Advertisement

Automatic Dietary Monitoring Using Wearable Accessories

  • Giovanni Schiboni
  • Oliver Amft
Chapter

Abstract

This chapter provides an introduction to the field of automatic dietary monitoring (ADM) that intends to derive diet-related behaviour information from unobtrusive sensors and data analysis algorithms. A conceptual gap found in most literature reviews on the relation of physiology and dietary activities is filled. A consistent knowledge-based physiological model for dietary activities is presented. A biomedical approach is adopted to retrieve phenomenological insights of the food preparation, intake, and digestion processes. A taxonomy of dietary activities and a literature review of wearable sensing approaches and dietary dimensions across all dietary activities are also presented.

Notes

Acknowledgement

This work has been partially funded by the European Union H2020 MSCA ITN ACROSSING project (GA no. 616757).

References

  1. 1.
    Wing, R. R., & Phelan, S. (2005). Long-term weight loss maintenance. The American Journal of Clinical Nutrition, 82(1 Suppl), 222S–225S. ISSN 0002-9165.Google Scholar
  2. 2.
    Burke, B. S. (1947). The dietary history as a tool in research. Journal of the American Dietetic Association, 23, 1041–1046.Google Scholar
  3. 3.
    Bellisle, F. (2003). Why should we study human food intake behaviour? Nutrition, Metabolism, and Cardiovascular Diseases, 13(4), 189–193.  https://doi.org/10.1016/ S0939-4753(03)80010-8.CrossRefGoogle Scholar
  4. 4.
    Witschi, J. C. (1990). Short-term dietary recall and recording methods. In W. Willett (Ed.), Nutritional epidemiology (Vol. 4, pp. 52–68). New York: Oxford University Press.Google Scholar
  5. 5.
    Lieffers, J. R. L., Vance, V. A., & Hanning, R. M. (2014). Use of mobile device applications in Canadian dietetic practice. Canadian Journal of Dietetic Practice and Research, 75(1), 41–47.  https://doi.org/10.3148/75.1.2014.41. CrossRefGoogle Scholar
  6. 6.
    Amft, O., & Troster, G. (2006, November). Methods for detection and classification of normal swallowing from muscle activation and sound. PHC 2006: Proceedings of the first international conference on pervasive computing technologies for healthcare, ICST, pp. 1–10. doi:  https://doi.org/10.1109/PCTHEALTH.2006.361624.
  7. 7.
    Amft, O., Junker, H., & Troster, G. (2005). Detection of eating and drinking arm gestures using inertial body-worn sensors. In B. Rhodes & K. Mase (Eds.), ISWC 2005: IEEE Proceedings of the ninth international symposium on wearable computers (pp. 160–163). Los Alamitos: IEEE Press.  https://doi.org/10.1109/ISWC.2005.17.Google Scholar
  8. 8.
    Amft, O., St¨ager, M., Lukowicz, P., & Troster, G. (2005). Analysis of chewing sounds for dietary monitoring. In UbiComp 2005: Proceedings of the 7th international conference on ubiquitous computing., volume 3660 of LNCS (pp. 56–72). Berlin/Heidelberg: Springer.  https://doi.org/10.1007/11551201 4.CrossRefGoogle Scholar
  9. 9.
    Amft, O., & Troster, G. (2009). On-body sensing solutions for automatic dietary monitoring. IEEE Pervasive Computing, 8(2), 62–70.  https://doi.org/10.1109/MPRV.2009.32.CrossRefGoogle Scholar
  10. 10.
    Amft, O. (2011). Ambient, on-body, and implantable monitoring technologies to assess dietary behaviour. In V. R. Preedy, R. R. Watson, & C. R. Martin (Eds.), International handbook of behavior, food and nutrition (Vol. 38, pp. 3507–3526). New York: Springer. ISBN 978-0-387-92270-6.CrossRefGoogle Scholar
  11. 11.
    Fontana, J. M., & Sazonov, E. (2014). Detection and characterization of food intake by wearable sensors, Wearable sensors (pp. 591–616). Oxford: Academic Press.Google Scholar
  12. 12.
    Passler, S., & Fischer, W.-J. (2014). Food intake monitoring: Automated chew event detection in chewing sounds. IEEE Journal of Biomedical and Health Informatics, 18(1), 278–289.CrossRefGoogle Scholar
  13. 13.
    Stumbo, P. J. (2013). New technology in dietary assessment: A review of digital methods in improving food record accuracy. Proceedings of the Nutrition Society, 72(01), 70–76.CrossRefGoogle Scholar
  14. 14.
    Kalantarian, H., Mortazavi, B., Alshurafa, N., Sideris, C., Le, T., & Sarrafzadeh, M. (2016). A comparison of piezoelectric-based inertial sensing and audio-based detection of swallows. Obesity Medicine, 1, 6–14. ISSN 2451-8476.  https://doi.org/10.1016/j.obmed.2016.01.003.CrossRefGoogle Scholar
  15. 15.
    Steele, R. (2015). An overview of the state of the art of automated capture of dietary intake information. Critical Reviews in Food Science and Nutrition, 55(13), 1929–1938.CrossRefGoogle Scholar
  16. 16.
    Hassannejad, H., Matrella, G., Ciampolini, P., De Munari, I., Mordonini, M., & Cagnoni, S. (2017). Automatic diet monitoring: A review of computer vision and wearable sensor-based methods. International Journal of Food Sciences and Nutrition, 68(6), 656–670.Google Scholar
  17. 17.
    Prioleau, T., Moore, E., & Ghovanloo, M. (2017). Unobtrusive and wearable systems for automatic dietary monitoring. IEEE Transactions on Biomedical Engineering, 99, 1–1. ISSN 0018-9294.  https://doi.org/10.1109/TBME. 2016.2631246.Google Scholar
  18. 18.
    Vu, T., Lin, F., Alshurafa, N., & Xu, W. (2017). Wearable food intake monitoring technologies: A comprehensive review. Computers, 6(1), 4.  https://doi.org/10.3390/computers6010004.CrossRefGoogle Scholar
  19. 19.
    Jones, B. (2012). Normal and abnormal swallowing: Imaging in diagnosis and therapy. New York: Springer Science & Business Media.Google Scholar
  20. 20.
    Dodds, W. J., Stewart, E. T., & Logemann, J. A. (1990). Physiology and radiology of the normal oral and pharyngeal phases of swallowing. American Journal of Roentgenology, 154(5), 953–963. ISSN 0361-803X.  https://doi.org/10.2214/ajr.154.5.2108569.CrossRefGoogle Scholar
  21. 21.
    Pereira, L. J., de Wijk, R. A., Gaviao, M. B. D., & van der Bilt, A. (2006). Effects of added fluids on the perception of solid food. Physiology & Behavior, 88(4), 538–544.CrossRefGoogle Scholar
  22. 22.
    Der Bilt, A. V., de Liz Pocztaruk, R., & Abbink, J. H. (2010). Skull vibration during chewing of crispy food. Journal of Texture Studies, 41(6), 774–788.CrossRefGoogle Scholar
  23. 23.
    Fontijn-Tekamp, F. A., van der Bilt, A., Abbink, J. H., & Bosman, F. (2004). Swallowing threshold and masticatory performance in dentate adults. Physiology & Behavior, 83(3), 431–436.  https://doi.org/10.1016/j.physbeh.2004.08. 026. CrossRefGoogle Scholar
  24. 24.
    Hiiemae, K. (2004). Mechanisms of food reduction, transport and deglutition: How the texture of food affects feeding behavior. Journal of Texture Studies, 35(2), 171–200.  https://doi.org/10.1111/j.1745-4603.2004.tb00832.x.CrossRefGoogle Scholar
  25. 25.
    Gray-Stuart, E. M. (2016). Modelling food breakdown and Bolus formation during mastication: A thesis presented in partial fulfilment of the requirements for the degree of doctor of philosophy in bioprocess engineering at Massey University, Palmerston North, New Zealand. Thesis, Massey University.Google Scholar
  26. 26.
    Aguilera, J. M. (2005). Why food microstructure? Journal of Food Engineering, 67(1–2), 3–11.  https://doi.org/10.1016/j.jfoodeng.2004.05.050. IV Iberoamerican Congress of Food Engineering (CIBIA IV).CrossRefGoogle Scholar
  27. 27.
    Amft, O., Kusserow, M., & Troster, G. (2007, March). Probabilistic parsing of dietary activity events. In S. Leonhardt, T. Falck, & P. Mahonen (Eds.), BSN 2007: Proceedings of the international workshop on wearable and implantable body sensor networks (Vol. 13, pp. 242–247). Springer. doi:  https://doi.org/10.1007/978-3-540-70994-741.
  28. 28.
    Kissileff, H. R., & Guss, J. L. (2001). Microstructure of eating behavior in humans. Appetite, 36(1), 70–78.  https://doi.org/10.1006/appe.2000.0369. CrossRefGoogle Scholar
  29. 29.
    Tortora, G. J., & Derrickson, B. H. (2008). Principles of anatomy and physiology. Hoboken: Wiley. ISBN 978-0-470-08471-7. Google-Books-ID: uNwfOPPYgKAC.Google Scholar
  30. 30.
    Manfield, A. (2015, August). Win diabetes in 4/2 weeks. Morrisville: Lulu Press. ISBN: 978-1-326-39258-1. Google-Books-ID: WhKFCgAAQBAJ.Google Scholar
  31. 31.
    Block, G. (1982). A review of validations of dietary assessment methods. American Journal of Epidemiology, 115(4), 492–505.Google Scholar
  32. 32.
    Barbosa-Canovas, G. V., Juliano, P., & Peleg, M. (2009). Engineering properties of foods. Food Engineering, I, 39.Google Scholar
  33. 33.
    Winkler, E., & Turrell, G. (2010). Confidence to cook vegetables and the buying habits of Australian households. Journal of the American Dietetic Association, 110(5), S52–S61.CrossRefGoogle Scholar
  34. 34.
    Pham, C., & Olivier, P. (2009). Slice&Dice: recognizing food preparation activities using embedded accelerometers. Proceedings of the European conference on ambient intelligence, AmI ’09, Berlin, Heidelberg, Springer-Verlag, pp. 34–43. ISBN 978-3-642-05407-5. doi:  https://doi.org/10.1007/978-3-642-05408-2 4.
  35. 35.
    Plotz, T., Moynihan, P., Pham, C., & Olivier, P. (2011). Activity recognition and healthier food preparation. In L. Chen, C. D. Nugent, J. Biswas, & J. Hoey (Eds.), Activity recognition in pervasive intelligent environments. Atlantis ambient and pervasive intelligence (Vol. 4, pp. 313–329). Paris: Atlantis Press. ISBN 978-90-78677-42-0 978-94-91216-05-3. doi:  https://doi.org/10.2991/978–94–91216-05-3 14.
  36. 36.
    Ward, J. A., Bharatula, N., Troster, G., & Lukowicz, P. (2002). Continuous activity recognition in the kitchen using miniaturised sensor button. Technical notes.Google Scholar
  37. 37.
    Patterson, D. J., Fox, D., Kautz, H., & Philipose, M. (2005). Fine-grained activity recognition by aggregating abstract object usage. In B. Rhodes & K. Mase (Eds.), ISWC 2005: Proceedings of the ninth IEEE international symposium on wearable computers (pp. 44–51). Los Alamitos: IEEE Press.  https://doi.org/10.1109/ISWC.2005.22.Google Scholar
  38. 38.
    Ye, X., Chen, G., Gao, Y., Wang, H., & Cao, Y. (2016). Assisting food journaling with automatic eating detection. Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems, CHI EA’16, New York, ACM, pp. 3255–3262. ISBN 978-1-4503-4082-3. doi:  https://doi.org/10.1145/2851581.2892426.
  39. 39.
    Ye, X., Chen, G., & Cao, Y. (2015). Automatic eating detection using head-mount and wrist-worn accelerometers. 2015 17th international conference on e-Health networking, application services (HealthCom.), pp. 578–581. doi:  https://doi.org/10.1109/HealthCom.2015.7454568.
  40. 40.
    Thomaz, E., Essa, I., & Abowd, G. D. (2015). A practical approach for recognizing eating moments with wrist-mounted inertial sensing. Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, UbiComp ’15, New York, pp. 1029–1040. ACM. ISBN 978-1-4503-3574-4. doi:  https://doi.org/10.1145/2750858. 2807545.
  41. 41.
    Scisco, J. L., Muth, E. R., & Hoover, A. W. (2014). Examining the utility of a bite-count-based measure of eating activity in free-living human beings. Journal of the Academy of Nutrition and Dietetics, 114(3), 464–469. ISSN 2212-2672.  https://doi.org/10.1016/j.jand.2013.09.017.CrossRefGoogle Scholar
  42. 42.
    Dong, Y., Scisco, J., Wilson, M., Muth, E., & Hoover, A. (2014). Detecting periods of eating during free-living by tracking wrist motion. IEEE Journal of Biomedical and Health Informatics, 18(4), 1253–1260. ISSN 2168-2208.  https://doi.org/10.1109/JBHI.2013.2282471.CrossRefGoogle Scholar
  43. 43.
    Eskandari, S. (2013). Bite detection and differentiation using templates of Wrist motion. PhD thesis, Clemson University.Google Scholar
  44. 44.
    Dong, Y., Hoover, A., Scisco, J., & Muth, E. (2012. ISSN 1573-3270). A new method for measuring meal intake in humans via automated wrist motion tracking. Applied Psychophysiology and Biofeedback, 37(3), 205–215.  https://doi.org/10.1007/s10484-012-9194-1.CrossRefGoogle Scholar
  45. 45.
    Amft, O., Bannach, D., Pirkl, G., Kreil, M., & Lukowicz, P. (2010). Towards wearable sensing based assessment of fluid intake. Per-health 2010: Proceedings of the first IEEE PerCom workshop on pervasive healthcare, IEEE, pp. 298–303. doi:  https://doi.org/10.1109/PERCOMW.2010.5470653.
  46. 46.
    Amft, O., & Troster, G. (2008). Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine, 42(2), 121–136. ISSN 0933-3657.  https://doi.org/10.1016/j.artmed.2007.11.007.CrossRefGoogle Scholar
  47. 47.
    Junker, H., Amft, O., Lukowicz, P., & Troster, G. (2008). Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6), 2010–2024.  https://doi.org/10.1016/j.patcog.2007.11.016.CrossRefMATHGoogle Scholar
  48. 48.
    Kadomura, A., Li, C-Y., Chen, Y-C., Chu, H-H., Tsukada K., & Siio I. (2013). Sensing fork and persuasive game for improving eating behavior. Proceeding UbiComp ’13 adjunct proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication, ACM Press, pp. 71–74. ISBN 978-1-4503-2215-7. doi:  https://doi.org/10.1145/1114 2494091.2494112.
  49. 49.
    Huo, X., Wang, J., & Ghovanloo, M. (2008). A magneto-inductive sensor based wireless tongue-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(5), 497–504. ISSN 1534-4320.  https://doi.org/10.1109/TNSRE.2008.2003375.CrossRefGoogle Scholar
  50. 50.
    Wang, Y-X., Lo, L-Y., & Hu, M-C. (2014). Eat as much as you can: A Kinect-based facial rehabilitation game based on mouth and tongue movements. Proceedings of the 22nd ACM international conference on multimedia, ACM, pp. 743–744.Google Scholar
  51. 51.
    Tamilia, E., Formica, D., Scaini, A., & Taffoni, F. (2016). An automated system for the analysis of newborns’ oral-motor behavior. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(12), 1294–1303.CrossRefGoogle Scholar
  52. 52.
    Huang, Q., Wang, W., & Zhang, Q. (2017). Your glasses know your diet: Dietary monitoring using electromyography sensors. IEEE Internet of Things Journal, 99, 1–1. ISSN 2327-4662.  https://doi.org/10.1109/JIOT.2017. 2656151.Google Scholar
  53. 53.
    Zhang, R., & Amft, O. (2016). Bite glasses: Measuring chewing using EMG and bone vibration in smart eyeglasses. Proceedings of the 2016 ACM international symposium on wearable computers, ISWC ’16, New York, ACM, pp. 50–52. ISBN 978–1–4503-4460-9. doi:  https://doi.org/10.1145/2971763.2971799.
  54. 54.
    Zhang, R., & Amft, O. (2016). Regular-look eyeglasses can monitor chewing. Proceedings of the 2016 ACM international symposium on wearable computers, ACM, pp. 389–392. doi:  https://doi.org/10.1145/2968219.2971374.
  55. 55.
    Zhang, R., Bernhart, S., & Amft, O. (2016). Diet eyeglasses: Recognising food chewing using EMG and smart eyeglasses. Proceedings of the international conference on wearable and implantable body sensor networks (BSN’ 16), IEEE, pp. 7–12 doi:  https://doi.org/10.1109/BSN.2016.7516224.
  56. 56.
    Farooq, M., & Sazonov, E. (2016). A novel wearable device for food intake and physical activity recognition. Sensors, 16(7), 1067. ISSN 1424-8220.  https://doi.org/10.3390/s16071067.CrossRefGoogle Scholar
  57. 57.
    Chung, J., Chung, J., Wonjun, O., Yoo, Y., Lee, W. G., & Bang, H. (2017). A glasses-type wearable device for monitoring the patterns of food intake and facial activity. Scientific Reports, 7, 41690. ISSN 2045-2322.  https://doi.org/10.1038/srep41690.CrossRefGoogle Scholar
  58. 58.
    Amft, O. (2010). A wearable earpad sensor for chewing monitoring. Sensors 2010: Proceedings of IEEE sensors conference, IEEE, pp. 222–227. doi:  https://doi.org/10.1109/ICSENS.2010.5690449.
  59. 59.
    Amft, O., Kusserow, M., & Troster, G. (2009). Bite weight prediction from acoustic recognition of chewing. IEEE Transactions on Biomedical Engineering, 56(6), 1663–1672.  https://doi.org/10.1109/TBME.2009. 2015873.CrossRefGoogle Scholar
  60. 60.
    Gao, Y., Zhang, N., Wang, H., Ding, X., Ye X.,Chen, G., & Cao, Y. (2016). iHear food: Eating detection using commodity bluetooth headsets. Connected health: Applications, systems and engineering technologies (CHASE), 2016 I.E. first international conference on, IEEE, pp. 163–172.Google Scholar
  61. 61.
    Liu, J., Johns, E., Atallah, L., Pettitt, C., Lo, B., Frost, G., & Yang, G. Z. (2012, May). An intelligent food-intake monitoring system using wearable sensors. 2012 ninth international conference on wearable and implantable body sensor networks (BSN), pp. 154–160. doi: https://doi.org/10.1109/BSN. 2012.11.Google Scholar
  62. 62.
    Mirtchouk, M., Merck, C., & Kleinberg, S. (2016). Automated estimation of food type and amount consumed from body-worn audio and motion sensors. Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, UbiComp ’16, New York, ACM, pp. 451–462. ISBN 978-1-4503-4461-6. doi:  https://doi.org/10.1145/2971648.2971677.
  63. 63.
    Nishimura, J., & Kuroda, T. (2008). Eating habits monitoring using wireless wearable in-ear microphone. ISWPC 2008: Proceedings of the 3rd international symposium on wireless pervasive computing, IEEE, pp. 130–132. doi:  https://doi.org/10.1109/ISWPC.2008.4556181.
  64. 64.
    Passler, S., Wolff, M., & Fischer, W.-J. (2012). Food intake monitoring: An acoustical approach to automated food intake activity detection and classification of consumed food. Physiological Measurement, 33(6), 1073.  https://doi.org/10.1088/0967-3334/33/6/1073.CrossRefGoogle Scholar
  65. 65.
    Shuzo, M., Komori, S., Takashima, T., Lopez, G., Tatsuta, S., Yanagimoto, S., Warisawa, S., Delaunay, J.-J., & Yamada, I. (2009). Wearable eating habit sensing using sound information. Proceedings of the 2009 conference on micromechatronics for information and precision equipment, pp. 221–222.Google Scholar
  66. 66.
    Bedri, A., Verlekar, A., Thomaz, E., Avva, V., & Starner, T. (2015). Detecting mastication: A wearable approach. Proceedings of the 2015 ACM on international conference on multimodal interaction, ACM, pp. 247–250.Google Scholar
  67. 67.
    Li, C-Y., Chen, Y-C., Chen, W-J., Huang P., & Chu, H-H. (2013). Sensor-embedded teeth for oral activity recognition. Proceedings of the 2013 international symposium on wearable computers, ISWC ’13, New York, ACM, pp. 41–44. ISBN 978-1-4503-2127-3. doi:  https://doi.org/10.1145/2493988.2494352.
  68. 68.
    Stellar, E., & Shrager, E. E. (1985). Chews and swallows and the microstructure of eating. The American Journal of Clinical Nutrition, 42(5 Suppl), 973–982. ISSN 0002-9165.Google Scholar
  69. 69.
    Bi, Y., Lv, M., Song, C., Xu, W., Guan, N., & Yi, W. (2016). AutoDietary: A wearable acoustic sensor system for food intake recognition in daily life. IEEE Sensors Journal, 16(3), 806–816. ISSN 1530-437X.  https://doi.org/10.1109/JSEN.2015.2469095.CrossRefGoogle Scholar
  70. 70.
    Olubanjo, T., & Ghovanloo, M. (2014). Tracheal activity recognition based on acoustic signals. Engineering in medicine and biology society (EMBC), 2014 36th annual international conference of the IEEE, IEEE, pp. 1436–1439.Google Scholar
  71. 71.
    Peyron, A., Lassauzay, C., & Woda, A. (2002). Effects of increased hardness on jaw movement and muscle activity during chewing of visco-elastic model foods. Experimental Brain Research, 142(1), 41–51.  https://doi.org/10.1007/s00221-001-0916-5.CrossRefGoogle Scholar
  72. 72.
    Wahl, F., Zhang, R., Freund, M., & Amft, O. (2017). Personalizing 3D-printed smart eyeglasses to augment daily life. IEEE Computer, 50(2), 26–35.  https://doi.org/10.1109/MC.2017.44.CrossRefGoogle Scholar
  73. 73.
    Zhang, R., & Amft, O. (2017). Monitoring chewing and eating in free-living using smart eyeglasses. IEEE Journal of Biomedical and Health Informatics, 99, 1–1. ISSN 2168–2194. doi:  https://doi.org/10.1109/JBHI.2017. 2698523.
  74. 74.
    Ono, T., Hori, K., & Nokubi, T. (2004). Pattern of tongue pressure on hard palate during swallowing. Dysphagia, 19(4), 259–264.CrossRefGoogle Scholar
  75. 75.
    Hori, K., Ono, T., Tamine, K., Kondo, J., Hamanaka, S., Maeda, Y., Dong, J., & Hatsuda, M. (2009). Newly developed sensor sheet for measuring tongue pressure during swallowing. Journal of Prosthodontic Research, 53(1), 28–32.CrossRefGoogle Scholar
  76. 76.
    Farooq, M., Fontana, J. M., & Sazonov, E. (2014). A novel approach for food intake detection using electroglottography. Physiological Measurement, 35(5), 739–751.  https://doi.org/10.1088/0967-3334/35/5/739.CrossRefGoogle Scholar
  77. 77.
    Cheng, J., Zhou,B., Kunze, K., Rheinlander, C. C., Wille, S., Wehn, N., Weppner, J., & Lukowicz, P. (2013). Activity recognition and nutrition monitoring in every day situations with a textile capacitive neckband. Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication, UbiComp ’13 Adjunct, New York, ACM, pp. 155–158. ISBN 978-1-4503-2215-7. doi:  https://doi.org/10.1145/2494091.2494143.
  78. 78.
    Kalantarian, H., Alshurafa, N., Le, T., & Sarrafzadeh, M. (2015). Monitoring eating habits using a piezoelectric sensor-based necklace. Computers in Biology and Medicine, 58, 46–55.  https://doi.org/10.1016/j.compbiomed.2015.01.005.CrossRefGoogle Scholar
  79. 79.
    Rahman, T., Adams, A. T., Zhang, M., Cherry, E., Zhou, B., Peng, H., & Choudhury, T. (2014). BodyBeat: A mobile system for sensing non-speech body sounds. MobiSys 2014: Proceedings of the 12th annual international conference on mobile systems, applications, and services, MobiSys ’14, Bretton Woods, New Hampshire, ACM, pp. 2–13. ISBN 978–1–4503-2793-0. doi:  https://doi.org/10.1145/2594368.2594386.
  80. 80.
    Yatani, K., & Truong, K. N. (2012). BodyScope: A wearable acoustic sensor for activity recognition. Ubicomp 2012: Proceedings of the 2012 ACM conference on Ubiquitous Computing, UbiComp ’12, Pittsburgh, Pennsylvania, ACM, p. 341–350. ISBN 978-1-4503-1224-0. doi:  https://doi.org/10.1145/2370216.2370269.
  81. 81.
    Sazonov, E., Schuckers, S., Lopez-Meyer, P., Makeyev, O., Sazonova, N., Melanson, E. L., & Neuman, M. (2008). Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior. Physiological Measurement, 29(5), 525–541.  https://doi.org/10.1088/0967-3334/29/5/001.CrossRefGoogle Scholar
  82. 82.
    Dong, B., & Biswas, S. (2014). Wearable sensing for liquid intake monitoring via apnea detection in breathing signals. Biomedical Engineering Letters, 4(4), 378–387. ISSN 2093-9868, 2093-985X.  https://doi.org/10.1007/ s13534-014-0149-8.CrossRefGoogle Scholar
  83. 83.
    Moriniere, S., Boiron, M., Alison, D., Makris, P., & Beutter, P. (2008). Origin of the sound components during pharyngeal swallowing in normal subjects. Dysphagia, 23(3), 267–273. ISSN 0179-051X, 1432-0460.  https://doi.org/10.1007/s00455-007-9134-z.CrossRefGoogle Scholar
  84. 84.
    Zhang, R., Freund, M., Amft, O., Cheng, J., Zhou, B., Lukowicz, P., Fernando, S., & Chabrecek, P. (2016). A generic sensor fabric for multi-modal swallowing sensing in regular upper-body shirts. Proceedings of the 2016 ACM international symposium on wearable computers, ISWC ’16, New York, USA, ACM, pp. 46–47. ISBN 978-1-4503-4460-9. doi:  https://doi.org/10.1145/2971763.2971785.
  85. 85.
    Costa, M. M. B., & de Oliveira Lemme, E. M. (2010). Coordination of respiration and swallowing: Functional pattern and relevance of vocal folds closure. Arquivos de Gastroenterologia, 47(1), 42–48. ISSN 0004-2803.  https://doi.org/10.1590/S0004-28032010000100008.CrossRefGoogle Scholar
  86. 86.
    Tomomasa, T., Morikawa, A., Sandler, R. H., Mansy, H. A., Koneko, H., Masahiko, T., Hyman, P. E., & Itoh, Z. (1999). Gastrointestinal sounds and migrating motor complex in fasted humans. The American Journal of Gastroenterology, 94(2), 374–381.CrossRefGoogle Scholar
  87. 87.
    Yamaguchi, K., Yamaguchi, T., Odaka, T., & Saisho, H. (2006). Evaluation of gastrointestinal motility by computerized analysis of abdominal auscultation findings. Journal of Gastroenterology and Hepatology, 21(3), 510–514.  https://doi.org/10.1111/j.1440-1746.2005.03997.x.CrossRefGoogle Scholar
  88. 88.
    Kirilina, S. I., Kusainov, R. K., Makukha, V. K., Mubarakshin, R. A., Poltaratskaya, E. S., & Sirota, G. G. (2016). The time-response characteristics of gastrointestinal motility. Actual problems of electronics instrument engineering (APEIE), 2016 13th international scientific-technical conference on, IEEE, Vol. 1, pp. 434–435.Google Scholar
  89. 89.
    Sazonova, N. A., Browning, R., & Sazonov, E. S. (2011). Prediction of bodyweight and energy expenditure using point pressure and foot acceleration measurements. The Open Biomedical Engineering Journal, 5, 110.CrossRefGoogle Scholar
  90. 90.
    Lu, L., Ji, R., & Liu, M. (2014). Design of real-time body weight monitor systems based on smart phones. Mechatronics and control (ICMC), 2014 international conference on, IEEE, pp. 1392–1396.Google Scholar
  91. 91.
    Hellstrom, P., Folke, M., & Ekstrom, M. (2015). Wearable weight estimation system. Procedia Computer Science, 64, 146–152.CrossRefGoogle Scholar
  92. 92.
    Parker, D. R., Carlisle, K., Cowan, F. J., Corrall, R. J., & Read, A. E. (1995). Post-prandial mesenteric blood flow in humans: Relationship to endogenous gastrointestinal hormone secretion and energy content of food. European Journal of Gastroenterology & Hepatology, 7(5), 435–440.Google Scholar
  93. 93.
    Asada, H. H., Shaltis, P., Reisner, A., Rhee, S., & Hutchinson, R. C. (2003). Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Engineering in Medicine and Biology Magazine, 22(3), 28–40.CrossRefGoogle Scholar
  94. 94.
    Westerterp-Plantenga, M. S., Wouters, L., & ten Hoor, F. (1990). Deceleration in cumulative food intake curves, changes in body temperature and diet-induced thermogenesis. Physiology & Behavior, 48(6), 831–836.  https://doi.org/10.1016/0031-9384(90)90235-V.CrossRefGoogle Scholar
  95. 95.
    Locher, I., Kirstein, T., & Troster, G. (2005). Temperature profile estimation with smart textiles. Proceedings of the international conference on intelligent textiles, smart clothing, well-being, and design, Tampere, Citeseer, pp. 19–20.Google Scholar
  96. 96.
    Dassau, E., Herrero, P., Zisser, H., Buckingham, B. A., Jovanovic, L., Man, C. D., Cobelli, C., Vehi, J., & Doyle, F. J. (2008). Implications of meal library & meal detection to glycemic control of type 1 diabetes mellitus through MPC control. IFAC Proceedings, 41(2), 4228–4233. ISSN 1474-6670.  https://doi.org/10.3182/20080706-5-KR-1001.00711.CrossRefGoogle Scholar
  97. 97.
    Xie, J., & Wang, Q. (2015). Meal detection and meal size estimation for type 1 diabetes treatment: A variable state dimension approach. ASME 2015 dynamic systems and control conference, p. V001T15A003. doi:  https://doi.org/10.1115/DSCC2015-9905.
  98. 98.
    Turksoy, K., Samadi, S., Feng, J., Littlejohn, E., Quinn, L., & Cinar, A. (2016). Meal detection in patients with type 1 diabetes: A new module for the multivariable adaptive artificial pancreas control system. IEEE Journal of Biomedical and Health Informatics, 20(1), 47–54. ISSN 2168-2194.  https://doi.org/10.1109/JBHI.2015.2446413.CrossRefGoogle Scholar
  99. 99.
    Kelly, P., Marshall, S. J., Badland, H., Kerr, J., Oliver, M., Doherty, A. R., & Foster, C. (2013). An ethical framework for automated, wearable cameras in health behavior research. American Journal of Preventive Medicine, 44(3), 314–319.CrossRefGoogle Scholar
  100. 100.
    Pettitt, C., Liu, J., Kwasnicki, R. M., Yang, G.-Z., Preston, T., & Frost, G. (2016). A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate. The British Journal of Nutrition, 115(1), 160–167. ISSN 1475-2662.  https://doi.org/10.1017/S0007114515004262.CrossRefGoogle Scholar
  101. 101.
    Gemming, L., Rush, E., Maddison, R., Doherty, A., Gant, N., Utter, J., & Mhurchu, C. N. (2015). Wearable cameras can reduce dietary under-reporting: Doubly labelled water validation of a camera-assisted 24 h recall. The British Journal of Nutrition, 113(2), 284–291. ISSN 1475-2662.  https://doi.org/10.1017/S0007114514003602.CrossRefGoogle Scholar
  102. 102.
    Sen, S., Subbaraju, V., Misra, A., Balan, R. K., & Lee, Y. (2015, March). The case for smartwatch-based diet monitoring. 2015 I.E. international conference on pervasive computing and communication workshops (PerCom workshops), pp. 585–590. doi:  https://doi.org/10.1109/PERCOMW.2015.7134103.
  103. 103.
    Jia, W., Chen, H.-C., Yue, Y., Li, Z., Fernstrom, J., Bai, Y., Li, C., & Sun, M. (2014). Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutrition, 17(8), 1671–1681. ISSN 1475-2727.  https://doi.org/10.1017/S1368980013003236.CrossRefGoogle Scholar
  104. 104.
    Chen, H.-C., Jia, W., Sun, X., Li, Z., Li, Y., Fernstrom, J. D., Burke, L. E., Baranowski, T., & Sun, M. (2015). Saliency-aware food image segmentation for personal dietary assessment using a wearable computer. Measurement Science & Technology, 26(2). ISSN 0957-0233).  https://doi.org/10.1088/0957-0233/26/2/025702.
  105. 105.
    Anthimopoulos, M., Dehais, J., Shevchik, S., Ransford, B. H., Duke, D., Diem, P., & Mougiakakou, S. (2015). Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones. Journal of Diabetes Science and Technology, 9(3), 507–515.  https://doi.org/10.1177/1932296815580159.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.ACTLab Research GroupUniversity of PassauPassauGermany

Personalised recommendations