Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

Abstract

In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstraldomain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank. As next step, we select the best features using the Fast Correlation-Based Filtering method. Since our dataset has 129 users, then as next step, we augmented the training data by randomly selecting a percentage of the training data and adding a small random Gaussian noise to the extracted features. We then train a radial basis SVM model using the Leave-One-Out (LOO) methodology. The experimental results show an average accuracy classification improvement ranging of 15%, and an accuracy classification improvement ranging from 4% to 34% compared with baseline (state of the art) for specific emotions such as depression, anxiety, stress, and typical emotional states.

Making drawings speak through mathematical metrics

“…2) We can also extend the study to other species, particularly great apes, which are known to draw, and assess whether their drawing is motivated by internal representativeness 20 . 3) Finally, the method can be extended to psychopathologies such as autism 79,80 and even certain emotional disorders 21,81 , or simply be used to measure learning di culties and creativity 22,82 . New technologies combined with new mathematical methods appear to be very useful and provide new possibilities to test mental states, intentions and emotions beyond these representations 83 .…”

Exploiting Spectral and Cepstral Handwriting Features on Diagnosing Parkinson’s Disease

“…Finally, in [36] we proposed the use of spectral and cepstral features for emotion recognition. Here, we concatenated these features with very simple temporal features.…”

“…In the second step, it takes the features of the first steps and selectes the ones with correlation lower than a threshold. In our modified version [36], mFCBF differs of the original FCBF at step 5, where the selected feature is the one having higher correlation with the output. Algorithm 5.1 shows a pseudo-code for the mFCBF process.…”

Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning

“…In our work on Parkinson's disease detection [16], we found it useful to add kinematic and statistical features; therefore, the first contribution of this work is to add these features to the user's features that we used in [17]. The second contribution of this work is the use of a PCA-mFCBF pipeline; in fact, it is probably the most important contribution.…”