Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques.
Stress is one of the biggest problems in modern society. It may not be possible for people to perceive if they are under high stress or not. It is important to detect stress early and unobtrusively. In this context, stress detection can be considered as a classification problem. In this study, it wa...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 4; pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142576163&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142576163 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2020 vid: 44 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142576163 142576163 142576163 10.1007/s10916-020-1530-z 142576163 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques. aug: au: Sağbaş, Ensar Arif Korukoglu, Serdar Balli, Serkan affil: Faculty of Engineering, Department of Computer Engineering, Ege University, 35100, İzmir, Turkey sug: subj: Stress, Psychological Diagnosis Smartphone Machine Learning Accelerometers Keyboards Human Algorithms Mobile Applications Descriptive Statistics Decision Trees Probability ab: Stress is one of the biggest problems in modern society. It may not be possible for people to perceive if they are under high stress or not. It is important to detect stress early and unobtrusively. In this context, stress detection can be considered as a classification problem. In this study, it was investigated the effects of stress by using accelerometer and gyroscope sensor data of the writing behavior on a smartphone touchscreen panel. For this purpose, smartphone data including two states (stress and calm) were collected from 46 participants. The obtained sensor signals were divided into 5, 10 and 15 s interval windows to create three different data sets and 112 different features were defined from the raw data. To obtain more effective feature subsets, these features were ranked by using Gain Ratio feature selection algorithm. Afterwards, writing behaviors were classified by C4.5 Decision Trees, Bayesian Networks and k-Nearest Neighbor methods. As a result of the experiments, 74.26%, 67.86%, and 87.56% accuracy classification results were obtained respectively. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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