Automated Ingestion Detection for a Health Monitoring System

Obesity is a global epidemic that imposes a financial burden and increased risk for a myriad of chronic diseases. Presented here is an overview of a prototype automated ingestion detection (AID) process implemented in a health monitoring system (HMS). The automated detection of ingestion supports personal record keeping which is essential during obesity management. Personal record keeping allows the care provider to monitor the therapeutic progress of a patient. The AID-HMS determines the levels of ingestion activity from sounds captured by an external throat microphone. Features are extracted from the sound recording and presented to machine learning classifiers, where a simple voting procedure is employed to determine instances of ingestion. Using a dataset acquired from seven individuals consisting of consumption of liquid and solid, speech, and miscellaneous sounds, > 94% of ingestion sounds are correctly identified with false positive rates around 9% based on 10-fold cross validation. The detected levels of ingestion activity are transmitted and stored on a remote web server, where information is displayed through a web application operating in a web browser. This information allows remote users (health provider) determine meal lengths and levels of ingestion activity during the meal. The AID-HMS also provides a basis for automated reinforcement for the patient.

[1]  Gerhard Tröster,et al.  Methods for Detection and Classification of Normal Swallowing from Muscle Activation and Sound , 2006, 2006 Pervasive Health Conference and Workshops.

[2]  D. Frankenfield,et al.  Validation of several established equations for resting metabolic rate in obese and nonobese people. , 2003, Journal of the American Dietetic Association.

[3]  C. Taylor,et al.  Evaluation of a nurse-care management system to improve outcomes in patients with complicated diabetes. , 2003, Diabetes care.

[4]  Astro Teller,et al.  The Development of the SenseWear ® armband , a Revolutionary Energy Assessment Device to Assess Physical Activity and Lifestyle , 2006 .

[5]  Michael R Neuman,et al.  Toward Objective Monitoring of Ingestive Behavior in Free‐living Population , 2009, Obesity.

[6]  Carl Taswell.  Near-best basis selection algorithms with non-additive information cost functions , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[7]  Edward Sazonov,et al.  Abstract—the Detection of Swallowing Events by Acoustic , 2011 .

[8]  D. Bhatia,et al.  Energy-efficient ambulatory activity monitoring for disease management , 2008, 2008 5th International Summer School and Symposium on Medical Devices and Biosensors.

[9]  Charlene Compher,et al.  Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. , 2005, Journal of the American Dietetic Association.

[10]  K. Saito,et al.  Tooth shape reconstruction from ct images using spline Curves , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[11]  William Walker,et al.  Mobile health monitoring systems , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Astro Teller.  A platform for wearable physiological computing , 2004, Interact. Comput..

[13]  E. Y. Hamid,et al.  Wavelet-based data compression of power system disturbances using the minimum description length criterion , 2001 .

[14]  Michael R. Neuman,et al.  Recognition of Swallowing Sounds Using Time-Frequency Decomposition and Limited Receptive Area Neural Classifier , 2008, SGAI Conf..

[15]  Michael R. Neuman,et al.  Automatic Detection of Swallowing Events by Acoustical Means for Applications of Monitoring of Ingestive Behavior , 2010, IEEE Transactions on Biomedical Engineering.

[16]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[17]  S. Rössner,et al.  Obesity: the disease of the twenty-first century , 2002, International Journal of Obesity.

[18]  D. Tate,et al.  Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. , 2003, JAMA.

[19]  David B Allison,et al.  Clinical implications of obesity with specific focus on cardiovascular disease: a statement for professionals from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism: endorsed by the American College of Cardiology Foundation. , 2004, Circulation.

[20]  Jo Shapiro,et al.  Evaluation and treatment of swallowing disorders , 1992, Comprehensive therapy.

[21]  Gerhard Tröster,et al.  On-Body Sensing Solutions for Automatic Dietary Monitoring , 2009, IEEE Pervasive Computing.

[22]  Kevin G Volpp,et al.  Financial incentive-based approaches for weight loss: a randomized trial. , 2008, JAMA.

[23]  R. Callister,et al.  The SHED‐IT Randomized Controlled Trial: Evaluation of an Internet‐based Weight‐loss Program for Men , 2009, Obesity.

[24]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[25]  Naoki Saito,et al.  Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum-description-length criterion , 1994, Defense, Security, and Sensing.

[26]  Zahra Moussavi,et al.  Comparison of recurrence plot features of swallowing and breath sounds , 2008 .

[27]  Dinesh Bhatia,et al.  Power efficient multi-band contextual activity monitoring for assistive environments , 2008, PETRA '08.

[28]  J.O. Hill.  Novel engineering approaches to obesity, overweight, and energy balance: public health needs and research opportunities , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Kenji Suzuki,et al.  A neck mounted interface for sensing the swallowing activity based on swallowing sound , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  E. Finkelstein,et al.  Annual medical spending attributable to obesity: payer-and service-specific estimates. , 2009, Health affairs.

[31]  Z. Moussavi,et al.  Automated Extraction of Swallowing Sounds Using a Wavelet-Based Filter , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  F Xavier Pi-Sunyer,et al.  The obesity epidemic: pathophysiology and consequences of obesity. , 2002, Obesity research.

[33]  Zahra Moussavi,et al.  Analysis of swallowing sounds using hidden Markov models , 2007, Medical & Biological Engineering & Computing.

[34]  I. Tetens,et al.  The potential association between fruit intake and body weight – a review , 2009, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[35]  A. Hande,et al.  Remote Blood Pressure Monitoring Using a Wireless Sensor Network , 2006 .

[36]  J. Levine,et al.  Non-Exercise Activity Thermogenesis: The Crouching Tiger Hidden Dragon of Societal Weight Gain , 2006, Arteriosclerosis, thrombosis, and vascular biology.

[37]  R. Winett,et al.  Using Internet technology to deliver a behavioral weight loss program. , 2001, JAMA.

[38]  Z. Moussavi,et al.  Analysis of Temporal Pattern of Swallowing Mechanism , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  Gerhard Tröster,et al.  Recognition of dietary activity events using on-body sensors , 2008, Artif. Intell. Medicine.

[40]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[41]  Scott R. Youmans.  Increasing the Objectivity of the Clinical Dysphagia Evaluation , 2003 .

[42]  Edward Sazonov,et al.  Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior. , 2008, Physiological measurement.

[43]  Gerhard Tröster,et al.  Detection of eating and drinking arm gestures using inertial body-worn sensors , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[44]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[45]  Mukta Bhatele,et al.  Remote Blood Pressure Monitoring Using a Wireless Sensor Network , 2013 .

[46]  Paul Lukowicz,et al.  Analysis of Chewing Sounds for Dietary Monitoring , 2005, UbiComp.

[47]  Zahra Moussavi,et al.  Swallowing sound detection using hidden markov modeling of recurrence plot features , 2009 .

[48]  S. Goodwin,et al.  The Practical Guide to the Identification, Evaluation and Treatment of Overweight and Obesity in Adults , 2002 .

[49]  M Sharma,et al.  Behavioural interventions for preventing and treating obesity in adults , 2007, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[50]  A. Yadollahi,et al.  A model for normal swallowing sounds generation based on wavelet analysis , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[51]  James A Levine,et al.  Non-exercise activity thermogenesis , 2003, Proceedings of the Nutrition Society.

[52]  L. March,et al.  Influence of BMI on Health‐related Quality of Life: Comparison Between an Obese Adult Cohort and Age‐matched Population Norms , 2009, Obesity.

[53]  Arya M. Sharma,et al.  Obesity is a sign – over‐eating is a symptom: an aetiological framework for the assessment and management of obesity , 2010, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[54]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[55]  M. Mifflin,et al.  A new predictive equation for resting energy expenditure in healthy individuals. , 1990, The American journal of clinical nutrition.

[56]  Martin Vetterli,et al.  Wavelets and filter banks: theory and design , 1992, IEEE Trans. Signal Process..

[57]  G A Colditz,et al.  Impact of overweight on the risk of developing common chronic diseases during a 10-year period. , 2001, Archives of internal medicine.