Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor

IEEE Journal of Biomedical and Health Informatics

Volumn 17, Issue 2, 2013, Pages 420-426

  Nam, Yunyoung ^a   Park, Jung Wook ^b  

^a Stony Brook University   (United States)

^b LG Electronics Inc   (South Korea)

Author keywords

Accelerometer; Activity classification; Activity recognition; Baby care; Child care; Wearable device

Indexed keywords

ACCELEROMETERS; ATMOSPHERIC PRESSURE; BAROMETERS; FREQUENCY DOMAIN ANALYSIS; PATTERN RECOGNITION; PRESSURE SENSORS; SUPPORT VECTOR MACHINES;

ACTIVITY CLASSIFICATIONS; ACTIVITY RECOGNITION; BABY CARE; CHILD CARE; WEARABLE DEVICES;

WEARABLE SENSORS;

ACCELEROMETRY; AMBULATORY MONITORING; CLASSIFICATION; DAILY LIFE ACTIVITY; DEVICES; FEMALE; HUMAN; INFANT; LOCOMOTION; MALE; PHYSIOLOGY; PRESCHOOL CHILD; PRESSURE; PROCEDURES; SUPPORT VECTOR MACHINE; ARTICLE; EQUIPMENT; METHODOLOGY;

ACCELEROMETRY; ACTIVITIES OF DAILY LIVING; CHILD, PRESCHOOL; FEMALE; HUMANS; INFANT; LOCOMOTION; MALE; MONITORING, AMBULATORY; PRESSURE; SUPPORT VECTOR MACHINES;

EID: 84885088576     PISSN: 21682194     EISSN: None     Source Type: Journal    
DOI: 10.1109/JBHI.2012.2235075     Document Type: Article

References (22)

1. A. M. Khan, Y.-K. Lee, S. Y. Lee, and T.-S. Kim. (2010, Sep.). A triaxial accelerometer-based physical-activity recognition via augmentedsignal features and a hierarchical recognizer. Trans. Info. Tech. Biomed., [Online]. 14(5), pp. 1166-1172, Available: http://dx.doi.org/10.1109/TITB.2010.2051955
2. N. Li, Y. Hou, and Z. Huang. (2011). A real-time algorithm based on triaxial accelerometer for the detection of human activity state. in Proc. 6th Int. Conf. Body Area Netw., ser. BodyNets '11. ICST, Brussels, Belgium, Belgium: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), [Online]. pp. 103-106, Available: http://dl.acm.org/citation.cfm?id= 2318776.2318801
3. A. G. Bonomi. (2011). Physical activity recognition using a wearable accelerometer. in Proc. Sens. Emot., ser. Philips Research Book Series, J. Westerink, M. Krans, and M. Ouwerkerk, Eds., Springer Netherlands, [Online]. vol. 12, pp. 41-51. Available: http://dx.doi.org/10.1007/978-90-481-3258-4-3
4. S. Boughorbel, J. Breebaart, F. Bruekers, I. Flinsenberg, and W. ten Kate. (2010). Child-activity recognition from multi-sensor data. in Proc. 7th Int. Conf. Methods Tech. Behav. Res., ser. MB '10. New York, NY, USA: ACM, [Online]. pp. 38:1-38:3, Available: http://doi.acm.org/10.1145/1931344.1931382
5. A. Fleury, M. Vacher, and N. Noury, "Svm-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results," IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 274-283, Mar. 2010.
6. C. Zhu and W. Sheng. (2009). Multi-sensor fusion for human daily activity recognition in robot-assisted living. in Proc. 4th ACM/IEEE Int. Conf. Human Robot Int., ser. HRI '09. New York, NY, USA: ACM, [Online]. pp. 303-304, Available: http://doi.acm.org/10.1145/1514095.1514187
7. L. Atallah, B. Lo, R. King, and G.-Z. Yang, "Sensor positioning for activity recognition using wearable accelerometers," IEEE Trans. Biomed. Circuits Syst., vol. 5, no. 4, pp. 320-329, Aug. 2011.
8. L. Bao and S. S. Intille. (2004). Activity recognition from user-annotated acceleration data. Pervas. Comput., [Online]. pp. 1-17, Available: http://www.springerlink.com/content/9aqflyk4f47khyjd
9. R. Luo and M. Kay, "Multisensor integration and fusion in intelligent systems," IEEE Trans. Syst., Man, Cybern., vol. 19, no. 5, pp. 901-931, Sep./Oct. 1989. (Pubitemid 20642874)
10. R. R. Brooks and S. S. Iyengar, Multi-sensor Fusion: Fundamentals and Applications With Software. Englewood Cliffs, NJ: Prentice-Hall, 1998.
11. R. Luo, C.-C. Yih, and K. L. Su, "Multisensor fusion and integration: Approaches, applications, and future research directions," IEEE Sensors J., vol. 2, no. 2, pp. 107-119, Apr. 2002. (Pubitemid 44357916)
12. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman. (2005). Activity recognition from accelerometer data. in Proc. 17th Conf. Innovat. Appl. Artif. Intell., AAAI Press, [Online]. vol. 3, pp. 1541-1546, Available: http://dl.acm.org/citation.cfm?id= 1620092.1620107
13. B. Baas and S. U. D. of Electrical Engineering. (1999). An Approach to Low-Power, High-Performance, Fast Fourier Transform Processor Design. Stanford, CA: Stanford University. [Online]. Available: http://books.google.com/books?id= 61KFHwAACAAJ
14. R. Kohavi. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. in Proc. 14th Int. Joint Conf. Artif. Intell., Francisco, CA, USA: Morgan Kaufmann Publishers Inc., [Online]. vol. 2, pp. 1137-1143, Available: http://dl.acm.org/citation.cfm?id= 1643031.1643047
15. J. R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning), 1st ed. San Mateo, CA: Morgan Kaufmann, Oct. 1992.
16. J. C. Platt. (1999). Fast training of support vector machines using sequential minimal optimization, Advances in kernel methods., B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds., Cambridge, MA: MIT Press. [Online]. pp. 185-208, Available: http://dl.acm.org/citation.cfm?id= 299094.299105
17. S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. (2001, Mar.). Improvements to platt's SMO algorithm for SVM classifier design. Neur. Comput., [Online]. 13(3), pp. 637-649, Available: http://dx.doi.org/10. 1162/089976601300014493
18. F. Foerster, M. Smeja, and J. Fahrenberg. (1999, Sep.). Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring. Comput. Human Behav., [Online]. 15(5), pp. 571-583, Available: http://dx.doi.org/10.1016/S0747-5632(99)00037-0
19. G. H. John and P. Langley. (1995). Estimating continuous distributions in bayesian classifiers. [Online]. pp. 338-345, Available: http://citeseerx.ist. psu.edu/viewdoc/summary?doi= 10.1.1.8.3257
20. K. Aminian, P. Robert, E. Buchser, B. Rutschmann, D. Hayoz, and M. Depairon. (1999, May). Physical activity monitoring based on accelerometry: validation and comparison with video observation. Med. Biol. Eng. Comput., [Online]. 37(3), pp. 304-308, Available: http://dx.doi.org/10.1007/BF02513304
21. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems). 2nd ed. San Francisco, CA, : Morgan Kaufmann, 2005.
22. M. A. McDowell, C. D. Fryar, C. L. Ogden, and K. M. Flegal. (2008). Anthropometric reference data for children and adults: United States, 2003-2006. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [Online]. Available: http://www.cdc.gov/nchs/data/nhsr/nhsr010.pdf.