Yanyi Zhang, Xinyu Li, Jianyu Zhang, Shuhong Chen, Moliang Zhou, Richard A. Farneth, I. Marsic, R. Burd
{"title":"CAR - a deep learning structure for concurrent activity recognition: poster abstract","authors":"Yanyi Zhang, Xinyu Li, Jianyu Zhang, Shuhong Chen, Moliang Zhou, Richard A. Farneth, I. Marsic, R. Burd","doi":"10.1145/3055031.3055058","DOIUrl":null,"url":null,"abstract":"We introduce the Concurrent Activity Recognizer (CAR) - an efficient deep learning structure that recognizes complex concurrent teamwork activities from multimodal data. We implemented the system in a challenging medical setting, where it recognizes 35 different activities using Kinect depth video and data from passive RFID tags on 25 types of medical objects. Our preliminary results showed our system achieved an 84% average accuracy with 0.20 F1-Score.","PeriodicalId":206082,"journal":{"name":"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3055031.3055058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We introduce the Concurrent Activity Recognizer (CAR) - an efficient deep learning structure that recognizes complex concurrent teamwork activities from multimodal data. We implemented the system in a challenging medical setting, where it recognizes 35 different activities using Kinect depth video and data from passive RFID tags on 25 types of medical objects. Our preliminary results showed our system achieved an 84% average accuracy with 0.20 F1-Score.