EMOTHAW: A novel database for emotional state recognition from handwriting

• URL: http://arxiv.org/abs/2202.12245v1
• Date: Wed, 23 Feb 2022 15:15:44 GMT
• Title: EMOTHAW: A novel database for emotional state recognition from handwriting
• Authors: Laurence Likforman-Sulem, Anna Esposito, Marcos Faundez-Zanuy, Stephan Clemen\c{c}on, Gennaro Cordasco
• Abstract summary: We present a first publicly available handwriting database which relates emotional states to handwriting, that we call EMOTHAW. This database includes samples of 129 participants whose emotional states are assessed by the Depression Anxiety Stress Scales (DASS) questionnaire. Records consist in pen positions, on-paper and in-air, time stamp, pressure, pen azimuth and altitude.
• Score: 2.4149105714758545
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The detection of negative emotions through daily activities such as handwriting is useful for promoting well-being. The spread of human-machine interfaces such as tablets makes the collection of handwriting samples easier. In this context, we present a first publicly available handwriting database which relates emotional states to handwriting, that we call EMOTHAW. This database includes samples of 129 participants whose emotional states, namely anxiety, depression and stress, are assessed by the Depression Anxiety Stress Scales (DASS) questionnaire. Seven tasks are recorded through a digitizing tablet: pentagons and house drawing, words copied in handprint, circles and clock drawing, and one sentence copied in cursive writing. Records consist in pen positions, on-paper and in-air, time stamp, pressure, pen azimuth and altitude. We report our analysis on this database. From collected data, we first compute measurements related to timing and ductus. We compute separate measurements according to the position of the writing device: on paper or in-air. We analyse and classify this set of measurements (referred to as features) using a random forest approach. This latter is a machine learning method [2], based on an ensemble of decision trees, which includes a feature ranking process. We use this ranking process to identify the features which best reveal a targeted emotional state. We then build random forest classifiers associated to each emotional state. Our results, obtained from cross-validation experiments, show that the targeted emotional states can be identified with accuracies ranging from 60% to 71%.

Related papers

• Construction and Evaluation of Mandarin Multimodal Emotional Speech Database [0.0]
  The validity of dimension annotation is verified by statistical analysis of dimension annotation data. The recognition rate of seven emotions is about 82% when using acoustic data alone. The database is of high quality and can be used as an important source for speech analysis research.
  arXiv  Detail & Related papers  (2024-01-14T17:56:36Z)
• DepressionEmo: A novel dataset for multilabel classification of depression emotions [6.26397257917403]
  DepressionEmo is a dataset designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts. This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models. We provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and Light GBM; and deep learning methods such as BERT, GAN-BERT, and BART.
  arXiv  Detail & Related papers  (2024-01-09T16:25:31Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• PART: Pre-trained Authorship Representation Transformer [64.78260098263489]
  Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage. Previous works use hand-crafted features or classification tasks to train their authorship models, leading to poor performance on out-of-domain authors. We propose a contrastively trained model fit to learn textbfauthorship embeddings instead of semantics.
  arXiv  Detail & Related papers  (2022-09-30T11:08:39Z)
• Seeking Subjectivity in Visual Emotion Distribution Learning [93.96205258496697]
  Visual Emotion Analysis (VEA) aims to predict people's emotions towards different visual stimuli. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. We propose a novel textitSubjectivity Appraise-and-Match Network (SAMNet) to investigate the subjectivity in visual emotion distribution.
  arXiv  Detail & Related papers  (2022-07-25T02:20:03Z)
• Gender classification by means of online uppercase handwriting: A text-dependent allographic approach [0.0]
  This paper presents a gender classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a male or a female.
  arXiv  Detail & Related papers  (2022-03-18T10:37:19Z)
• Writer Recognition Using Off-line Handwritten Single Block Characters [59.17685450892182]
  We use personal identity numbers consisting of the six digits of the date of birth, DoB. We evaluate two recognition approaches, one based on handcrafted features that compute directional measurements, and another based on deep features from a ResNet50 model. Results show the presence of identity-related information in a piece of handwritten information as small as six digits with the DoB.
  arXiv  Detail & Related papers  (2022-01-25T23:04:10Z)
• Towards an IMU-based Pen Online Handwriting Recognizer [2.6707647984082357]
  We present a online handwriting recognition system for word recognition based on inertial measurement units (IMUs) This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss.
  arXiv  Detail & Related papers  (2021-05-26T09:47:19Z)
• EmoGraph: Capturing Emotion Correlations using Graph Networks [71.53159402053392]
  We propose EmoGraph that captures the dependencies among different emotions through graph networks. EmoGraph outperforms strong baselines, especially for macro-F1. An experiment illustrates the captured emotion correlations can also benefit a single-label classification task.
  arXiv  Detail & Related papers  (2020-08-21T08:59:29Z)
• Emotion Carrier Recognition from Personal Narratives [74.24768079275222]
  Personal Narratives (PNs) are recollections of facts, events, and thoughts from one's own experience. We propose a novel task for Narrative Understanding: Emotion Carrier Recognition (ECR)
  arXiv  Detail & Related papers  (2020-08-17T17:16:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.