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Volumn , Issue , 2013, Pages 1575-1581

Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION;

EID: 84910045914     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (73)

References (22)
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    • Bao, L.1    Intille, S. S.2
  • 2
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    • (Suppl 1)
    • Bassett Jr., D. R.; Rowlands, A.; and Trost, S. G. 2012. Calibrationa nd validation of wearable monitors. Med Sci Sports Exerc. 1(1 Suppl 1):S32–38.
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    • Bassett, D. R.1    Rowlands, A.2    Trost, S. G.3
  • 4
    • 69349091456 scopus 로고    scopus 로고
    • Detection of type, duration, and intensity of physical activity using an accelerometer
    • Bonomi, A.; Goris, A.; Yin, B.; and Westerterp, K. 2009. Detection of type, duration, and intensity of physical activity using an accelerometer. Medicine & Science in Sports & Exercise 41(9):1770.
    • (2009) Medicine & Science in Sports & Exercise , vol.41 , Issue.9 , pp. 1770
    • Bonomi, A.1    Goris, A.2    Yin, B.3    Westerterp, K.4
  • 8
    • 0042711018 scopus 로고    scopus 로고
    • On the need for time series data mining benchmarks: a survey and empirical demonstration
    • Keogh, E., and Kasetty, S. 2003. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and Knowledge Discovery 7(4):349–371.
    • (2003) Data Mining and Knowledge Discovery , vol.7 , Issue.4 , pp. 349-371
    • Keogh, E.1    Kasetty, S.2
  • 12
    • 70350115240 scopus 로고    scopus 로고
    • An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
    • Staudenmayer, J.; Pober, D.; Crouter, S.; Bassett, D.; and Freedson, P. 2009. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology 107(4):1300–1307.
    • (2009) Journal of Applied Physiology , vol.107 , Issue.4 , pp. 1300-1307
    • Staudenmayer, J.1    Pober, D.2    Crouter, S.3    Bassett, D.4    Freedson, P.5
  • 15
    • 28044434144 scopus 로고    scopus 로고
    • Conducting accelerometer-based activity assessments in field-based research
    • (suppl)
    • Trost, S. G.; McIver, K. L.; and Pate, R. R. 2005. Conducting accelerometer-based activity assessments in field-based research. Medicine and Science in Sports and Exercise 37(11 suppl):S531–543.
    • (2005) Medicine and Science in Sports and Exercise , vol.37 , Issue.11 , pp. S531-S543
    • Trost, S. G.1    McIver, K. L.2    Pate, R. R.3
  • 16
    • 0035110824 scopus 로고    scopus 로고
    • Challenges and opportunities for measuring physical activity in sedentary adults
    • Tudor-Locke, C., and Myers, A. 2001. Challenges and opportunities for measuring physical activity in sedentary adults. Sports Medicine 31(2):91–100.
    • (2001) Sports Medicine , vol.31 , Issue.2 , pp. 91-100
    • Tudor-Locke, C.1    Myers, A.2
  • 19
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    • Stacked generalization
    • Wolpert, D. 1992. Stacked generalization. Neural Networks 5(2):241–259.
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    • Wolpert, D.1
  • 20
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    • A brief survey on sequence classification
    • Xing, Z.; Pei, J.; and Keogh, E. 2010. A brief survey on sequence classification. SIGKDD Explor. Newsl. 12(1):40–48.
    • (2010) SIGKDD Explor. Newsl , vol.12 , Issue.1 , pp. 40-48
    • Xing, Z.1    Pei, J.2    Keogh, E.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.