Samenvatting
Early identification of emotions of software developers can enable timely intervention in order to support developers' well-being and prevent burnout. We present a machine learning experiment aimed at recognizing emotions during programming tasks using wearable biometric sensors, tracking electrodermal activity and heart-related metrics.
As a gold standard for supervised learning, we rely on a state-of-the-art tool for emotion recognition based on facial expression analysis. We design, implement and evaluate an approach that combines the output of two classifiers for neutral valence recognition and positive/negative polarity classification.
Our findings suggest that biometric sensors in a wristband can be used to identify emotions whose recognition would otherwise need an intrusive webcam.
As a gold standard for supervised learning, we rely on a state-of-the-art tool for emotion recognition based on facial expression analysis. We design, implement and evaluate an approach that combines the output of two classifiers for neutral valence recognition and positive/negative polarity classification.
Our findings suggest that biometric sensors in a wristband can be used to identify emotions whose recognition would otherwise need an intrusive webcam.
| Originele taal-2 | Engels |
|---|---|
| Pagina's | 1-8 |
| Status | Gepubliceerd - 18 okt. 2022 |
| Evenement | International Conference on Affective Computing and Intelligent Interaction - Nara, Japan Duur: 18 okt. 2022 → 21 okt. 2022 Congresnummer: 10 https://acii-conf.net/2022/ |
Congres
| Congres | International Conference on Affective Computing and Intelligent Interaction |
|---|---|
| Verkorte titel | ACII |
| Land/Regio | Japan |
| Stad | Nara |
| Periode | 18/10/22 → 21/10/22 |
| Internet adres |