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Traditional emotion\u2010sensing techniques often focus on singular emotions, limiting their ability to grasp the complexity of emotional experiences. Herein, a fully printed, organic wearable sensor capable of multimodal emotion sensing by noninvasively monitoring physiological indicators such as heart rate, breathing patterns, and voice signatures is presented. The recorded signals are processed using a long short\u2010term memory (LSTM) neural network, achieving over 91% classification accuracy in distinguishing different emotions through a data fusion approach, showing &gt;9% enhancement in accuracy as compared to feature fusion. As a proof of concept, Q\u2010learning is implemented with the data to simulate emotional responses in a robotic model. 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