Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,an...Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.展开更多
Leucine rich repeat proteins have gained considerable interest as therapeutic targets due to their expression and biological activity within the central nervous system. LINGO-1 has received particular attention since ...Leucine rich repeat proteins have gained considerable interest as therapeutic targets due to their expression and biological activity within the central nervous system. LINGO-1 has received particular attention since it inhibits axonal regeneration after spinal cord injury in a Rho A dependent manner while inhibiting leucine rich repeat and immunoglobulin-like domain-containing protein 1（LINGO-1） disinhibits neuron outgrowth. Furthermore, LINGO-1 suppresses oligodendrocyte precursor cell maturation and myelin production. Inhibiting the action of LINGO-1 encourages remyelination both in vitro and in vivo. Accordingly, LINGO-1 antagonists show promise as therapies for demyelinating diseases. An analogous protein to LINGO-1, amphoterin-induced gene and open reading frame-3（AMIGO3）, exerts the same inhibitory effect on the axonal outgrowth of central nervous system neurons, as well as interacting with the same receptors as LINGO-1. However, AMIGO3 is upregulated more rapidly after spinal cord injury than LINGO-1. We speculate that AMIGO3 has a similar inhibitory effect on oligodendrocyte precursor cell maturation and myelin production as with axogenesis. Therefore, inhibiting AMIGO3 will likely encourage central nervous system axonal regeneration as well as the production of myelin from local oligodendrocyte precursor cell, thus providing a promising therapeutic target and an area for future investigation.展开更多
文摘Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.
基金supported by a grant from The University of Birmingham
文摘Leucine rich repeat proteins have gained considerable interest as therapeutic targets due to their expression and biological activity within the central nervous system. LINGO-1 has received particular attention since it inhibits axonal regeneration after spinal cord injury in a Rho A dependent manner while inhibiting leucine rich repeat and immunoglobulin-like domain-containing protein 1（LINGO-1） disinhibits neuron outgrowth. Furthermore, LINGO-1 suppresses oligodendrocyte precursor cell maturation and myelin production. Inhibiting the action of LINGO-1 encourages remyelination both in vitro and in vivo. Accordingly, LINGO-1 antagonists show promise as therapies for demyelinating diseases. An analogous protein to LINGO-1, amphoterin-induced gene and open reading frame-3（AMIGO3）, exerts the same inhibitory effect on the axonal outgrowth of central nervous system neurons, as well as interacting with the same receptors as LINGO-1. However, AMIGO3 is upregulated more rapidly after spinal cord injury than LINGO-1. We speculate that AMIGO3 has a similar inhibitory effect on oligodendrocyte precursor cell maturation and myelin production as with axogenesis. Therefore, inhibiting AMIGO3 will likely encourage central nervous system axonal regeneration as well as the production of myelin from local oligodendrocyte precursor cell, thus providing a promising therapeutic target and an area for future investigation.
