wearable devices. Generally, the models developed for stress prediction do not provide accurate results because the stress patterns are highly subjective and vary from person to person. Therefore, person-dependent models may achieve higher accuracies. These models, however, have to be trained with collected data over a comparatively longer period of time. In this paper, an Adaptive Neuro-Fuzzy Inference System aided Fire Works Grey Wolf Optimization (ANFIS-FWGWO) classification algorithm has been proposed for stress prediction. In particular, the proposed machine learning framework has been implemented to predict computer users' stress by using a sensor-integrated keyboard data. Various physiological parametric data were acquired during two different phases for the experimentation, and the received data was analyzed using an efficient machine learning framework. Specifically, the proposed framework encompasses various techniques such as data preprocessing for data smoothing and the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection algorithm for identifying the important features on the data set. From the experimental analysis, it is concluded that the ANFIS-FWGWO classification algorithm discriminates the stress subjects with a high degree of accuracy when compared with existing classification algorithms.

Novel Internet of Things based approach toward diabetes prediction using deep learning models

“…The future work of this study is to suggest an edge-based data processing system to bring computation and data storage closer to the patient's location to improve emergency services response time and save bandwidth in the system ( 20 ). In this research ( 21 ), the authors have investigated the effects of stress and for this purpose they used the sensor data from the accelerometer and gyroscope on smart phones. The authors monitored the writing behavior of 46 participants on the touchscreen panel of smart phones.…”

“…The authors applied a range of classification algorithms on these features such as Decision Trees, Bayesian Networks and k-Nearest Neighbor methods. The authors observed that the accuracy of these classifiers was 74.26, 67.86, and 87.56% respectively ( 21 ). In paper ( 22 ) discusses applicability of Intelligent IoT based on Collaborative Machine Learning in healthcare and medicine by presenting a holistic multi-layer architecture.…”