Sound-Based Recognition of Touch Gestures and Emotions for Enhanced Human-Robot Interaction

Emotion recognition and touch gesture decoding are crucial for advancing human-robot interaction (HRI), especially in social environments where emotional cues and tactile perception play important roles. However, many humanoid robots, such as Pepper, Nao, and Furhat, lack full-body tactile skin, limiting their ability to engage in touch-based emotional and gesture interactions. In addition, vision-based emotion recognition methods usually face strict GDPR compliance challenges due to the need to collect personal facial data. To address these limitations and avoid privacy issues, this paper studies the potential of using the sounds produced by touching during HRI to recognise tactile gestures and classify emotions along the arousal and valence dimensions. Using a dataset of tactile gestures and emotional interactions from 28 participants with the humanoid robot Pepper, we design an audio-only lightweight touch gesture and emotion recognition model with only 0.24M parameters, 0.94MB model size, and 0.7G FLOPs. Experimental results show that the proposed model effectively recognises the arousal and valence states of different emotions, as well as various tactile gestures, when the input audio length varies. The proposed model is of low-latency and achieves similar results as well-known pretrained audio neural networks (PANNs), but with much smaller FLOPs, number of parameters, and model size.