Handwriting and Drawing Features for Detecting Negative Moods

Cordasco, Gennaro; Scibelli, Filomena; Faundez-Zanuy, Marcos; Likforman-Sulem, Laurence; Esposito, Anna

doi:10.1007/978-3-319-95095-2_7

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…• Speech: para-verbals features-e.g., speed, silences, pauses [3,5,[8][9][10]-as well as non-verbal features [17][18][19][20] in read and spontaneous speeches; • Handwriting and drawing [4,6,8] mainly focusing on the shape of the drawn lines; • Video analysis: face expressions [21], eye movements [22]; • Content of written and spoken words [23][24][25][26]; • Electroencephalogram (EEG) [27][28][29][30][31]; • Multimodality: more than a source of data is used and combined to improve detection performance [32,33].…”

Section: Related Workmentioning

confidence: 99%

“…These procedures are extremely timeconsuming, require a high level of expertise to reliably interpret the interviews' outputs, and can be affected by clinicians' theoretical orientations and an overestimation of patient's progresses. In the last years, however, an ever-growing interest has been turned toward the possibility of detecting depression through automatic methods by making use of the interaction between human beings and computer-aided devices via a suitable definition of measurable behavioural depression features [3][4][5][6][7][8][9][10]. Some authors analyse speaking rates and silences in read and spontaneous speech (see [3,5,[8][9][10] for further details), whereas others consider handwriting, drawing, and other behavioral features (see [4,6,8], also for more complete references).…”

Section: Introductionmentioning

confidence: 99%

“…In the last years, however, an ever-growing interest has been turned toward the possibility of detecting depression through automatic methods by making use of the interaction between human beings and computer-aided devices via a suitable definition of measurable behavioural depression features [3][4][5][6][7][8][9][10]. Some authors analyse speaking rates and silences in read and spontaneous speech (see [3,5,[8][9][10] for further details), whereas others consider handwriting, drawing, and other behavioral features (see [4,6,8], also for more complete references). Nevertheless, speech remains the easiest source of behavioral data to be collected.…”

Section: Introductionmentioning

confidence: 99%

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A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

et al. 2021

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Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.

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