Measuring Human Energy Intake Adam Hoover Electrical &

Measuring Human Energy Intake Adam Hoover Electrical &

Measuring Human Energy Intake Adam Hoover Electrical & Computer Engineering Department Outline

Motivation, existing tools, related work Tracking wrist motion to count bites Relating bites to calories Detecting eating activities during the day The Language of Eating Conclusion Motivation Prevalence of obesity

02/29/2020 Worldwide: 1.9 billion adults (39%) are overweight and 600 million adults (13%) are obese (WHO 2014). USA: 34.9% of adults are obese and 17% of children are obese (CDC 2010, Flegal et al. 2010). 3 Costs

Comorbidities: Diabetes, heart disease, high blood pressure, stroke, and higher rates of certain cancers (Wellman et al. 2002) Annual medical cost in the United States: $147 billion in 2008 (Finkelstein et al. 2009) (Mokdad et al, Actual causes of death in the United States, 2004, JAMA) 02/29/2020 4 Energy Measures Energy Intake (Calories or Joules)

Energy absorbed through food intake. Energy Expenditure (Calories or Joules) Homeostasis (body maintenance), exercise. <- weight change -> 02/29/2020 5 Clinical Tools

Calorimetry Chamber (measures EE) Bomb Calorimeter (measures EI if exact same serving fully consumed) *Over a week, EI EE = weight change Doubly Labeled Water

(measures EE directly, EI indirectly*) Free-Living Tools: EE EE: physical activity monitors, pedometers 7 Free-Living Tools: EI EI: food diary, manual counting, database-assisted logs

Problem #1: Compliance (not easy to use for long period of time) Problem #2: Underestimation/underreporting bias (dozens of studies have found it ranges 10-50%, evaluated using doubly labeled water) Challenge: Develop body-worn sensors similar to activity monitors 8 Related work Wearable sensor-based approaches

Throat and ear (Sazonov et al. 2010) Lanyard camera (Gemming et al. 2013) Arms and back (Amft et al. 2008) Detect swallows, chewing sounds Recognize eating gestures Challenges: Compliance (social stigma, comfort), accuracy 02/29/2020

9 Our concept: Bite Counter Audible alarms to queue behaviors such as slowing eating or portion control Worn like a watch

Tracks wrist motion to detect eating activities and count bites (hand-to-mouth gestures) 02/29/2020 10 Outline

Motivation, existing tools, related work Tracking wrist motion to count bites Relating bites to calories Detecting eating activities during the day The Language of Eating Conclusion

Wrist Roll Motion Wrist rolls to get food from table to mouth Roll is independent of other axes of motion Algorithm The wrist undergoes a characteristic roll motion during the taking of a bite of food that can be tracked using a gyroscope Biologically, this can be related to the necessary orientations for (1) picking food up, and (2) placing food into the mouth

Demo of Bite Counting Early test: 49 meals (47 participants), 1675 bites 86% bites detected, 81% positive predictive value Talking and other actions between 67% of bites Cafeteria Experiment Main food service for Clemson University Seats ~800 people

Huge variety of foods and beverages Cafeteria Experiment 276 participants (1 meal each) 380 different foods and beverages consumed 22,383 total bites 82% bites detected, 82% positive predictive value Bite Counting Accuracy

most accurate food: salad bar (88%) least accurate food: ice cream cone (39%) Accuracy increases with age (77% 18-30, 88% 50+) Minor variations in accuracy due to utensil, container, gender, ethnicity Currently studying this Bite Database Outline

Motivation, existing tools, related work Tracking wrist motion to count bites Relating bites to calories Detecting eating activities during the day The Language of Eating

Conclusion Embedded System Design Audible alarm On/off button Lab model Watch model

Stores timestamped log of meals (bite count) Bite-to-Calorie Correlation each point = 1 meal 2 weeks data (~50 meals), 1 person Correlation Test 83 subjects wore for 2 weeks, 3246 total meals each plot = 1 person

0.4 correlation 0.7 correlation Correlation Comparison Physical activity monitors 1 Energy expenditure Our device Energy intake

76% 0.4 Westerterp & Plasqui, 2007, "Physical Activity Assessment with Accelerometers: An Evaluation against Doubly Labeled Water", in Obesity, vol 15, pp 2371-2379. 1 Converting Bites to Calories kpb = kilocalories per bite Formula based on height (h), weight (w), age (a) kpb (male) = 0.2455 h + 0.0449 w 0.2478 a kpb (female) = 0.1342 h + 0.0290 w 0.0534 a Formula fit using 83-people 2-week data set

Tested on 276 meals cafeteria data set Calories in Cafeteria Meals Error: Mean and Variance Outline

Motivation, existing tools, related work Tracking wrist motion to count bites Relating bites to calories Detecting eating activities during the day The Language of Eating Conclusion All Day Wrist Tracking Sum of acceleration shows peaks preceding and following meals

02/29/2020 27 Algorithm Segment at peaks Calculate features of segments Classify using Bayesian classifier

Tested on 43 subjects, 449 total hours (8-12 hours per subject), containing 116 meals/snacks 81% accuracy in detecting eating activity at 1 second resolution Outline

Motivation, existing tools, related work Tracking wrist motion to count bites Relating bites to calories Detecting eating activities during the day The Language of Eating Conclusion Language Recognition Context of preceding words helps recognition of subsequent words

02/29/2020 30 Eating Gesture Recognition Most likely a bite is coming next 02/29/2020 31

Hidden Markov Models : ( | , , , : ( |

? Baseline classifiers (use no history): HMM (recognize each gesture independently) KNN (most similar gesture) Results More contextual history improves recognition accuracy

Outline Motivation, existing tools, related work Tracking wrist motion to count bites Relating bites to calories Detecting eating activities during the day

The Language of Eating Conclusion Applications Weight loss/maintenance Objective, automated monitoring Cognitive workload Offload energy intake monitoring Real-time feedback The device can give cues to stop eating Observation Applications

time of day #bites Acknowledgments Collaborators

Adam Hoover, Electrical & Computer Engineering Department, Clemson University Eric Muth, Psychology Department, Clemson University Students: Yujie Dong, Jenna Scisco, Raul Ramos-Garcia, James Salley, Mike Wilson, Surya Sharma, Ziqing Huang, Soheila Eskandari, Yiru Shen, Phil Jasper, Amelia Kinsella, Jose Reyes, Meredith Drennan, Xueting Yu, Michael Wooten, Megan Becvarik, Ryan Mattfeld Pat ONeil, Weight Management Center, Medical University of South Carolina

Kevin Hall, Laboratory Biological Modeling, NIH Kathleen Melanson, Slowing Eating, University of Rhode Island Brie Turner-McGrievy, University of South Carolina Corby Martin, Pennington Biomedical Research Center, LSU Funding

NIH NIDDK STTR 1R41DK091141-01A1, 2R42DK091141-02 NIH NHLBI R01 HL118181-01A1 NIH NCI R21 CA187929-01A1 South Carolina Launch South Carolina Clinical and Translational Institute 02/29/2020 37 Questions?

For more info: www.ces.clemson.edu/~ahoover

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