Pervasive eating habits monitoring and recognition through a wearable acoustic sensor?

Proceedings - PERVASIVEHEALTH 2014: 8th International Conference on Pervasive Computing Technologies for Healthcare

Volumn , Issue , 2014, Pages 174-177

  Bi, Yin ^a   Xu, Wenyao ^b   Guan, Nan ^a   Wei, Yangjie ^a   Yi, Wang ^a  

^a NORTHEASTERN UNIVERSITY   (China)

^b UNIVERSITY AT BUFFALO   (United States)

Author keywords

Eating habit; Feature extraction; KNN; SVM

Indexed keywords

ACOUSTIC WAVES; DIAGNOSIS; FEATURE EXTRACTION; MHEALTH; NEAREST NEIGHBOR SEARCH; SMARTPHONES; SUPPORT VECTOR MACHINES; UBIQUITOUS COMPUTING;

ACOUSTIC SENSORS; ACOUSTIC SIGNALS; CLINICAL DIAGNOSIS; EATING HABITS; IMPORTANT FEATURES; K NEAREST NEIGHBOR (KNN); LIFESTYLE-RELATED DISEASE; RECOGNITION ACCURACY;

WEARABLE SENSORS;

EID: 84928881593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.4108/icst.pervasivehealth.2014.255423     Document Type: Conference Paper

References (24)

1. O. Amft. A wearable earpad sensor for chewinging monitoring. IEEE Sensor Conference, 2010.
2. O. Amft. Ambient, on-body, and implantable monitoring technologies to assess dietary behaviour. International Handbook of Behavior, Diet and Nutrition, Springer, 2011.
3. O. Amft, M. Kusserow, and G. Troster. Automatic identification of temporal sequences in chewinging sounds. BIBM, 2010.
4. L. Bao and S. Intille. Activity recognition from user-annotated acceleration data. Pervasive Comput., 2004.
5. F. Bellisle. Why should we study human food intake behaviour? Nutr Metab Cardiovasc Dis., 13(4), 2003.
6. T. Cover and P. Hart. Nearest neighbor pattern classification. IEEE Trans. on Information Theory, 1967.
7. N. DeBelie and V. D. Smedt. Principal component analysis of chewinging sounds to detect differences in apple crispness. Postharvest Biol Technol, 18, 2000.
8. B. Drake. Food crushing sounds. an introductory study. J Food Sci, 28(2), 1963.
9. K. O. et al. No contact-type chewinging number counting equipment using infrared sensor. T. SICE, 38(9), 2002.
10. M. S. et al. Wearable eating habit sensing system using internal body sound. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 4(1), 2010.
11. R. Z. et al. Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007.
12. A. Farri, A. Accornero, and C. Burdese. Social importance of dysphagia: its impact on diagnosis and therapy. ACTA Otorhinolaryngo Italica, 2007.
13. M. Furuta and H. Miyako. Observation of matters related to chewinging habits. Japanese Journal of Psychosomatic Dentistry, 14(2), 1999.
14. T. Huynh and B. Schiele. Analyzing features for activity recognition. Proceedings of the 2005 joint conference on Smart objects and ambient intelligence, 2005.
15. W. L. III, A. Deibel, C. Glembin, and E. Munday. Analysis of food crushing sound during mastication: Frequency-time studies. Journal of Texture Studies, 19(1), 1988.
16. G. Lopez and I. Yamada. New healthcare society supported by wearable sensors and information mapping based services. WIVE, 2009.
17. P. Lopez-Meyer, S. Schuckers, O. Makeyev, and E. Sazonov. Detection of periods of food intake using support vector machines. EMBS, 2010.
18. J. Nishimura and T. Kuroda. Eating habits monitoring using wireless wearable in-ear microphone. ISWPC, 2008.
19. N. Ravi, N. Dandekar, and P. M. et al. Activity recognition from accelerometer data. AAAI, 2005.
20. V. Vapnik. Statistical learning theory. 1998.
21. K. Veropoulos, C. Campbell, and N. Cristianini. Controlling the sensitivity of support vector machines. IJCAI, 1999.
22. Z. M. Vickers. Food sounds: How much information do they contain? J Food Sci, 45(6), 1980.
23. A. Yadollahi and Z. Moussavi. Feature selection for swallowinging sounds classification. EMBC, 2007.
24. M. Zhang and A. Sawchuk. A feature selection-based framework for human activity recognition using wearable multimodal sensors. BODYNETS, 2011.