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

Visual and acoustic techniques for motorcycle collision warning system with EEG validation

Dans de nombreux pays, le taux de mortalité des motocyclistes est beaucoup plus élevé que celui des autres conducteurs de véhicules. Parmi de nombreux autres facteurs, les collisions arrière des motocyclettes contribuent fortement à ces décès de motards. Les systèmes de détection de collision peuvent être utilisés pour minimiser ces accidents mortels. Cependant, la plupart des systèmes de détection de collision existants n'identifient pas le type de danger potentiel auquel sont exposés les motocyclistes. Chaque système d'alerte de collision utilise une technique de détection de collision distincte, ce qui limite ses performances et rend impératif l'étude de son efficacité. Malheureusement, aucun travail de ce type n'a été signalé dans ce domaine particulier pour les motocyclistes. Par conséquent, il est important d'étudier la réponse physiologique du motocycliste contre ces systèmes d'alerte de collision. Dans cette recherche, une méthode de détection et de classification des véhicules approchant par l'arrière est présentée. Pour la détection de collision, une approche basee vision et la technique basee sur le son ont été utilisées. Pour les techniques visuelles et acoustiques, des caractéristiques d'apparence et de spectre de puissance ont été utilisées, respectivement, pour détecter le véhicule qui s'approche à l'extrémité arrière de la motocyclette. En ce qui concerne la classification des véhicules, seule une technique acoustique est utilisée; un spectre de puissance acoustique et des caractéristiques énergétiques sont utilisés pour classer les véhicules qui approchent. Deux types d'ensembles de données, à savoir des ensembles de données acquises durant ce travail (obtenues en plaçant une caméra à l'arrière d'une motocyclette) et des ensembles de données disponibles telechargeables (pour la détection visuelle et pour la classification audio des véhicules) sont utilisés pour la validation. La méthodologie proposée a permis de détecter et de classer les véhicules pour des ensembles de données acquises durent cette these. De même, pour les ensembles de données disponibles , le taux positif vrai le plus élevé et le taux de détection faux le plus faible ont été atteints par rapport aux méthodes de l etat de l art. En outre, une étude physiologique basée sur le potentiel lié à l'événement (ERP) a été réalisée sur les motocyclistes afin d'étudier leurs réponses vis-à-vis du système d'alerte de collision arrière. Deux types d'avertissements auditifs (c'est-à-dire verbal et buzzer) sont utilisés pour ce système d'avertissement. Pour étudier la réponse des motocyclistes, les composantes N1, N2, P3 et N400 ont été extraits des données d'électroencéphalographie (EEG). Ces systèmes d avertissement ont montré des effets positifs au niveau des neuronal sur les motocyclistes et réduisent leur temps de réaction et les ressources attentionnelles nécessaires pour traiter correctement la cible. En résumé, le système d'avertissement de collision par l'arrière proposé avec des avertissements verbaux auditifs augmente considérablement la vigilance du motocycliste et peut être utile pour éviter les scénarios possibles de collision arrière.In many countries, motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. Collision detection systems can be used to minimize these fatalities. However, most of the existing collision detection systems do not identify the type of potential hazard faced by motorcyclists. Every collision warning system used a distinctive collision detection technique, which limits its performance and makes it imperative to study its effectiveness. Unfortunately, no such work has been reported in that particular domain for motorcyclists. Therefore, it is important to study the physiological response of the motorcyclist against these collision warning systems. In this research, a rear end vehicle detection and classification method is presented for motorcyclists. For collision detection, vision technique and acoustic technique have been used. For visual and acoustic techniques, appearance features and power spectrum have been used, respectively, to detect the approaching vehicle at the rear end of the motorcycle. As for the vehicle classification, only an acoustic technique is utilized; an acoustic power spectrum and energy features are used to classify the approaching vehicles. Two types of datasets which are comprised of self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and online datasets (for vision-based vehicle detection and for audio based vehicle classification techniques) are used for validation. Proposed methodology successfully detected and classified the vehicle for self-recorded datasets. Similarly, for online datasets, the higher true positive rate and less false detection rate has been achieved as compared to the existing state of the art methods. Moreover, an event-related potential (ERP) based physiological study has been performed on motorcyclists to investigate their responses towards the rear end collision warning system. Two types of auditory warnings (i.e., verbal and buzzer) are used for this warning system. To study the response of the motorcyclists, the N1, N2, P3, and N400 components have been extracted from the Electroencephalography (EEG) data. These introduced systems have shown positive effects at neural levels on motorcyclists and reduce their reaction time and attentional resources required for processing the target correctly. In summary, the proposed rear-end collision warning system with auditory verbal warnings significantly increases the alertness of the motorcyclist and can be helpful to avoid the possible rear-end collision scenarios

Visual and Acoustic based Techniques for Motorcycle Collision Warning System with EEG Validation

In many countries, motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. Collision detection systems can be used to minimize these fatalities. However, most of the existing collision detection systems do not identify the type of potential hazard faced by motorcyclists

Développement d'un système d'avertissemment sonore, validé par EEG, basé sur des approches vision et acoustique pour la detection de véhicules approchants des véhicules moteur deux roues

In many countries, motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. Collision detection systems can be used to minimize these fatalities. However, most of the existing collision detection systems do not identify the type of potential hazard faced by motorcyclists. Every collision warning system used a distinctive collision detection technique, which limits its performance and makes it imperative to study its effectiveness. Unfortunately, no such work has been reported in that particular domain for motorcyclists. Therefore, it is important to study the physiological response of the motorcyclist against these collision warning systems. In this research, a rear end vehicle detection and classification method is presented for motorcyclists. For collision detection, vision technique and acoustic technique have been used. For visual and acoustic techniques, appearance features and power spectrum have been used, respectively, to detect the approaching vehicle at the rear end of the motorcycle. As for the vehicle classification, only an acoustic technique is utilized; an acoustic power spectrum and energy features are used to classify the approaching vehicles. Two types of datasets which are comprised of self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and online datasets (for vision-based vehicle detection and for audio based vehicle classification techniques) are used for validation. Proposed methodology successfully detected and classified the vehicle for self-recorded datasets. Similarly, for online datasets, the higher true positive rate and less false detection rate has been achieved as compared to the existing state of the art methods. Moreover, an event-related potential (ERP) based physiological study has been performed on motorcyclists to investigate their responses towards the rear end collision warning system. Two types of auditory warnings (i.e., verbal and buzzer) are used for this warning system. To study the response of the motorcyclists, the N1, N2, P3, and N400 components have been extracted from the Electroencephalography (EEG) data. These introduced systems have shown positive effects at neural levels on motorcyclists and reduce their reaction time and attentional resources required for processing the target correctly. In summary, the proposed rear-end collision warning system with auditory verbal warnings significantly increases the alertness of the motorcyclist and can be helpful to avoid the possible rear-end collision scenarios.Dans de nombreux pays, le taux de mortalité des motocyclistes est beaucoup plus élevé que celui des autres conducteurs de véhicules. Parmi de nombreux autres facteurs, les collisions arrière des motocyclettes contribuent fortement à ces décès de motards. Les systèmes de détection de collision peuvent être utilisés pour minimiser ces accidents mortels. Cependant, la plupart des systèmes de détection de collision existants n'identifient pas le type de danger potentiel auquel sont exposés les motocyclistes. Chaque système d'alerte de collision utilise une technique de détection de collision distincte, ce qui limite ses performances et rend impératif l'étude de son efficacité. Malheureusement, aucun travail de ce type n'a été signalé dans ce domaine particulier pour les motocyclistes. Par conséquent, il est important d'étudier la réponse physiologique du motocycliste contre ces systèmes d'alerte de collision. Dans cette recherche, une méthode de détection et de classification des véhicules approchant par l'arrière est présentée. Pour la détection de collision, une approche basee vision et la technique basee sur le son ont été utilisées. Pour les techniques visuelles et acoustiques, des caractéristiques d'apparence et de spectre de puissance ont été utilisées, respectivement, pour détecter le véhicule qui s'approche à l'extrémité arrière de la motocyclette. En ce qui concerne la classification des véhicules, seule une technique acoustique est utilisée; un spectre de puissance acoustique et des caractéristiques énergétiques sont utilisés pour classer les véhicules qui approchent. Deux types d'ensembles de données, à savoir des ensembles de données acquises durant ce travail (obtenues en plaçant une caméra à l'arrière d'une motocyclette) et des ensembles de données disponibles telechargeables (pour la détection visuelle et pour la classification audio des véhicules) sont utilisés pour la validation. La méthodologie proposée a permis de détecter et de classer les véhicules pour des ensembles de données acquises durent cette these. De même, pour les ensembles de données disponibles , le taux positif vrai le plus élevé et le taux de détection faux le plus faible ont été atteints par rapport aux méthodes de l etat de l art. En outre, une étude physiologique basée sur le potentiel lié à l'événement (ERP) a été réalisée sur les motocyclistes afin d'étudier leurs réponses vis-à-vis du système d'alerte de collision arrière. Deux types d'avertissements auditifs (c'est-à-dire verbal et buzzer) sont utilisés pour ce système d'avertissement. Pour étudier la réponse des motocyclistes, les composantes N1, N2, P3 et N400 ont été extraits des données d'électroencéphalographie (EEG). Ces systèmes d avertissement ont montré des effets positifs au niveau des neuronal sur les motocyclistes et réduisent leur temps de réaction et les ressources attentionnelles nécessaires pour traiter correctement la cible. En résumé, le système d'avertissement de collision par l'arrière proposé avec des avertissements verbaux auditifs augmente considérablement la vigilance du motocycliste et peut être utile pour éviter les scénarios possibles de collision arrière

Rear-end vision-based collision detection system for motorcyclists

International audienceIn many countries, the motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. To increase the safety of motorcyclists and minimize their road fatalities, this paper introduces a vision-based rear-end collision detection system. The binary road detection scheme contributes significantly to reduce the negative false detections and helps to achieve reliable results even though shadows and different lane markers are present on the road. The methodology is based on Harris corner detection and Hough transform. To validate this methodology, two types of dataset are used: (1) self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and (2) online datasets (recorded by placing a camera at the front of a car). This method achieved 95.1% accuracy for the self-recorded dataset and gives reliable results for the rear-end vehicle detections under different road scenarios. This technique also performs better for the online car datasets. The proposed technique's high detection accuracy using a monocular vision camera coupled with its low computational complexity makes it a suitable candidate for a motorbike rear-end collision detection system. (C) 2017 SPIE and IS&T

Event-Related Potential Responses of Motorcyclists Towards Rear End Collision Warning System

International audienceThere are many types of collision warning systems to increase the safety of motorcyclists. These systems use different types of collision detection techniques with each one having some limitations, restricting the performance of the system. To find the effectiveness of collision warning system notifications, it is important to study the physiological response of drivers toward these systems. Existing studies are limited to the physiological response of car drivers and use only buzzer warnings for these systems. Unfortunately, no such work in that particular domain has been reported for motorcyclists. Since motorcycles have different maneuverability as compared with cars and other vehicles, it is important to investigate the response of motorcyclists toward these collision warning systems. Also, it is believed that providing verbal information about any potential hazard will further assist the motorcyclist to avoid it. The aim of this paper is to investigate the physiological responses of motorcyclists to the rear end collision warning system when auditory verbal warnings are utilized. To study the response of the motorcyclists, the N100, N200, P300, and N400 eventrelated potential components have been extracted from the recorded Electroencephalography data. It has been found that the rear end collision warning system with auditory verbal warnings significantly increases the alertness of the motorcyclist and can be helpful to avoid the possible rear-end collision scenarios. This system has shown positive effects at neural levels on motorcyclists and reduces their reaction time and attentional resources required for processing the target correctly

Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture

Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications

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

Seasonal variation in aflatoxins levels in edible seeds, estimation of its dietary intake and vitamin E levels from Southern Areas of Punjab, Pakistan

A total of 779 samples of edible nuts (melon seeds, watermelon seeds, pumpkin seeds, and cantaloupe seeds) from Southern Punjab (Pakistan), were collected during the summer and the winter seasons. The natural occurrence of aflatoxins (AFs) and vitamin E (tocopherols) levels were investigated using HPLC. The results have shown that 180 (43.4%) of samples from the winter season and 122 (33.4%) samples from the summer season were found positive for AFs. Elevated average levels of total AFs (20.9 ± 3.10 μg/kg, dry weight) were observed in watermelon seeds without shell, and the lowest average amount (15.9 ± 3.60 μg/kg) were documented in melon seeds without shell samples from the winter season. An elevated average amount of total AFs 17.3 ± 1.50 μg/kg was found in pumpkin seeds available without a shell. The results have documented a significant difference in total AFs levels in edible seeds available with shells versus without shells (α = 0.05 & 0.01). The highest dietary intake of 6.30 μg/kg/day was found in female individuals from consuming pumpkin seeds (without shell) in the winter season. A value of 3.00 μg/kg/day was found in pumpkin seed without shell in the summer season in female individuals. The highest total tocopherol levels were 22.2 ± 7.70 ng/100 g in pumpkin seeds samples from the winter season and 14.5 ± 5.50 mg/100 g in melon seed samples from the summer season. The variation of total tocopherol levels in edible seeds among the winter and summer seasons showed a significant difference (p ≤ 0.0054), except watermelon seeds samples with non-significant differences (p ≥ 0.183)