Cumulative video analysis based smart framework for detection of depression disorders
16 Apr 2015-pp 1-5
TL;DR: In this paper, a novel cumulative video analysis based on action units and fictional points for analysis of facial moment is proposed for detection of depression disorders through gesture recognition, and appropriate action is taken according to scale of the depression in the patient and the severity of it.
Abstract: Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning which can hamper the lives of individuals. The absence of any objective diagnostic aid for depression leads to a range of biases in the diagnosis and ongoing monitoring. This study throws light upon the contribution of gestures and facial points for depression analysis. This paper discusses a novel cumulative video analysis proposed by us based on action units and fictional points for analysis of facial moment. Experimental results are carried out using real world clinical data and interactive sessions with neuro experts. This smart framework developed by us is useful for detection of depression disorders through gesture recognition. The diagnosis is done and appropriate action is taken according to scale of the depression in the patient and the severity of it.
Citations
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TL;DR: This study proposed an approach for short-term detection of mood disorders based on elicited speech responses and found that CNN- and LSTM-based attention models improved the mood disorder detection accuracy of the proposed method by approximately 11%.
49 citations
23 Mar 2016
TL;DR: The proposed work presents a framework to monitor the depression patient with the help of their daily physical activities, posture movement, location detection, behavioral changes, and significant biomedical parameter changes using WBAN sensors.
Abstract: Wireless Body Area Networks consist of low power lightweight wearable and/or implantable sensor nodes that are often placed remotely for applications like ubiquitous health care, military, sports, entertainment and many other areas. Depression is becoming a common problem in human beings. In this paper the proposed work presents a framework to monitor the depression patient with the help of their daily physical activities, posture movement, location detection, behavioral changes, and significant biomedical parameter changes using WBAN sensors. It monitors the patient, generates alarm and notifies the caregiver if any abnormal situation occurs.
9 citations
TL;DR: A comprehensive review of contactless sensing methods for mental health monitoring is presented in this article , where the authors categorize the applications of these methods into detection, recognition, and monitoring of vital signs.
Abstract: The process of monitoring mental health has relied on methods, such as invasive sensing and self-reporting. The use of these methods has been limited because of the invasiveness of sensing devices or the subjective nature of patients’ responses. Recent research focuses on the contactless sensing methods used to objectively monitor mental health issues. These methods allow continuous collection of real-time data in a nondisruptive manner. Machine learning methods are then applied to the sensed data to predict information, such as physical activity, gestures, and heart rate. This information can be then used to assess mental health issues, such as depression, stress, and anxiety, among others. This article presents a comprehensive review of contactless sensing methods for mental health monitoring. It investigates the published research that focuses on contactless sensing methods to predict mental health condition. Moreover, this review categorizes the applications of contactless sensing methods into detection, recognition, and monitoring of vital signs. Furthermore, a comparison of recent studies on contactless sensing methods is presented, which shows the effectiveness and reliability of these methods. This study also highlights the existing challenges in contactless sensing methods and provides future research directions to mitigate these challenges.
5 citations
06 Jul 2021
TL;DR: In this paper, a survey about different techniques and machine learning models for depression detection is presented, where features of different signals are extracted and further handled to develop a machine learning model.
Abstract: The most common psychiatric disorder is Clinical Depression. More than fifteen percent of people undergo an incident of major depression sometime during their life. There is an increase in depressive disorders and other-regarding symptoms in past years, which leads to the importance of early detection of depression. Detection of depression can be automated by analyzing a person's behaviour and emotions. The depressive disorder influences auditory speech qualities, facial expressions, thinking, and some cardiovascular activities; hence depression can be recognized by analyzing such factors and also trying to find out which method is more suitable and reliable. Through preprocessing, features of different signals are extracted and further handled to develop a machine learning model. Neural Networks are a faster and efficient way to analyze depression. This survey briefs about different techniques and machine learning models for depression detection.
22 May 2023
TL;DR: In this article , a novel depression detection method based on deep learning was proposed, which used deep neural network variants, such as 1D-CNN, 2DCNN, and BiLSTM, for speech depression detection.
Abstract: Computers can get insight into the user's mental state, including depression prediction, by analyzing speech signals. Numerous uses exist, ranging from customer service to depression-related suicide prevention. In this study, we proposed a novel depression detection method based on deep learning. Deep neural network variants, 1D-CNN, 2D-CNN, and BiLSTM, were utilized. This research developed a new speech depression dataset, namely the Sorrow Analysis Dataset. It is an English depression audio dataset of 64 recordings of depressed and non-depressed individuals. Results showed that of the various architectures tested, 1D-CNN was found to produce the highest average accuracy of 97% with 5-fold validation.
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
"Cumulative video analysis based sma..." refers methods in this paper
...In our system, we use PCA to reduce high dimensional features to lower ones, hence reducing the complexity....
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01 Jan 1978
3,840 citations
Additional excerpts
...Depression Analysis Flow Graph 978-1-4799-6272-3/15/$31.00(c)2015 IEEE II....
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TL;DR: This work proposes an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm and shows that the effects of appearance variation during fitting can be precomputed (“projected out”) using this algorithm and how it can be extended to include a global shape normalising warp.
Abstract: Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instances i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed (“projected out”) using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.
1,775 citations
"Cumulative video analysis based sma..." refers background in this paper
...Some of these will be processed and others discarded on basis of irregularity, lack of clarity, blur motions and head movements....
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08 Dec 2009
TL;DR: The findings suggest the feasibility of automatic detection of depression, raise new issues in automated facial image analysis and machine learning, and have exciting implications for clinical theory and practice.
Abstract: Current methods of assessing psychopathology depend almost entirely on verbal report (clinical interview or questionnaire) of patients, their family, or caregivers. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of psychological disorder, much of which may occur outside the awareness of either individual. We compared clinical diagnosis of major depression with automatically measured facial actions and vocal prosody in patients undergoing treatment for depression. Manual FACS coding, active appearance modeling (AAM) and pitch extraction were used to measure facial and vocal expression. Classifiers using leave-one-out validation were SVM for FACS and for AAM and logistic regression for voice. Both face and voice demonstrated moderate concurrent validity with depression. Accuracy in detecting depression was 88% for manual FACS and 79% for AAM. Accuracy for vocal prosody was 79%. These findings suggest the feasibility of automatic detection of depression, raise new issues in automated facial image analysis and machine learning, and have exciting implications for clinical theory and practice.
425 citations
Additional excerpts
...Depression Analysis Flow Graph 978-1-4799-6272-3/15/$31.00(c)2015 IEEE II....
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TL;DR: For instance, this paper found that individuals with a history of major depressive disorder (MDD) were more likely than those without current depressive symptomatology to control their initial smiles with negative affect-related expressions.
Abstract: Individuals suffering from depression show diminished facial responses to positive stimuli. Recent cognitive research suggests that depressed individuals may appraise emotional stimuli differently than do nondepressed persons. Prior studies do not indicate whether depressed individuals respond differently when they encounter positive stimuli that are difficult to avoid. The authors investigated dynamic responses of individuals varying in both history of major depressive disorder (MDD) and current depressive symptomatology (N = 116) to robust positive stimuli. The Facial Action Coding System (Ekman & Friesen, 1978) was used to measure affect-related responses to a comedy clip. Participants reporting current depressive symptomatology were more likely to evince affect-related shifts in expression following the clip than were those without current symptomatology. This effect of current symptomatology emerged even when the contrast focused only on individuals with a history of MDD. Specifically, persons with current depressive symptomatology were more likely than those without current symptomatology to control their initial smiles with negative affect-related expressions. These findings suggest that integration of emotion science and social cognition may yield important advances for understanding depression.
87 citations
"Cumulative video analysis based sma..." refers background in this paper
...In other findings we come across the use of smiles of individuals used as analysis parameters which are in relation to the contraction and expansion of facial muscles....
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