17 research outputs found
An Ambient Intelligence-based Approach For Longitudinal Monitoring of Verbal and Vocal Depression Symptoms
Automatic speech recognition (ASR) technology can aid in the detection,
monitoring, and assessment of depressive symptoms in individuals. ASR systems
have been used as a tool to analyze speech patterns and characteristics that
are indicative of depression. Depression affects not only a person's mood but
also their speech patterns. Individuals with depression may exhibit changes in
speech, such as slower speech rate, longer pauses, reduced pitch variability,
and decreased overall speech fluency. Despite the growing use of machine
learning in diagnosing depression, there is a lack of studies addressing the
issue of relapse. Furthermore, previous research on relapse prediction has
primarily focused on clinical variables and has not taken into account other
factors such as verbal and non-verbal cues. Another major challenge in
depression relapse research is the scarcity of publicly available datasets. To
overcome these issues, we propose a one-shot learning framework for detecting
depression relapse from speech. We define depression relapse as the similarity
between the speech audio and textual encoding of a subject and that of a
depressed individual. To detect depression relapse based on this definition, we
employ a Siamese neural network that models the similarity between of two
instances. Our proposed approach shows promising results and represents a new
advancement in the field of automatic depression relapse detection and mental
disorders monitoring.Comment: 12 page
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are
leading contributors to the global burden of disease and intensely influence
social and financial welfare of individuals. The present comprehensive review
concentrate on the two mental disorders: Major depressive Disorder (MDD) and
Bipolar Disorder (BD) with noteworthy publications during the last ten years.
There is a big need nowadays for phenotypic characterization of psychiatric
disorders with biomarkers. Electroencephalography (EEG) signals could offer a
rich signature for MDD and BD and then they could improve understanding of
pathophysiological mechanisms underling these mental disorders. In this review,
we focus on the literature works adopting neural networks fed by EEG signals.
Among those studies using EEG and neural networks, we have discussed a variety
of EEG based protocols, biomarkers and public datasets for depression and
bipolar disorder detection. We conclude with a discussion and valuable
recommendations that will help to improve the reliability of developed models
and for more accurate and more deterministic computational intelligence based
systems in psychiatry. This review will prove to be a structured and valuable
initial point for the researchers working on depression and bipolar disorders
recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table
Leveraging recent advances in deep learning for audio-Visual emotion recognition
International audienceEmotional expressions are the behaviors that communicate our emotional state or attitude to others. They are expressed through verbal and non-verbal communication. Complex human behavior can be understood by studying physical features from multiple modalities; mainly facial, vocal and physical gestures. Recently, spontaneous multi-modal emotion recognition has been extensively studied for human behavior analysis. In this paper, we propose a new deep learning-based approach for audio-visual emotion recognition. Our approach leverages recent advances in deep learning like knowledge distillation and high-performing deep architectures. The deep feature representations of the audio and visual modalities are fused based on a model-level fusion strategy. A recurrent neural network is then used to capture the temporal dynamics. Our proposed approach substantially outperforms state-of-the-art approaches in predicting valence on the RECOLA dataset. Moreover, our proposed visual facial expression feature extraction network outperforms state-of-the-art results on the AffectNet and Google Facial Expression Comparison datasets
3D Geometric salient patterns analysis on 3D meshes
Pattern analysis is a wide domain that has wide applicability in many fields. In fact, texture analysis is one of those fields, since the texture is defined as a set of repetitive or quasi-repetitive patterns. Despite its importance in analyzing 3D meshes, geometric texture analysis is less studied by geometry processing community. This paper presents a new efficient approach for geometric texture analysis on 3D triangular meshes. The proposed method is a scale-aware approach that takes as input a 3D mesh and a user-scale. It provides, as a result, a similarity-based clustering of texels in meaningful classes. Experimental results of the proposed algorithm are presented for both real-world and synthetic meshes within various textures. Furthermore, the efficiency of the proposed approach was experimentally demonstrated under mesh simplification and noise addition on the mesh surface. In this paper, we present a practical application for semantic annotation of 3D geometric salient texels
BiometricAccessFilter: A Web Control Access System Based on Human Auditory Perception for Children Protection
Along with internet growth, security issues come into existence. Efficient tools to control access and to filter undesirable web content are needed all the time. In this paper, a control access method for web security based on age estimation is proposed, where the correlation between human age and auditory perception is taken into account. In particular, access is denied if a person’s age is not appropriate for the given web content. Unlike existing web access filters, our biometric approach offers greater security and protection to individual privacy. From a technical point of view, the machine-learning regression model is used to estimate the person’s age. The primary contributions of this paper include an age estimation module based on human auditory perception and provision of an open-source web filter to prevent adults from accessing children web applications. The proposed system can also be used to limit the access of children to a webpage specially designed for adults. Our system is evaluated with a dataset collected from 201 persons with different ages from 06 to 60 years old, where it considered 109 male and 82 female volunteers. Results indicate that our system can estimate the age of a person with an accuracy of 97.04% and a root mean square error (RMSE) of 4.2 years. It presents significant performances in the verification scenario with an Equal Error Rate (EER) of 1.4%
Machine Learning-based Approaches for Post-Traumatic Stress Disorder Diagnosis using Video and EEG Sensors: A Review
According to the World Health Organization, approximately 6 in 100 people suffer from Post-traumatic stress disorder (PTSD) at some point in their lives. PTSD is a mental disorder with a set of symptoms observed in a person following exposure to a life-threatening event or witness a death. The primary characteristic of PTSD is the persistence over time of certain symptoms: flashbacks, traumatic nightmares, acute stress and symptoms of depression. In a stressful situation, the whole body goes into tension. This has an energy cost and affects the voice, breathing, daily gestures and the face and which are referred to in this survey paper as external symptoms.
The diagnosis of PTSD is based on the Diagnostic and Statistical Manual of Mental Disorders (also known by the acronym DSM) classification using standard questionnaires where the patient self-reports their condition based on their symptoms. Indeed, these questionnaires have several limitations and give an imprecise diagnosis.Â
Sensors and wearable based technologies can play a key role in improving the diagnosis, the prognosis and the assistance of PTSD. Recently, Computer-Aided Diagnosis (CAD) systems have been proposed for PTSD detection based on external symptoms and brain activity using video and EEG sensors. To the best of our knowledge, this is the first survey paper that gives a literature overview of machine learning based approaches for PTSD diagnosis using video and EEG sensors. In addition, a comparison between existing approaches and a discussion about the potential avenues for future PTSD research is provided.</p