Emotion Detection Algorithm Research Articles

Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases.

EmoTunes: Your Emotional Soundtrack

The human face is an important organ of an individual’s body and it especially plays an important role in extraction of an individual‘s behavior and emotional state. Manually segregating the list of songs and generating an appropriate playlist based on an individual’s emotional features is a very tedious, time consuming, labor intensive and upheld task. Various algorithms have been proposed and developed for automating the playlist generation process.However the proposed existing algorithms in use are computationally slow, less accurate and sometimes even require use of additional hardware like EEG or sensors. This proposed system based on facial expression extracted will generate a playlist automatically thereby reducing the effort and time involved in rendering the process manually. Thus the proposed system tends to reduce the computational time involved in obtaining the results and the overall cost of the designed system, thereby increasing the overall accuracy of the system. Testing of the system is done on both user dependent (dynamic) and user independent (static) dataset. Facial expressions are captured using an inbuilt camera. The accuracy of the emotion detection algorithm used in the system for real time images is around 85-90%, while for static images it is around 98- 100%.The proposed algorithm on an average calculated estimation takes around 0.95-1.05 sec to generate an emotion based music playlist. Thus, it yields better accuracy in terms of performance and computational time and reduces the designing cost, compared to the algorithms used in the literature survey. Keywords—Audio Emotion Recognition, Music Information Retrieval, Emotion Extraction Module, Audio Feature Extraction Module, Artificial Neural Networks, Confusion Matrix, Viola and Jones Face Detection

Facial Expression Based Music Recommendation System

The human face is a crucial organ for conveying an individual's emotional state and behavior. However, manually creating a playlist based on an individual's emotional features can be a labor-intensive and time-consuming task. To automate this process, several algorithms have been proposed, but they are often slow and inaccurate. To address this, a new system is proposed that utilizes facial expression extraction to generate an appropriate playlist automatically. This system can significantly reduce the computational time and overall cost of playlist generation while increasing accuracy. The system captures facial expressions using an inbuilt camera, and the emotion detection algorithm used has an accuracy of approximately 85-90% for real-time images and 98-100% for static images. By leveraging this high level of accuracy and performance, the proposed system outperforms existing algorithms used in the literature survey. Based on the detected emotion, the system creates a playlist that matches the individual's emotional state. This novel approach offers a more efficient and accurate way to generate personalized playlists, ultimately saving time and effort for users

Platform-centric vs. multi-party service failure: an examination of consumers’ negative word of mouth about sharing economy platforms

PurposeThis paper aims to identify platform-centric versus multiparty service failure on sharing economy platforms via topic modeling analysis of consumers’ negative online reviews. The authors also sought to understand consumers’ reactions to these experiences by detecting negative discrete emotions. The authors then contrasted consumers’ responses to platform-centric and multiparty service failure through the theoretical lens of failure controllability.Design/methodology/approachThe authors used a large-scale data set containing more than 81,000 negative app reviews on eight representative hospitality and tourism sharing economy platforms. Topic modeling coupled with emotion detection algorithms revealed 11 themes reflecting diverse forms of platform-centric versus multiparty service failure and their associations with negative discrete emotions based on regression analysis.FindingsThe 11 themes reflecting diverse forms of platform-centric versus multiparty service failure were as follows: app glitch, customer service, locating and pooling, account issues, transaction, offer redemption, interface challenges, intermediary inaction, service lateness and cancellation, incorrect order and fee structure. The analysis suggests that platform-centric service failure is more likely than multiparty service failure to elicit negative discrete emotions.Originality/valueThe research enriches the understanding of platform-related service failure beyond dyadic service interaction. In particular, the authors bring to light two forms of platform-related service failure that warrant scholarly attention: platform-centric versus multiparty service failure. By uncovering the distinct negative emotional associations of platform-centric versus multiparty service failure, the research adds novel empirical evidence to the service failure literature and the relevant attribution theory. Findings offer long-term implications for the sustainable development of sharing economies and platform businesses in contemporary hospitality.

Automated Emotion Identification Using Fourier-Bessel Domain-Based Entropies.

Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

Multi-Channel EEG Emotion Recognition Based on Parallel Transformer and 3D-Convolutional Neural Network

Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based emotion recognition focuses on exploiting temporal, spatial, and spatiotemporal EEG data for emotion recognition. Due to the lack of consideration of both spatial and temporal aspects of EEG data, the accuracy of EEG emotion detection algorithms employing solely spatial or temporal variables is low. In addition, approaches that use spatiotemporal properties of EEG for emotion recognition take temporal and spatial characteristics of EEG into account; however, these methods extract temporal and spatial information directly from EEG data. Since there is no reconstruction of the EEG data format, the temporal and spatial properties of the EEG data cannot be extracted efficiently. To address the aforementioned issues, this research proposes a multi-channel EEG emotion identification model based on the parallel transformer and three-dimensional convolutional neural networks (3D-CNN). First, parallel channel EEG data and position reconstruction EEG sequence data are created separately. The temporal and spatial characteristics of EEG are then retrieved using transformer and 3D-CNN models. Finally, the features of the two parallel modules are combined to form the final features for emotion recognition. On the DEAP, Dreamer, and SEED databases, the technique achieved greater accuracy in emotion recognition than other methods. It demonstrates the efficiency of the strategy described in this paper.

Efficient regional multi feature similarity measure based emotion detection system in web portal using artificial neural network

Emotion detection from facial expression has been well studied. There are numerous techniques has been discussed for the accuracy of emotion detection, however the methods suffer with higher false classification ratio. Towards the development of emotion detection, a novel region based multi feature similarity approach has been presented in this article. Considering, shape and geometry measure alone would not acquire higher performance in the classification. It is necessary to consider and combine multiple features towards the problem. With this motivation, the proposed Regional Multi Feature Similarity (RMFS) based emotion detection algorithm enhances the input facial image and extracts shape feature, geometry feature and wrinkle features with colors are considered. Extracted features are trained with neural network. At the classification stage, MFS measure has been estimated towards the features of various emotion class in different layers of neural network. Finally, a single one has been classified as result using artificial neural network. The proposed method improves the performance of emotion detection with reduced false ratio.

A new emotion detection algorithm using extracted features of the different time-series generated from ST intervals Poincaré map

Emotion recognition using biological signals plays an essential role in studying psychological states. In many studies, the distinct effect of each physiological signal usually is ignored. By limiting the physiological signals and just evaluating the ECG signals, the paper aims to study the dynamical behavior of the Poincaré map in the form of the feature extraction from the generated time-series. For this purpose, the detection of emotional states in the two-dimensional model of emotion (Arousal-Valence) is utilized using the electrocardiogram signals recorded on the MAHNOB-HCI tagging database. So after signal processing, the waves of Q, R, S, and T are detected by processing the ECG signals using the Pan-Tompkins algorithm. The Poincaré map is adopted for the RR, QT, and ST intervals, then five different time-series are extracted from this mapping, subsequently, various features are extracted from these different time-series in the time domain, frequency domain, time-frequency domain, and nonlinear domain analysis. Finally, the classification of emotional states is performed using three classifiers: KNN, SVM, and MLP. In this study, the extracted optimal features of the different time-series generated from Poincaré map of the ST Intervals are achieved the best average accuracies of 82.17 % ± 4.73 and 78.07 % ± 3.59 in the arousal and valence model, respectively. The superiority of the obtained results from the extracted features of the five different time-series generated from ST-Intervals Poincaré map expresses that in comparison with RR and QT Intervals, the ST Intervals are more affected by ANS response to the emotional stimuli.

Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques.

BackgroundEmotions affect our mental health: they influence our perception, alter our physical strength, and interfere with our reason. Emotions modulate our face, voice, and movements. When emotions are expressed through the voice or face, they are difficult to measure because cameras and microphones are not often used in real life in the same laboratory conditions where emotion detection algorithms perform well. With the increasing use of smartphones, the fact that we touch our phones, on average, thousands of times a day, and that emotions modulate our movements, we have an opportunity to explore emotional patterns in passive expressive touches and detect emotions, enabling us to empower smartphone apps with emotional intelligence.ObjectiveIn this study, we asked 2 questions. (1) As emotions modulate our finger movements, will humans be able to recognize emotions by only looking at passive expressive touches? (2) Can we teach machines how to accurately recognize emotions from passive expressive touches?MethodsWe were interested in 8 emotions: anger, awe, desire, fear, hate, grief, laughter, love (and no emotion). We conducted 2 experiments with 2 groups of participants: good imagers and emotionally aware participants formed group A, with the remainder forming group B. In the first experiment, we video recorded, for a few seconds, the expressive touches of group A, and we asked group B to guess the emotion of every expressive touch. In the second experiment, we trained group A to express every emotion on a force-sensitive smartphone. We then collected hundreds of thousands of their touches, and applied feature selection and machine learning techniques to detect emotions from the coordinates of participant’ finger touches, amount of force, and skin area, all as functions of time.ResultsWe recruited 117 volunteers: 15 were good imagers and emotionally aware (group A); the other 102 participants formed group B. In the first experiment, group B was able to successfully recognize all emotions (and no emotion) with a high 83.8% (769/918) accuracy: 49.0% (50/102) of them were 100% (450/450) correct and 25.5% (26/102) were 77.8% (182/234) correct. In the second experiment, we achieved a high 91.11% (2110/2316) classification accuracy in detecting all emotions (and no emotion) from 9 spatiotemporal features of group A touches.ConclusionsEmotions modulate our touches on force-sensitive screens, and humans have a natural ability to recognize other people’s emotions by watching prerecorded videos of their expressive touches. Machines can learn the same emotion recognition ability and do better than humans if they are allowed to continue learning on new data. It is possible to enable force-sensitive screens to recognize users’ emotions and share this emotional insight with users, increasing users’ emotional awareness and allowing researchers to design better technologies for well-being.

The impact of weak ground truth and facial expressiveness on affect detection accuracy from time-continuous videos of facial expressions

In this paper we address two issues concerning real-world time-continuous emotion detection from videos of users’ faces: (i) the impact of weak ground truth on the emotion detection accuracy and (ii) the impact of the users’ facial expressiveness on the emotion detection accuracy. We implemented an appearance-based emotion detection algorithm that uses Gabor features and a k nearest neighbors classifier. We tested the performance of this algorithm on two datasets with different ground truth strengths (a firm ground truth dataset and a weak ground truth dataset). Then we split the dataset into three subsets reflecting different levels of users’ facial expressiveness (low, mid and high) and performed separate emotion detection.

The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata

We present the LDOS-PerAff-1 Corpus that bridges the affective computing and recommender system research areas, which makes it unique. The corpus is composed of video clips of subjects’ affective responses to visual stimuli. These affective responses are annotated in the continuous valence-arousal-dominance space. Furthermore, the subjects are annotated with their personality information using the five-factor personality model. We also provide the explicit ratings that the users gave to the images used for the visual stimuli. In the paper we present the results of four experiments conducted with the corpus; an affective content-based recommender system, a personality-based collaborative filtering recommender system, an emotion-detection algorithm and a qualitative study of the latent factors.

Object oriented architecture for affective multimodal e-learning interfaces

This paper focuses on the architecture of affective multimodal user interfaces that exist in e-learning applications. The OO (Object Oriented) approach has been adopted in order to combine evidence from multiple modalities of interaction and data from emotion stereotypes and classify them into well structured objects with their own properties and methods. The proposed architecture can be adopted in future multi-purpose emotion recognition systems with multiple modalities and improved emotion detection algorithms. Furthermore, the resulting emotion detection server is capable of using and handling transmitted information from different sources of data during human-computer interaction. The evaluation of the OO architecture for affective interaction by programmers and teachers revealed that there are considerable improvements in the resulting e-learning system, including easiness in adding a modality of interaction, better structure for the available multi-modal information and user friendlier environment for the human-computer interaction. The evaluation of the resulting system provided quite satisfactory results considering the proposed affective multimodal architecture.