Using Deep Learning for Energy Expenditure Estimation with wearable sensors

2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015

Volumn , Issue , 2015, Pages 501-506

  Zhu, Jindan ^a   Pande, Amit ^a   Mohapatra, Prasant ^a   Han, Jay J ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP LEARNING; DISEASES; MEAN SQUARE ERROR; NEURAL NETWORKS;

CARDIO-VASCULAR DISEASE; CONVOLUTION NEURAL NETWORK; ENERGY EXPENDITURE; ENERGY EXPENDITURE ESTIMATION; HEART RATE SENSORS; IMPORTANT FEATURES; ROOT MEAN SQUARE ERRORS; TRIAXIAL ACCELEROMETER;

WEARABLE SENSORS;

EID: 84966473909     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HealthCom.2015.7454554     Document Type: Conference Paper

References (22)

1. Y. Wang, M. A. Beydoun, L. Liang, B. Caballero, and S. K. Kumanyika, "Will all americans become overweight or obese? estimating the progression and cost of the us obesity epidemic," Obesity, vol. 16, no. 10, pp. 2323-2330, 2008.
2. M. Altini, J. Penders, and O. Amft, "Energy expenditure estimation using wearable sensors: A new methodology for activity-specific models," in Proceedings of the Conference on Wireless Health, ser. WH '12. New York, NY, USA: ACM, 2012, pp. 1:1-1:8.
3. A. Pande, Y. Zeng, A. K. Das, P. Mohapatra, S. Miyamoto, E. Seto, E. K. Henricson, and J. J. Han, "Energy expenditure estimation with smartphone body sensors," in Proceedings of the 8th International Conference on Body Area Networks, ser. BodyNets '13. ICST, 2013, pp. 8-14.
4. A. Pande, G. Casazza, A. Nicorici, E. Seto, S. Miyamoto, M. Lange, T. Abresch, P. Mohapatra, and J. Han, "Energy expenditure estimation in boys with duchene muscular dystrophy using accelerometer and heart rate sensors," in Healthcare Innovation Conference (HIC), 2014 IEEE, Oct 2014, pp. 26-29.
5. D. Ciresan, U. Meier, and J. Schmidhuber, "Multi-column deep neural networks for image classification," in in proceedings of CVPR 2012, 2012, pp. 3642-3649.
6. S. Ji, W. Xu, M. Yang, and K. Yu, "3d convolutional neural networks for human action recognition," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 1, pp. 221-231, Jan 2013.
7. P. S. Freedson, K. Lyden, S. Kozey-Keadle, and J. Staudenmayer, "Evaluation of artificial neural network algorithms for predicting mets and activity type from accelerometer data: validation on an independent sample," J Appl Physiol (1985), vol. 111, no. 6, pp. 1804-12, 2011.
8. M. P. Rothney, M. Neumann, A. Beziat, and K. Y. Chen, "An artificial neural network model of energy expenditure using nonintegrated acceleration signals," J Appl Physiol (1985), vol. 103, no. 4, pp. 1419-27, 2007.
9. F. Albinali, S. Intille, W. Haskell, and M. Rosenberger, "Using wearable activity type detection to improve physical activity energy expenditure estimation," in Proceedings of the 12th ACM International Conference on Ubiquitous Computing, ser. UbiComp '10. New York, NY, USA: ACM, 2010, pp. 311-320.
10. A. G. Bonomi and G. Plasqui, ""divide and conquer": Assessing energy expenditure following physical activity type classification," J Appl Physiol (1985), vol. 112, no. 5, p. 932; author reply 933, 2012.
11. L. Deng and D. Yu, "Deep learning: Methods and applications," Found. Trends Signal Process., vol. 7, no. 3 ;4, pp. 197-387, Jun. 2014.
12. W. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115-133, 1943.
13. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 7, pp. 1527-1554, Jul. 2006.
14. I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," in Advances in Neural Information Processing Systems, 2014, pp. 3104-3112.
15. Q. V. Le, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng, "Building high-level features using large scale unsupervised learning," in In International Conference on Machine Learning, 2012. 103, 2012.
16. Y. Zheng, Q. Liu, E. Chen, Y. Ge, and J. Zhao, "Time series classification using multi-channels deep convolutional neural networks," in Web-Age Information Management, ser. Lecture Notes in Computer Science. Springer International Publishing, 2014, vol. 8485, pp. 298-310.
17. M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and J. Zhang, "Convolutional neural networks for human activity recognition using mobile sensors," 2014.
18. L. Bao and S. Intille, "Activity recognition from user-annotated acceleration data," in Pervasive Computing, ser. Lecture Notes in Computer Science, A. Ferscha and F. Mattern, Eds., vol. 3001. Springer Berlin Heidelberg, 2004, pp. 1-17. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-24646-6 1
19. A. M. Roza and H. M. Shizgal, "The harris benedict equation reevaluated: resting energy requirements and the body cell mass." The American Journal of Clinical Nutrition, vol. 40, no. 1, pp. 168-82, 1984.
20. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, U. D. Montral, and M. Qubec, "Greedy layer-wise training of deep networks," in In NIPS. MIT Press, 2007.
21. F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio, "Theano: new features and speed improvements," Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.
22. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The weka data mining software: An update," ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18, 2009.