Abstract
In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.
Highlights
Morphological biometrics, based on quantitative measures of the human body [1,2], as well as behavioural biometrics, based on the patterns of actions performed by a subject, have proved to be helpful for e-security and e-health [3]
These features are based on several factors: the duration for which the pen is used on the sheet or near it, the total time to complete a specific handwriting/drawing task and other features based on the number of strokes performed during a task and/or the pressure applied by the pen on the paper, In this research, we have improved upon the classification accuracies as compared with our previous research [15] by using principal component analysis (PCA) and modified fast correlation–based filtering strategies
We propose the merging of the temporal, kinematic, statistical, spectraland cepstral-domain features to detect the mood state
Summary
Morphological biometrics, based on quantitative measures of the human body [1,2], as well as behavioural biometrics, based on the patterns of actions performed by a subject, have proved to be helpful for e-security and e-health [3]. The use of behavioural biometrics, especially, in particular, the online analysis of the activity of a subject performing a handwriting or drawing task enables the characterisation of mood states, especially, depression, anxiety and stress [14]. Their work shows the use of various features to discriminate among negative moods (depression, anxiety and stress) with significant accuracy, sensitivity and specificity by using random forest classification These features are based on several factors: the duration for which the pen is used on the sheet or near it (in the air), the total time to complete a specific handwriting/drawing task and other features based on the number of strokes performed during a task and/or the pressure applied by the pen on the paper, In this research, we have improved upon the classification accuracies as compared with our previous research [15] by using principal component analysis (PCA) and modified fast correlation–based filtering (mFCBF) strategies. We can see that the first PC variance PC1 is greatly reduced after applying PCA
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