| Summary: | 碩士 === 國立臺灣大學 === 電子工程學研究所 === 107 === Affective computing is a key function for human–computer interaction (HCI), which makes it possible for machines and computers to realize human’s emotion and mentality, and further give appropriate responses and services based-on human’s current mental status. As intelligent IoT develops, more and more wearable devices are equipped with different kinds of sensors. We can effectively get various signals sensed from subjects and have the access for continuously monitoring. For this trend, physiological signals reflecting nature responses can be good inputs for affective computing framework. A future application of this scenario is i-Factory. Once the mental status of worker can be perceived by analyzing physiological signals. Workload of each worker can be dynamically adjusted based on the prediction outcome. Consequently, the productivity efficiency of the whole factory can be enhanced.
However, continuously affect monitoring will accumulate a great amount of data. If all the data are analyzed by the cloud, they would result in lack of bandwidth. Multiple layers of routers in the transmission process would lead to large latency. All these would cause a barrier to overall system performance. By contrast, analyzing data in edge devices can significantly reduce the amount of transmitted data and increase the efficiency of computation in the cloud. But the classification accuracy is relatively low due to the resource constrained problem. As a consequence, we aim to propose a cascaded edge-cloud framework for emotion recognition. Large portion of data can be screened by edge devices and only the data that are hard to be recognized would be transmitted to cloud for accurate prediction. For cloud server, entropy-domain features are extracted to quantify the complexity of signal. A high-accuracy framework is established based on multi-modal analysis of physiological signals. For edge devices, extreme learning machine (ELM) is applied to classification in the scenario of restricted hardware and computation resources. Ensemble learning is then used to enhance prediction performance. Finally, we combine both edge and cloud framework to form a cascaded system and attain the results of both high accuracy and low energy consumption.
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