An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data
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...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9475069/ |
| _version_ | 1818678145185742848 |
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| author | P. B. Pankajavalli G. S. Karthick R. Sakthivel |
| author_facet | P. B. Pankajavalli G. S. Karthick R. Sakthivel |
| author_sort | P. B. Pankajavalli |
| collection | DOAJ |
| description | 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 non-invasive 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. |
| first_indexed | 2024-12-17T09:10:36Z |
| format | Article |
| id | doaj.art-1b8b1667d2184939ab9bf27da3ee2266 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T09:10:36Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b8b1667d2184939ab9bf27da3ee22662022-12-21T21:55:14ZengIEEEIEEE Access2169-35362021-01-019950239503510.1109/ACCESS.2021.30943349475069An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard DataP. B. Pankajavalli0https://orcid.org/0000-0003-1992-7386G. S. Karthick1https://orcid.org/0000-0002-9340-6928R. Sakthivel2https://orcid.org/0000-0002-5528-2709Department of Computer Science, Bharathiar University, Coimbatore, IndiaDepartment of Computer Science, Bharathiar University, Coimbatore, IndiaDepartment of Applied Mathematics, Bharathiar University, Coimbatore, IndiaToday’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 non-invasive 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.https://ieeexplore.ieee.org/document/9475069/Machine learning frameworkstress predictionfire works algorithmgrey wolf optimizationfeature selectionclassification |
| spellingShingle | P. B. Pankajavalli G. S. Karthick R. Sakthivel An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data IEEE Access Machine learning framework stress prediction fire works algorithm grey wolf optimization feature selection classification |
| title | An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data |
| title_full | An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data |
| title_fullStr | An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data |
| title_full_unstemmed | An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data |
| title_short | An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data |
| title_sort | efficient machine learning framework for stress prediction via sensor integrated keyboard data |
| topic | Machine learning framework stress prediction fire works algorithm grey wolf optimization feature selection classification |
| url | https://ieeexplore.ieee.org/document/9475069/ |
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