Automatic meal intake monitoring using Hidden Markov Models

Abstract In the latest years, the number of elderly people that has been living alone and need regular support has highly increased. Meal intake monitoring is a well-known strategy that enables premature detection of health problems. There are several attempts to develop automatic meal intake monitoring systems, but they are inadequate to monitor elderly people at home. In this context, we propose an automatic meal intake monitoring system that helps tracking people's eating behaviors, and is adequate for elderly remote monitoring at home due to its nonintrusive features. The system uses the MS Kinect sensor that provides the coordinates of the user's sitting skeleton during his meals. It analyzes the coordinates, detects eating gestures, and classifies them using Hidden Markov Models (HMMs) to estimate the user's eating behavior. A demonstrative prototype for detection and classification of gestures was implemented and tested. The detection module got satisfactory percentages of sensitivity, having a minimum of 72.7% and a maximum of 90%. The Classification module was tested with 3 proposed methods and the best method had a good average percentage of success (approximately 83%) in the classification of Soup and Main dish; regarding the left hand transporting Liquids, the results were less successful.

[1]  J. B. Monteiro,et al.  Fatores que afetam o consumo alimentar e a nutrição do idoso , 2000 .

[2]  M. Sevick,et al.  A preliminary study of PDA-based dietary self-monitoring in hemodialysis patients. , 2005, Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation.

[3]  Donald T. Rowland,et al.  Population Aging: The Transformation of Societies , 2012 .

[4]  Cristina Videira Lopes,et al.  Monitoring Intake Gestures using Sensor Fusion (Microsoft Kinect and Inertial Sensors) for Smart Hom , 2012 .

[5]  Howard D. Wactlar,et al.  Dining activity analysis using a hidden Markov model , 2004, ICPR 2004.

[6]  Junichi Yamagishi,et al.  An Introduction to HMM-Based Speech Synthesis , 2006 .

[7]  Luís Pádua,et al.  Evaluation of MS kinect for elderly meal intake monitoring , 2014 .

[8]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[9]  Rachel K. Johnson,et al.  The use of a personal digital assistant for dietary self-monitoring does not improve the validity of self-reports of energy intake. , 2006, Journal of the American Dietetic Association.

[10]  Paul Lukowicz,et al.  Monitoring Dietary Behavior with a Smart Dining Tray , 2015, IEEE Pervasive Computing.

[11]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[12]  Gregory D. Abowd,et al.  A practical approach for recognizing eating moments with wrist-mounted inertial sensing , 2015, UbiComp.

[13]  Jane Yung-jen Hsu,et al.  The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface , 2006, Pervasive.

[14]  Yvonne Rogers,et al.  When Do We Eat? An Evaluation of Food Items Input into an Electronic Food Monitoring Application , 2006, 2006 Pervasive Health Conference and Workshops.

[15]  Mark Stamp,et al.  A Revealing Introduction to Hidden Markov Models , 2017 .

[16]  Rachel K. Johnson,et al.  Personal Digital Assistants are Comparable to Traditional Diaries for Dietary Self-Monitoring During a Weight Loss Program , 2007, Journal of Behavioral Medicine.