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Author Ungar, Eugene D. ♦ Schoenbaum, Iris ♦ Henkin, Zalmen ♦ Dolev, Amit ♦ Yehuda, Yehuda ♦ Brosh, Arieh
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Computer science, information & general works ♦ Library & information sciences ♦ Philosophy & psychology ♦ Psychology ♦ Natural sciences & mathematics ♦ Life sciences; biology ♦ Natural history of organisms ♦ Technology ♦ Medicine & health ♦ Incidence & prevention of disease ♦ Diseases ♦ Manufacture for specific uses ♦ Precision instruments & other devices
Subject Domain (in MeSH) Eukaryota ♦ Organisms ♦ Investigative Techniques ♦ Analytical, Diagnostic and Therapeutic Techniques and Equipment ♦ Behavior and Behavior Mechanisms ♦ Psychiatry and Psychology ♦ Information Science ♦ Information Science ♦ Geographic Locations ♦ Geographic Locations
Subject Keyword Discipline Biotechnology ♦ Geographic Information Systems ♦ Animals ♦ Cattle ♦ Discriminant Analysis ♦ Mediterranean Region ♦ Motor Activity ♦ Journal Article ♦ Research Support, Non-u.s. Gov't
Abstract The advent of the Global Positioning System (GPS) has transformed our ability to track livestock on rangelands. However, GPS data use would be greatly enhanced if we could also infer the activity timeline of an animal. We tested how well animal activity could be inferred from data provided by Lotek GPS collars, alone or in conjunction with IceRobotics IceTag pedometers. The collars provide motion and head position data, as well as location. The pedometers count steps, measure activity levels, and differentiate between standing and lying positions. We gathered synchronized data at 5-min resolution, from GPS collars, pedometers, and human observers, for free-grazing cattle (n = 9) at the Hatal Research Station in northern Israel. Equations for inferring activity during 5-min intervals (n = 1,475), classified as Graze, Rest (or Lie and Stand separately), and Travel were derived by discriminant and partition (classification tree) analysis of data from each device separately and from both together. When activity was classified as Graze, Rest and Travel, the lowest overall misclassification rate (10%) was obtained when data from both devices together were subjected to partition analysis; separate misclassification rates were 8, 12, and 3% for Graze, Rest and Travel, respectively. When Rest was subdivided into Lie and Stand, the lowest overall misclassification rate (10%) was again obtained when data from both devices together were subjected to partition analysis; misclassification rates were 6, 1, 26, and 17% for Graze, Lie, Stand, and Travel, respectively. The primary problem was confusion between Rest (or Stand) and Graze. Overall, the combination of Lotek GPS collars with IceRobotics IceTag pedometers was found superior to either device alone in inferring animal activity.
Spatial Coverage Mediterranean Region
Description Country affiliation: Israel
Author Affiliation: Ungar ED ( Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research Organization--Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel. eugene@volcani.agri.gov.il)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-01-01
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 11
Issue Number 1


Source: WHO-Global Index Medicus