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                <term xml:lang="en">Machine Learning</term>
                <term xml:lang="en">Physiological signals</term>
                <term xml:lang="en">Emotion recognition</term>
                <term xml:lang="en">Stationary sensors</term>
                <term xml:lang="en">Laboratory sensors</term>
                <term xml:lang="en">Wearable sensors</term>
                <term xml:lang="fr">Capteurs</term>
                <term xml:lang="fr">Signaux physiologiques</term>
                <term xml:lang="fr">Emotions</term>
                <term xml:lang="fr">Emotion</term>
                <term xml:lang="fr">Reconnaissance des émotions</term>
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              <p>Emotion recognition is an important research topic. Physiological signals seem to be an appropriate way for emotion recognition and specific sensors are required to collect these data. Therefore, laboratory sensors are commonly used while the number of wearable devices including similar physiological sensors is growing up. Many studies have been completed to evaluate the signal quality obtained by these sensors but without focusing on their emotion recognition capabilities. In the current study, Machine Learning models were trained to compare the Biopac MP150 (laboratory sensor) and Empatica E4 (wearable sensor) in terms of emotion recognition accuracy. Results show similar accuracy between data collected using laboratory and wearable sensors. These results support the reliability of emotion recognition outside laboratory.</p>
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              <p>La reconnaissance des émotions est un important sujet de recherche. Les signaux physiologiques semblent être un moyen approprié pour la reconnaissance des émotions. Des capteurs spécifiques sont nécessaires pour collecter ces données. Par conséquent, les capteurs de laboratoire sont généralement utilisés alors que le nombre de dispositifs portables incluant les mêmes capteurs physiologiques est en pleine expansion. De nombreuses études ont été effectuées afin d'évaluer la qualité du signal obtenu par ces capteurs mais sans pour autant se focaliser sur leur capacité en termes de reconnaissances des émotions. Dans la présente étude, des modèles de Machine Learning ont été entraînés afin de comparer le Biopac MP150 (capteur de laboratoire) et l'Empatica E4 (capteur portable) en termes de précisions au niveau de la reconnaissance émotionnelle. Les résultats témoignent d'une similarité de précision entre les données collectées par les capteurs de laboratoire et portable. Ces résultats confirment la fiabilité et la possibilité d'une reconnaissance des émotions hors laboratoire.</p>
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