Energy expenditure prediction using a miniaturized ear-worn sensor

Medicine and Science in Sports and Exercise

Volumn 43, Issue 7, 2011, Pages 1369-1377

  Atallah, Louis ^a   Leong, Julian J H ^b   Lo, Benny ^a   Yang, Guang Zhong ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE HOSPITAL   (United Kingdom)

Author keywords

ACCELEROMETER; ACTIVITY RECOGNITION; METABOLIC MEASUREMENT; WEARABLE SENSORS

Indexed keywords

ADULT; ALGORITHM; AMBULATORY MONITORING; ARTICLE; BICYCLE; CALORIMETRY; DAILY LIFE ACTIVITY; ENERGY METABOLISM; FEMALE; HUMAN; INSTRUMENTATION; MALE; METHODOLOGY; PHYSIOLOGY; REPRODUCIBILITY; RUNNING; VALIDATION STUDY; WALKING;

ACTIVITIES OF DAILY LIVING; ADULT; ALGORITHMS; BICYCLING; CALORIMETRY, INDIRECT; ENERGY METABOLISM; FEMALE; HUMANS; MALE; MONITORING, AMBULATORY; REPRODUCIBILITY OF RESULTS; RUNNING; WALKING;

EID: 79959917851     PISSN: 01959131     EISSN: 15300315     Source Type: Journal    
DOI: 10.1249/MSS.0b013e3182093014     Document Type: Article

References (37)

1. Atallah L, Aziz O, Lo B, Yang GZ. Detecting walking gait im¬pairment with an ear-worn sensor. In: Body Sensor Networks 2009, San Francisco, USA. Washington, DC: IEEE Computer Society; 2009. p. 177-82.
2. Aziz O, Atallah L, Lo B, et al. A pervasive body sensor network for measuring postoperative recovery at home. Surg Innov. 2007; 14(2):83-90. (Pubitemid 47399183)
3. Bartlett PL, Freund Y. Adaboost is consistent. In: Scholkopf B, Platt J, Hoffman T, editors. Advances in Neural Information Pro¬cessing Systems, Vol. 19. Proceedings of the 20th Annual Confer¬ence (NIPS 2006). Cambridge (MA): MIT Press; 2007. p. 105-12.
4. Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol. 2009; 107:655-61.
5. Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evalu¬ation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289:2560-72. (Pubitemid 37430158)
6. Choi JH, Hyun JL, Hwang T, Kim JP, Park JC, Shin K. Estima¬tion of activity energy expenditure: accelerometer approach. In: Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society. Washington, DC; IEEE Computer Society; 2005. IEEE-EMBS; 2005; p. 3830-3.
7. Cromwell RL, Newton RA, Carlton LG. Horizontal plane head stabilization during locomotor tasks. J Mot Behav. 2001;33: 49-58. (Pubitemid 32202722)
8. Crouter SE, Clowers KG, Bassett DR Jr. A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol. 2006;100:1324-31. (Pubitemid 43671491)
9. Crouter SE, Schneider PL, Karabulut M, Basset DR. Validity of 10 electronic pedometers for measuring steps, distance and energy cost. Med Sci Sports Exerc. 2003;35(8):1455-60. (Pubitemid 36936169)
10. Devroye L. The uniform convergence of nearest neighbor regres¬sion function estimators and their application in optimization. IEEE Trans Inform Theory. 1978;4(2):142-51. (Pubitemid 8587977)
11. Foster RC, Lanningham-Foster LM, Manohar C, et al. Precision and accuracy of an ankle worn accelerometer-based pedometer in step counting and energy expenditure. Prev Med. 2005;41(3-4):778-83. (Pubitemid 41400044)
12. Freedson P, Kozey S, Lyden K, Staudenmayer J. Neural networks to predict METs from accelerometer counts. Med Sci Sports Exerc. 2010;42(5):117.
13. Go AS, Hylek EM, Philips KA, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm man¬agement and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) study. JAMA. 2001;285: 2370-5. (Pubitemid 32424106)
14. Guyon I. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157-82.
15. Haskell WL, Blair SN, Hill JO. Physical activity: health outcomes and importance for public health policy. Prev Med. 2009;49(4): 280-2.
16. Jolly K, Taylor R, Lip GY, et al. The Birmingham Rehabilitation Uptake Maximisation Study (BRUM). Home-based compared with hospital-based cardiac rehabilitation in a multi-ethnic popu¬lation: cost-effectiveness and patient adherence. Health Technol Assess. 2007;11(35):1-118.
17. Kavanagh J, Barrett R, Morrison S. The role of the neck and trunk in facilitating head stability during walking. Exp Brain Res. 2006; 172(4):454-63. (Pubitemid 43989351)
18. Kavanagh JJ, Morrison S, Barrett RS. Coordination of head and trunk accelerations during walking. Eur J Appl Physiol. 2005; 94(4):468-75. (Pubitemid 40974065)
19. Kim D, Kim HC. Estimation of activity energy expenditure based on activity classification using multi-site triaxial accelerometry. Electronics Lett. 2008;44(4):266-7. (Pubitemid 351263156)
20. Kumuhara H, Tanaka H, Schutz Y. Are pedometers adequate instruments for assessing energy expenditure? Eur J Clin Nutr. 2009;63:1425-32.
21. Langer D, Gosselink R, Sena R, et al. Validation of two activity monitors in patients with COPD. Thorax. 2009;64:641-2.
22. Lo B, Atallah L, Aziz O, ElHelw M, Darzi A, Yang GZ. Real-time pervasive monitoring for postoperative care. In: Proceedings of Body Sensor Networks 2007, Volume 1 of IFMBE. 2007. p. 122-7.
23. Lo B, Pansiot J, Yang GZ. Bayesian analysis of sub-plantar ground reaction force with BSN. In: Proceedings of the 2009 Sixth inter¬national Workshop on Wearable and Implantable Body Sensor Networks; 2009 June 3-5. 2009. p. 133-7.
24. Mathie MJ, Coster AC, Lovell NH, Celler BG. Accelerometry: providing an integrated, practical method for long-term, ambula¬tory monitoring of human movement. Physiol Meas. 2004;25(2): R1-20.
25. 2 portable metabolic system. IntJ Sports Med. 2001;22:280-4. (Pubitemid 32476608)
26. Navot A, Shpigelman L, Tishby N, Vaadia E. Nearest neighbor based feature selection for regression and its application to neural activity. In: Neural Information Processing Systems 18. Cambridge (MA): MIT Press; 2006. p. 995-1002.
27. Pansiot J, Lo B, King RC, McIlwraith D, Yang GZ. Climbsn: climber performance monitoring with BSN. In: IEEE Proceedings of the 5th International Workshop on Wearable and Implantable Body Sensor Networks 2008 (BSN). Hong Kong, China; 2008. p. 33-6
28. Physical activity and cardiovascular health. NIH Consensus Devel¬opment Panel on Physical Activity and Cardiovascular Health. JAMA. 1996;276(3):241-6.
29. Rothney MP, Neumann M, Beziat A, Chen KY. An artificial neural network model of energy expenditure using non-integrated acceleration signals. J Appl Physiol. 2007;103:1419-27. (Pubitemid 47605982)
30. Schapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach Learn. 1999;37(3):297-336. (Pubitemid 32210620)
31. Shrikrishna D, Atallah L, Lo B, et al. Comparison of a novel ear worn sensor (e-AR) with an armband sensor for physical activity monitoring in COPD. In: Annual Congress of the European Res¬piratory Society ERS 2010 in Barcelona, Spain; 2010 Sep 18-22. 2010. p. 511-2.
32. Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an acceler-ometer. J Appl Physiol. 2009;107:1300-7.
33. Swartz AM, Strath SJ, Bassett DR Jr, Obrien WL, King GA, Ainsworth BE. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc. 2000; 32(9 suppl):S450-6.
34. Wannamethee SG, Shaper AG. Physical activity in the prevention of cardiovascular disease. Sports Med. 2001;31(2):101-14.
35. Wixted AJ, Thiel DV, Hahn AG, Gore CJ, Pyne DB, James DA. Measurement of energy expenditure in elite athletes using MEMS-based triaxial accelerometers. IEEE Sensors J. 2007;7(4):481-7.
36. Yang GZ. Body Sensor Networks. New York (NY): Springer-Verlag; 2006. p. 423-80.
37. Zakeri I, Adolph AL, Puyau MR, Vohra FA, Butte NF. Applica¬tion of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry. J Appl Physiol. 2008;104:1665-73.