Predicting sleep and lying time of calves with a support vector machine classifier using accelerometer data

被引：32
|
作者：
Hokkanen, Ann-Helena [1 ,2 ]
Hanninen, Laura [1 ,2 ]
Tiusanen, Johannes [3 ]
Pastell, Matti [1 ,3 ]
机构：
[1] Univ Helsinki, Fac Vet Med, Res Ctr Anim Welf, FI-00014 Helsinki, Finland
[2] Univ Helsinki, Fac Vet Med, Dept Prod Anim Med, FI-00014 Helsinki, Finland
[3] Univ Helsinki, Dept Agr Sci, FI-00014 Helsinki, Finland
来源：
APPLIED ANIMAL BEHAVIOUR SCIENCE | 2011年 / 134卷 / 1-2期
关键词：
Calf; Automatic measurement; Accelerometer; BEHAVIOR; ACTIGRAPHY; DISORDERS; PATTERNS; STATE; WAKE;
D O I：
10.1016/j.applanim.2011.06.016
中图分类号：
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号：
0905 ;
摘要：
Sleep is essential to calves, but to date the only possibilities for measuring sleep in cattle production systems use ambulatory EEG or validated sleeping behavior assessments. We developed a small, neck-based, wireless accelerometer system for measuring the sleep and lying time of calves. We collected data from 10 dairy calves and developed a model based on wavelet analysis with a support vector machine classifier for measuring sleep and lying time and were able to record sleep and lying time accurately. For total sleeping time the model was able to distinguish (mean +/- SE) 90 +/- 3% and 85 +/- 4% of the sleeping bouts, and 82 +/- 2% of the occurrence of sleep. Correspondingly, the model distinguished 66 +/- 8% and 70 +/- 6% of the total time for NREM and REM sleep. 70 +/- 6% of the NREM sleep bout lengths and 80 +/- 5% of the REM sleep bouts were predicted. The numbers for NREM and REM bouts were 77 +/- 5% and 79 +/- 4%. respectively. The model correctly predicted 96 +/- 1% of total lying time. 79 +/- 6% of lying bout durations, and 77 +/- 7% of the occurrence of lying bouts. The device provides a method to measure sleep and lying time in calves continuously in a production environment without disturbing the animals. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：10 / 15
页数：6
相关论文
共 50 条
  • [1] Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier
    Al-Angari, Haitham M.
    Sahakian, Alan V.
    IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2012, 16 (03): : 463 - 468
  • [2] Automated recognition of obstructive sleep apnea using ensemble support vector machine classifier
    Kalaivani, V
    INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY, 2020, 33 (03) : 274 - 289
  • [3] ECG Beat Classifier Using Support Vector Machine
    Besrour, R.
    Lachiri, Z.
    Ellouze, N.
    2008 3RD INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES: FROM THEORY TO APPLICATIONS, VOLS 1-5, 2008, : 816 - 820
  • [4] Analysis of schizophrenia using support vector machine classifier
    Jiji, G. Wiselin
    Rajesh, A.
    Kanagaraj, Ajitha
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (21) : 32505 - 32517
  • [5] A hierarchical classifier using new support vector machine
    Wang, YC
    Casasent, D
    EIGHTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, PROCEEDINGS, 2005, : 851 - 855
  • [6] Analysis of schizophrenia using support vector machine classifier
    G. Wiselin Jiji
    A. Rajesh
    Ajitha Kanagaraj
    Multimedia Tools and Applications, 2023, 82 : 32505 - 32517
  • [7] Predicting Patterns of Student Graduation Rates Using Naive Bayes Classifier and Support Vector Machine
    Kesumawati, Ayundyah
    Utari, Dina Tri
    8TH ANNUAL BASIC SCIENCE INTERNATIONAL CONFERENCE: COVERAGE OF BASIC SCIENCES TOWARD THE WORLD'S SUSTAINABILITY CHALLANGES, 2018, 2021
  • [8] Support Vector Machine Classifier with WHM Offset for Unbalanced Data
    Li, Boyang
    Hu, Jinglu
    Hirasawa, Kotaro
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2008, 12 (01) : 94 - 101
  • [9] Efficient Binary Classifier for Prediction of Diabetes Using Data Preprocessing and Support Vector Machine
    Pradhan, Madhavi
    Bamnote, G. R.
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON FRONTIERS OF INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2014, VOL 1, 2015, 327 : 131 - 140
  • [10] Extreme support vector machine classifier
    Liu, Qiuge
    He, Qing
    Shi, Zhongzhi
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2008, 5012 : 222 - 233
12345