Soo Min Kwon, Song Yang, Jian Liu, X. Yang, Wesam Saleh, Shreya Patel, Christine Mathews, Yingying Chen
{"title":"演示:使用毫米波传感器的免提人体活动识别","authors":"Soo Min Kwon, Song Yang, Jian Liu, X. Yang, Wesam Saleh, Shreya Patel, Christine Mathews, Yingying Chen","doi":"10.1109/DySPAN.2019.8935665","DOIUrl":null,"url":null,"abstract":"In this demo, we introduce a hands-free human activity recognition framework leveraging millimeter-wave (mmWave) sensors. Compared to other existing approaches, our network protects user privacy and can remodel a human skeleton performing the activity. Moreover, we show that our network can be achieved in one architecture, and be further optimized to have higher accuracy than those that can only get singular results (i.e. only get pose estimation or activity recognition). To demonstrate the practicality and robustness of our model, we will demonstrate our model in different settings (i.e. facing different backgrounds) and effectively show the accuracy of our network.","PeriodicalId":278172,"journal":{"name":"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Demo: Hands-Free Human Activity Recognition Using Millimeter-Wave Sensors\",\"authors\":\"Soo Min Kwon, Song Yang, Jian Liu, X. Yang, Wesam Saleh, Shreya Patel, Christine Mathews, Yingying Chen\",\"doi\":\"10.1109/DySPAN.2019.8935665\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this demo, we introduce a hands-free human activity recognition framework leveraging millimeter-wave (mmWave) sensors. Compared to other existing approaches, our network protects user privacy and can remodel a human skeleton performing the activity. Moreover, we show that our network can be achieved in one architecture, and be further optimized to have higher accuracy than those that can only get singular results (i.e. only get pose estimation or activity recognition). To demonstrate the practicality and robustness of our model, we will demonstrate our model in different settings (i.e. facing different backgrounds) and effectively show the accuracy of our network.\",\"PeriodicalId\":278172,\"journal\":{\"name\":\"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DySPAN.2019.8935665\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DySPAN.2019.8935665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Demo: Hands-Free Human Activity Recognition Using Millimeter-Wave Sensors
In this demo, we introduce a hands-free human activity recognition framework leveraging millimeter-wave (mmWave) sensors. Compared to other existing approaches, our network protects user privacy and can remodel a human skeleton performing the activity. Moreover, we show that our network can be achieved in one architecture, and be further optimized to have higher accuracy than those that can only get singular results (i.e. only get pose estimation or activity recognition). To demonstrate the practicality and robustness of our model, we will demonstrate our model in different settings (i.e. facing different backgrounds) and effectively show the accuracy of our network.