{"title":"基于加速度计的活动监测系统,用于自由漫游动物的行为分析","authors":"Gergo Sántha, G. Hermann","doi":"10.1109/SISY.2013.6662570","DOIUrl":null,"url":null,"abstract":"In this paper, an activity monitoring system is described based on a custom wireless 3-axis accelerometer sensor. The system is suitable for behavioral analysis of larger free-roaming animals. The wireless sensor is mounted on a collar and transmits the accelerometric data to the PC logging application. The received data can be recorded fully or partially by segmenting the events upon derivative threshold crossing or manual triggering. Furthermore, a segmentation method is proposed based on periodicity extraction from the selected axis data. The segmented data chunks can be introduced to machine learning algorithms (Artificial Neural Networks, Hidden Markov Models or Support Vector Machines) for further classification.","PeriodicalId":187088,"journal":{"name":"2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Accelerometer based activity monitoring system for behavioural analysis of free-roaming animals\",\"authors\":\"Gergo Sántha, G. Hermann\",\"doi\":\"10.1109/SISY.2013.6662570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an activity monitoring system is described based on a custom wireless 3-axis accelerometer sensor. The system is suitable for behavioral analysis of larger free-roaming animals. The wireless sensor is mounted on a collar and transmits the accelerometric data to the PC logging application. The received data can be recorded fully or partially by segmenting the events upon derivative threshold crossing or manual triggering. Furthermore, a segmentation method is proposed based on periodicity extraction from the selected axis data. The segmented data chunks can be introduced to machine learning algorithms (Artificial Neural Networks, Hidden Markov Models or Support Vector Machines) for further classification.\",\"PeriodicalId\":187088,\"journal\":{\"name\":\"2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY)\",\"volume\":\"259 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SISY.2013.6662570\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SISY.2013.6662570","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accelerometer based activity monitoring system for behavioural analysis of free-roaming animals
In this paper, an activity monitoring system is described based on a custom wireless 3-axis accelerometer sensor. The system is suitable for behavioral analysis of larger free-roaming animals. The wireless sensor is mounted on a collar and transmits the accelerometric data to the PC logging application. The received data can be recorded fully or partially by segmenting the events upon derivative threshold crossing or manual triggering. Furthermore, a segmentation method is proposed based on periodicity extraction from the selected axis data. The segmented data chunks can be introduced to machine learning algorithms (Artificial Neural Networks, Hidden Markov Models or Support Vector Machines) for further classification.