Literature DB >> 17280985

Estimation of walking energy expenditure by using support vector regression.

S Su1, L Wang, B Celler, E Ambikairajah, A Savkin.   

Abstract

This paper develops a new predictor of walking energy expenditure from wireless measurements of body movements using triaxial accelerometers. Reliable data were collected from repeated walking experiments in different conditions on a treadmill with simultaneous measurement of expired oxygen and carbon dioxide. Support vector regression, a powerful non-linear regression method, was used to process and model the data. This novel processing method sets this investigation apart from existing papers. Good results were achieved in the robust estimation of walking related energy expenditure from a number of variables derived from triaxial accelerometer and treadmill speed.

Entities:  

Year:  2005        PMID: 17280985     DOI: 10.1109/IEMBS.2005.1617240

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Dynamic modelling of heart rate response under different exercise intensity.

Authors:  Steven W Su; Weidong Chen; Dongdong Liu; Yi Fang; Weijun Kuang; Xiaoxiang Yu; Tian Guo; Branko G Celler; Hung T Nguyen
Journal:  Open Med Inform J       Date:  2010-05-28

2.  Predicting lying, sitting, walking and running using Apple Watch and Fitbit data.

Authors:  Daniel Fuller; Javad Rahimipour Anaraki; Bongai Simango; Machel Rayner; Faramarz Dorani; Arastoo Bozorgi; Hui Luan; Fabien A Basset
Journal:  BMJ Open Sport Exerc Med       Date:  2021-04-08
  2 in total

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