ArticleSensors (Basel, Switzerland)2023
Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Predicting Emotional Exhaustion with Multimodal Sensor Data During Return-to-Work Trajectories: A 6-Month Longitudinal Study.Journal of occupational rehabilitation · 2026Article
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- Bio-signal induced emotion monitoring and detection of anxiety: A sensor-driven approach with regression based random forest.MethodsX · 2025Article
- Development of Machine-Learning-Based Facial Thermal Image Analysis for Dynamic Emotion Sensing.Sensors (Basel, Switzerland) · 2025Article
- A deep learning approach to stress recognition through multimodal physiological signal image transformation.Scientific reports · 2025Article
- A Personalized Energy Expenditure Estimation Method Using Modified MET and Heart Rate-Based DQN.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG.Sensors (Basel, Switzerland) · 2024Article
- Improved EEG-based emotion recognition through information enhancement in connectivity feature map.Scientific reports · 2023Article
- Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications.Sensors (Basel, Switzerland) · 2023Article
- Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis.PloS one · 2023Article
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5 authors.
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Abstract
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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