Abstract
In this paper, we propose a framework called conversational partner inference using nonverbal information (abbreviated as CFN). We use the wrist-based wearable device that has an accelerometer sensor to detect the user’s hand movement. Besides, we propose three different methods, named leading CFN, trainling CFN and leading-trailing CFN, to integrate the detected movement behaviors with the sound data sensed by microphones to effectively infer conservational partners. In experiments, we collect real data to evaluate the proposed framework. The experimental results show that the accuracy of leading CFN is better than trailing CFN and leading-trailing CFN. Moreover, our approach shows higher accuracy than the state-of-the-art approach for conversational partner inference.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Mungtavesinsuk, D., Chen, YA., Wu, CW., Bajo, E., Kao, HW., Tseng, YC. (2018). Using Nonverbal Information for Conversation Partners Inference by Wearable Devices. In: Lin, YB., Deng, DJ., You, I., Lin, CC. (eds) IoT as a Service. IoTaaS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-030-00410-1_22
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DOI: https://doi.org/10.1007/978-3-030-00410-1_22
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