{"id":1833,"date":"2017-07-17T11:53:32","date_gmt":"2017-07-17T11:53:32","guid":{"rendered":"http:\/\/www.rtc.us.es\/?p=1833"},"modified":"2017-07-17T11:53:32","modified_gmt":"2017-07-17T11:53:32","slug":"embedded-neural-network-for-real-time-animal-behavior-classification","status":"publish","type":"post","link":"https:\/\/grupo.us.es\/rtclab\/embedded-neural-network-for-real-time-animal-behavior-classification\/","title":{"rendered":"Embedded neural network for real-time animal behavior classification"},"content":{"rendered":"<p style=\"text-align: justify;\"><strong>Abstract:<\/strong><br \/>\nRecent biological studies have focused on understanding animal interactions and welfare. To help biologists to obtain animals\u2019 behavior information, resources like wireless sensor networks are needed. Moreover, large amounts of obtained data have to be processed off-line in order to classify different behaviors. There are recent research projects focused on designing monitoring systems capable of measuring some animals\u2019 parameters in order to recognize and monitor their gaits or behaviors. However, network unreliability and high power consumption have limited their applicability.<br \/>\nIn this work, we present an animal behavior recognition, classification and monitoring system based on a wireless sensor network and a smart collar device, provided with inertial sensors and an embedded multi-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviors based on the collected information. In similar works, classification mechanisms are implemented in a server (or base station). The main novelty of this work is the full implementation of a reconfigurable neural network embedded into the animal\u2019s collar, which allows a real-time behavior classification and enables its local storage in SD memory. Moreover, this approach reduces the amount of data transmitted to the base station (and its periodicity), achieving a significantly improving battery life. The system has been simulated and tested in a real scenario for three different horse gaits, using different heuristics and sensors to improve the accuracy of behavior recognition, achieving a maximum of 81%.<\/p>\n<p style=\"text-align: justify;\">\n<p><a href=\"http:\/\/www.rtc.us.es\/wp-content\/uploads\/2016\/07\/Minerva.png\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-1325 aligncenter\" src=\"http:\/\/www.rtc.us.es\/wp-content\/uploads\/2016\/07\/Minerva.png\" alt=\"\" width=\"1769\" height=\"1764\" srcset=\"https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2016\/07\/Minerva.png 1769w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2016\/07\/Minerva-150x150.png 150w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2016\/07\/Minerva-300x300.png 300w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2016\/07\/Minerva-1024x1021.png 1024w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2016\/07\/Minerva-36x36.png 36w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2016\/07\/Minerva-115x115.png 115w\" sizes=\"(max-width: 1769px) 100vw, 1769px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><span id=\"cap0001\"><span class=\"label\">Fig. 1<\/span>. Network topology architecture.<\/span><\/p>\n<p style=\"text-align: center;\"><a href=\"http:\/\/www.rtc.us.es\/wp-content\/uploads\/2017\/07\/MINERVA2.jpg\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-1834\" src=\"http:\/\/www.rtc.us.es\/wp-content\/uploads\/2017\/07\/MINERVA2.jpg\" alt=\"\" width=\"2750\" height=\"1143\" srcset=\"https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2017\/07\/MINERVA2.jpg 2750w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2017\/07\/MINERVA2-300x125.jpg 300w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2017\/07\/MINERVA2-768x319.jpg 768w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2017\/07\/MINERVA2-1024x426.jpg 1024w\" sizes=\"(max-width: 2750px) 100vw, 2750px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><span id=\"cap0003\"><span class=\"label\">Fig. 3<\/span>. BridgeBoard (left) and base station (right).<\/span><\/p>\n<p style=\"text-align: left;\">\n<p><strong>Full text: <\/strong><a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231217311141\">\u00abEmbedded neural network for real-time animal behavior classification\u00bb, Neurocomputing,<\/a><strong><br \/>\n<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Recent biological studies have focused on understanding animal interactions and welfare. To help biologists to obtain animals\u2019 behavior information, resources like wireless sensor networks are needed. Moreover, large amounts [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1834,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[25,6,7],"tags":[],"_links":{"self":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts\/1833"}],"collection":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/comments?post=1833"}],"version-history":[{"count":1,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts\/1833\/revisions"}],"predecessor-version":[{"id":1838,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts\/1833\/revisions\/1838"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/media\/1834"}],"wp:attachment":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/media?parent=1833"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/categories?post=1833"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/tags?post=1833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}