期刊
出版社
IEEE
DOI: 10.1109/I2MTC48687.2022.9806622
关键词
Human Activity Recognition; Fine-grained Activity; WiFi-based Activity Recognition
This paper proposes a new approach for recognizing fine-grained human activities using CSI and RSSI WiFi data, achieving a high accuracy rate of 97.5%.
Device-free human activity recognition has become a topic of much interest in recent years. While there is much existing work on course-grained human activity recognition, the recognition of fine-grained human activities is still a research challenge. In this paper, we propose a new approach using CSI and RSSI WiFi data to recognize fine-grained human activities. We selected 4 different fine-grained human activities from a human-to-human interaction dataset and defined some frequency features over CSI and RSSI data to use as input to our classification model. Using some classification methods and the K Nearest Neighbors (KNN) classifier, we achieved 97.5% of accuracy in fine-grained human activity recognition.
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