Skip to main content

Advertisement

Log in

A New Method for Measuring Meal Intake in Humans via Automated Wrist Motion Tracking

  • Published:
Applied Psychophysiology and Biofeedback Aims and scope Submit manuscript

Abstract

Measuring the energy intake (kcal) of a person in day-to-day life is difficult. The best laboratory tool achieves 95 % accuracy on average, while tools used in daily living typically achieve 60–80 % accuracy. This paper describes a new method for measuring intake via automated tracking of wrist motion. Our method uses a watch-like device with a micro-electro-mechanical gyroscope to detect and record when an individual has taken a bite of food. Two tests of the accuracy of our device in counting bites found that our method has 94 % sensitivity in a controlled meal setting and 86 % sensitivity in an uncontrolled meal setting, with one false positive per every 5 bites in both settings. Preliminary data from daily living indicates that bites measured by the device are positively related to caloric intake illustrating the potential of the device to monitor energy intake. Future research should seek to further explore the relationship between bites taken and kilocalories consumed to validate the device as an automated measure of energy intake.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Amft, O., Junker, H., & Troster, G. (2005). Detection of eating and drinking arm gestures using inertial body-worn sensors. Proceedings of international symposium on wearable computers, (pp. 160–163).

  • Amft, O., & Troster, G. (2008). Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine, 42, 121–136.

    Article  PubMed  Google Scholar 

  • Amft, O., & Troster, G. (2009). On-body sensing solutions for automatic dietary monitoring. Pervasive Computing, 62–70.

  • Antipatis, V., & Gill, T. (2001). Obesity as a global problem. In P. Bjorntorp (Eds.), International textbook of obesity (pp. 3–22). London: Wiley.

    Google Scholar 

  • Arab, L., Wesseling-Pery, K., Jardack, P., Henry, J., & Winter, A. (2010). Eight self-administered 24-hour dietary recalls using the internet are feasible in African Americans and Whites. Journal of the American Dietetics Association, 110(6), 857–864.

    Article  Google Scholar 

  • Beasley, J., Riley, W., & Jean-Mary, J. (2005). Accuracy of a PDA-based dietary assessment program. Nutrition, 21, 672–677.

    Article  PubMed  Google Scholar 

  • Black, A., & Cole, T. (2000). Within- and between-subject variation in energy expenditure measured by the doubly-labelled water technique: Implications for validating reported dietary energy intake. European Journal of Clinical Nutrition, 54, 386–394.

    Article  PubMed  Google Scholar 

  • Boushey, C., Kerr, D., Wright, J., Lutes, K., Ebert, D., & Delp, E. (2009). Use of technology in children’s dietary assessment. European Journal of Clinical Nutrition, 63, S50–S57.

    Article  PubMed  Google Scholar 

  • Brunner, E., Stallone, D., Maneesh, J., Bingham, S., & Marmot, M. (2001). Dietary assessment in whitehall II: Comparison of 7d diet diary and food-frequency questionnaire and validity against biomarkers. British Journal of Nutrition, 86, 405–414.

    Article  PubMed  Google Scholar 

  • Burke, L., Wang, J., & Sevick, M. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111(1), 92–102.

    Article  PubMed  Google Scholar 

  • Burrows, T., Martin, R., & Collins, C. (2010). A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. Journal of the American Dietetic Association, 110(10), 1501–1510.

    Article  PubMed  Google Scholar 

  • Champagne, C., Bray, G., Kurtz, A., Monteiro, J., Tucker, E., Volaufovaand, J., et al. (2002). Energy intake and energy expenditure: A controlled study comparing dietitians and non-dietitians. Journal of the American Dietetic Association, 102(10), 1428–1432.

    Article  PubMed  Google Scholar 

  • Chang, K., Liu, S., Chu, H., Hsu, J., Chen, C., Lin, T., et al. (2006). The diet-aware dining table: Observing dietary behaviors over a tabletop surface. Proceedings of 4th international conference on pervasive computing (Vol. 3968, pp. 366–382).

  • Day, N., McKeown, N., Wong, M., Welch, A., & Bingham, S. (2001). Epidemiological assessment of diet: A comparison of 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. International Journal of Epidemiology, 30, 309–317.

    Article  PubMed  Google Scholar 

  • Dong, Y. (2009). A device for detecting and counting bites of food taken by a person during eating. Master’s Thesis, Electrical & Computer Engineering Dept., Clemson University.

  • Dong, Y., Hoover, A., & Muth, E. (2009). A device for detecting and counting bites of food taken by a person during eating. IEEE conference on bioinformatics and biomedicine, 265–268.

  • Dong, Y., Hoover, A., Scisco, J., & Muth, E. (2011). Detecting eating using a wrist mounted device during normal daily activities. International conference on embedded systems and applications.

  • Drennan, M. (2010). An assessment of linear wrist motion during the taking of a bite of food. Master’s Thesis, Electrical & Computer Engineering Dept., Clemson University.

  • Finkelstein, E., Trogdon, J., Cohen, J., & Dietz, W. (2009). Annual medical spending attributable to obesity: Payer- and service-specific estimates. Health Affairs, 28, w822–w831.

    Article  PubMed  Google Scholar 

  • Flegal, K., Carroll, M., Ogden, C., & Curtin, L. (2010). Prevalence and trends in obesity among us adults, 1999–2008. Journal of the American Medical Association, 303, 235–241.

    Article  PubMed  Google Scholar 

  • Glanz, K., Brug, J., & Assema, P. van (1997). Are awareness of dietary food intake and actual fat consumption associated? A Dutch–American comparison. European Journal of Clinical Nutrition, 51, 542–547.

    Article  PubMed  Google Scholar 

  • Hargrove, J. (2007). Does the history of food energy units suggest a solution to calorie confusion. Nutrition Journal, 6(44).

  • Jonnalagadda, S., Mitchell, D., Smiciklas-Wright, H., Meaker, K., Heel, N., Karmally, W., et al. (2000). Accuracy of energy intake data estimated by a multiple-pass, 24-hour dietary recall technique. Journal of the American Dietetic Association, 100(3), 303–311.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kirk, S., Penney, T., & McHugh, T. (2010). Characterizing the obesogenic environment: The state of the evidence with directions for future research. Obesity Reviews, 11, 109–117.

    Article  PubMed  Google Scholar 

  • Kissileff, H., Klingsberg, G., & Itallie, T. V. (1980). Universal eating monitor for continuous recording of solid or liquid consumption in man. American Journal of Physiology, 238(1), R14–R22.

    PubMed  Google Scholar 

  • Lichtman, S., Pisarska, K., Berman, E., Pestone, M., Dowling, H., Offenbacher, E., et al. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. The New England Journal of Medicine, 327(27), 1894–1898.

    Article  Google Scholar 

  • Lopez-Meyer, P., Makeyev, O., Schuckers, S., Melanson, E., Neuman, M., & Sazonov, E. (2010). Detection of food intake from swallowing sequences by supervised and unsupervised methods. Annals of Biomedical Engineering, 38(8), 2766–2774.

    Article  PubMed  Google Scholar 

  • Martin, C., Anton, S., York-Crowe, E., Heilbronn, L., VanSkiver, C., Redman, L., et al. (2007). Empirical evaluation of the ability to learn a calorie counting system and estimate portion size and food intake. British Journal of Nutrition, 98, 439–444.

    Article  PubMed  Google Scholar 

  • Martin, C., Han, H., Coulon, S., Allen, H., Champagne, C., & Anton, S. (2009). A novel method to remotely measure food intake of free-living people in real-time: The remote food photography method (rfpm). British Journal of Nutrition, 101(3), 446–456.

    Article  PubMed  Google Scholar 

  • McCabe-Sellers, B. (2010). Advancing the art and science of dietary assessment through technology. Journal of the American Dietetic Association, 110(1), 52–54.

    Article  PubMed  Google Scholar 

  • Muhlheim, L., Allison, D., Heshka, S., & Heymsfield, S. (1998). Do unsuccessful dieters intentionally underreport food intake?. International Journal of Eating Disorders, 24, 259–266.

    Article  PubMed  Google Scholar 

  • Muller, M., Bosy-Westphal, A., & Krawczak, M. (2010). Genetic studies of common types of obesity: A critique of the current use of phenotypes. Obesity Reviews, 11, 612–618.

    Article  PubMed  Google Scholar 

  • Plasque, G., & Westerterp, K. (2007). Physical activity assessment with accelerometers: An evaluation against doubly labeled water. Obesity, 15(10), 2371–2379.

    Article  Google Scholar 

  • Roberto, C., Larsen, P., Agnew, A., Balk, J., & Brownell, K. (2010). Evaluating the impact of menu labeling on food choices and intake. American Journal of Public Health, 100, 312–318.

    Article  PubMed  Google Scholar 

  • Saeik, Y., & Takeda, F. (2005). Proposal of food intake measurement system in medical use and its discussion of practical capability. Lecture Notes in Computer Science, 3683, 1266–1273.

    Article  Google Scholar 

  • Sazonov, E., Makeyev, O., Schuckers, S., Lopez-Meyer, P., Melanson, E., & Neuman, M. (2010). Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior. IEEE Transactions on Biomedical Engineering, 57(3), 626–633.

    Article  PubMed  Google Scholar 

  • Sazonov, E., & Schuckers, S. (2010). The energetics of obesity: A review: Monitoring energy intake and energy expenditure in humans. IEEE Engineering in Medicine and Biology Magazine, 29(1), 31–35.

    Article  PubMed  Google Scholar 

  • Sazonov, E., Schuckers, S., Lopez-Meyer, P., Makeyev, O., Melanson, E., Neuman, M., et al. (2009). Toward objective monitoring of ingestive behavior in free-living population. Obesity, 17(10), 1971–1975.

    Article  PubMed  Google Scholar 

  • Sazonov, E., Schuckers, S., Lopez-Meyer, P., Makeyev, O., Sazonova, N., Melanson, E., et al. (2008). Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior. Physiological Measurement, 29(5), 525–541.

    Article  PubMed  Google Scholar 

  • Schoeller, D. (1988). Measurement of energy expenditure in free-living humans by using doubly labeled water. Journal of Nutrition, 118, 1278–1289.

    PubMed  Google Scholar 

  • Six, B., Schap, T., Zhu, F., Mariappan, A., Bosch, M., Delp, F., et al. (2010). Evidence-based development of a mobile telephone food record. Journal of the American Dietetic Association, 110(1), 74–79.

    Article  PubMed  Google Scholar 

  • Speakman, J. (1997). Doubly labelled water—theory and practice (1 ed.). Berlin: Springer.

    Google Scholar 

  • STMicroelectronics. (2011, December 5). Mems inertial sensor, LPR410AL gyroscope.

  • Takeda, F., Kumada, K., & Takara, M. (2003). Dish extraction method with neural network for food intake measuring system on medical use. Proceedings of IEEE international symposium on computational intelligence for measurement system and applications, (pp. 56–59).

  • Thompson, F., & Subar, A. (2008). Dietary assessment methodology. In A. Coulston & C. Boushey (Eds.), Nutrition in the prevention and treatment of disease (2 ed.). New York: Academic Press.

    Google Scholar 

  • Thompson, F., Subar, A., Loria, C., Reedy, J., & Baranowski, T. (2010). Need for technological innovation in dietary assessment. Journal of American Dietetic Association, 110(1), 48–51.

    Article  Google Scholar 

  • Tooze, J., Subar, A., Thompson, F., Troiano, R., Schatzkin, A., & Kipnis, V. (2004). Psychosocial predictors of energy underreporting in a large doubly labeled water study. American Journal of Clinical Nutrition, 79, 795–804.

    PubMed  Google Scholar 

  • Williamson, D., Allen, H., Martin, P., Alfonso, A., Gerald, B., & Hunt, A. (2003). Comparison of digital photography to weighed and visual estimation of portion sizes. Journal of the American Dietetic Association, 103(9), 1139–1145.

    Article  PubMed  Google Scholar 

  • Wing, R., & Hill, J. (2001). Successful weight loss maintenance. Annual Review of Nutrition, 21, 323–341.

    Article  PubMed  Google Scholar 

  • Wing, R., & Phelan, S. (2005). Long-term weight loss maintenance. American Journal of Clinical Nutrition, 82, 222S–225S.

    PubMed  Google Scholar 

  • World Health Organization (2010, April 19). Obesity and overweight.

  • Yon, B., Johnson, R., Harvey-Berino, J., & Gold, J. (2006). The use of a personal digital assistant for dietary self-monitoring does not improve the validity of self-reports of energy intake. Journal of the American Dietetics Association, 106(8), 1256–1259.

    Article  Google Scholar 

  • Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C., Ebert, D., et al. (2010). The use of mobile devices in aiding dietary assessment and evaluation. IEEE Journal of Selected Topics in Signal Processing, 4(4), 756–766.

    Article  PubMed  Google Scholar 

  • Zhu, F., Mariappan, A., Boushey, C., Kerr, D., Lutes, K., Ebert, D., et al. (2008). Technology-assisted dietary assessment. Proceedings of SPIE: Computational Imaging VI (Vol. 6814, pp. 1–10).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adam Hoover.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dong, Y., Hoover, A., Scisco, J. et al. A New Method for Measuring Meal Intake in Humans via Automated Wrist Motion Tracking. Appl Psychophysiol Biofeedback 37, 205–215 (2012). https://doi.org/10.1007/s10484-012-9194-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10484-012-9194-1

Keywords

Navigation