Research Article
Using Nonverbal Information for Conversation Partners Inference by Wearable Devices
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@INPROCEEDINGS{10.1007/978-3-030-00410-1_22, author={Deeporn Mungtavesinsuk and Yan-Ann Chen and Cheng-Wei Wu and Ensa Bajo and Hsin-Wei Kao and Yu-Chee Tseng}, title={Using Nonverbal Information for Conversation Partners Inference by Wearable Devices}, proceedings={IoT as a Service. Third International Conference, IoTaaS 2017, Taichung, Taiwan, September 20--22, 2017, Proceedings}, proceedings_a={IOTAAS}, year={2018}, month={10}, keywords={Conversational partner inference Nonverbal information Social interaction analysis Wearable devices}, doi={10.1007/978-3-030-00410-1_22} }- Deeporn Mungtavesinsuk
Yan-Ann Chen
Cheng-Wei Wu
Ensa Bajo
Hsin-Wei Kao
Yu-Chee Tseng
Year: 2018
Using Nonverbal Information for Conversation Partners Inference by Wearable Devices
IOTAAS
Springer
DOI: 10.1007/978-3-030-00410-1_22
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 , and , 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 is better than and . Moreover, our approach shows higher accuracy than the state-of-the-art approach for conversational partner inference.
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