Exploring Bio-Behavioral Signal Trajectories of State Anxiety During Public Speaking

Nirjhar, Ehsanul Haque; Behzadan, Amir H.; Chaspari, Theodora

doi:10.1109/icassp40776.2020.9054160

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Comparison to The Existing Work: To the best of our knowledge, this is the first work for automated estimation of PA and NA scores using physiological signals. The reports closest to our proposed work are the continuous estimation of stress [10], [11] and anxiety [12] using wearable sensors. These reports showed a lower correlation when compared to our proposed NA estimation algorithm using CNN when fusing RESP+ACC (r=0.79) and our PA estimation using CNN with RESP+EMG (r=0.69), except for the work by Plarre et al [11] that reported a slightly higher correlation.…”

Section: Discussionmentioning

confidence: 91%

“…They report an 82% accuracy and a mean r 2 of 0.35. In another work, Nirjhar et al developed a temporal parametric model to estimate public speaking anxiety using speech signals and physiological signals from EDA and photoplethysmography signals [12]. The highest reported correlation was 0.37 (p<0.05).…”

Section: Discussionmentioning

confidence: 99%

“…As a result of missing such quantitative measurements, these emotion detection methods may not be used for tracking the amount of change in the affect scores or provide the inter-subject changes over time. Some other works estimate the severity levels of specific emotions such as stress [10], [11] and anxiety [12]. In this work, we estimated the PA and NA scores that show diverse emotions.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning

Hssayeni

Ghoraani

2021

IEEE Access

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Automated momentary estimation of positive and negative affects (PA and NA), the basic sense of feeling, can play an essential role in detecting the early signs of mood disorders. Physiological wearable sensors and machine learning have a potential to make such automated and continuous measurements. However, the physiological signals' features that are associated with the subject-reported PA or NA may not be known. In this work, we use data-driven feature extraction based on deep learning to investigate the application of raw physiological signals for estimating PA and NA. Specifically, we propose two multimodal data fusion methods with deep Convolutional Neural Networks. We use the proposed architecture to estimate PA and NA and also classify baseline, stress, and amusement emotions. The training and evaluation of the methods are performed using four physiological and one chest motion signal modalities collected using a chest sensing unit from 15 subjects. Overall, our proposed model performed better than traditional machine learning on hand-crafted features. Utilizing only two modalities, our proposed model estimated PA with a correlation of 0.69 (p<0.05) vs. 0.59 (p<0.05) with traditional machine learning. These correlations were 0.79 (p<0.05) vs. 0.73 (p<0.05) for NA estimation. The best emotion classification was achieved by the traditional method with 79% F1-score and 80% accuracy when all the four physiological modalities are used. This is while with only two modalities, the deep learning achieved 78% F1-score and 79% accuracy.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning

Hssayeni

Ghoraani

2021

IEEE Access

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“…On the other hand, regression has been applied to other related problems. For instance, in [ 19 ] the authors used statistical methods such as regression to predict anxiety based on wearable data (in this case BVP, ST, EDA, and microphone data), and in other studies it has been shown that continuous stress levels can be estimated based on regression and statistical methods (for instance [ 20 , 21 , 22 , 23 ]).…”

Section: Related Workmentioning

confidence: 99%

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Siirtola

Tamminen

Chandra

et al. 2023

Sensors

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This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors.

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