Bite detection and differentiation using templates of wrist motion MS Defense Exam Soheila Eskandari Committee members: Dr. Adam Hoover (chair) Dr. John N. Gowdy Dr. Eric R. Muth December 5 th, 2013 Department of Electrical and Computer Engineering

Outline ◦ Motivation and Background ◦ Methods ◦ Results ◦ Conclusions 2

Motivation 3 One third of U.S. adults were overweight and another one third were obese in (reported by NHANES) Cost associated with obesity was $117 billion in the US in 2000

Obesity treatments Weight maintenance goal is to achieve : EI=EE The problem is with the tools people use to measure EI

Mobile Health technologies Mobile monitoring of the human electrocardiogram (ECG) Heart rate, Breathing frequency, Blood pressure variations, Breathing amplitude. Detection of different sleep phases

Wrist motion tracking Dong et al. [7,8] developed a wrist-worn device to track wrist motion and measure the number of bites taken during a meal. Additional research showed that bites, automatically counted using this method, correlated with self-reported caloric intake at the meal level at 0.5. Amft [1] developed a wrist-worn device with the primary objective of detecting drinking activities, the container used, and the fluid level. Junker and Amft [1,2] presented a recognition system that used five inertial sensors located on the wrists, upper arms, and upper torso. Their research describes motion gestures based on the particular utensil used, establishing four gestures (cutlery, drink, spoon, hands). 6

Bite detection based on threshold method by wrist motion tracking T1 and T2 : The roll velocities T3 : Time interval between the first and second events of roll motion T4 : Time interval between the end of one bite and beginning of the next bite Tested on total of 276 subjects 22,383 bites True detection rate of 76% with a positive predictive value of 87% Adjusting the second timing threshold (T4): True detection rate of 82% and a positive predictive value of 82% Threshold algorithm: Let EVENT = 0 Loop Let V_t = measured roll vel. at time t if V_t > T1 and EVENT = 0 EVENT = 1 Let s=t if V_t T3 and EVENT = 1 Bite detected Let s=t EVENT = 2 if EVENT = 2 and T-s>T4 EVENT = 0 7

Template matching 8

Methods Data collection Bite templates Bite differentiation Bite detection 9

Data collection 10

Data collection tools: 11

Ground truth Total of 22,383 bites

Bite Templates Determine the overall pattern and variability pattern of wrist motion of a bite Created by : Using both the accelerometers and gyroscopes data Averaging the motion data across all the bites in the 22,383 total ground truth bites Over a six second window centered on the bite time Templates of food and drink bites Four different types of food bites:  bites taken with a fork,  bites taken with a spoon,  food bites eaten using one hand  food bites eaten using both hands

Bite differentiation Recognizing different types of bites using template matching against the typical motion pattern 14 ? ? ?

Algorithm:

Bite detection Detect the bites from other activities during a meal by template matching based on just roll motion Steps:  Sum of absolute difference between a bite template and the wrist motion data at every time step  Detecting local minima  Best template matched at the local minima position Detected bite

Ground truth bites Computer detected bites

Results Bite templates Bite differentiation Bite detection 18

Total bite templates 19

17,166 ground truth food bites 3,185 bites drink bites 20 Food bites (17,166 bites) Drink bites (3,185 bites)

Food bites larger average motion in the Z and roll axes Drink bites larger average motion in the X and yaw axes 21 Food bites (17,166 bites) Drink bites (3,185 bites)

Drink bites longer (slower) motion than food bites in the yaw axis. Roll motion for drink bites is opposite to food bites, with negative roll preceding positive roll. 22 Food bites (17,166 bites) Drink bites (3,185 bites)

23 Food bites (17,166 bites) Drink bites (3,185 bites) Food bites opposite average motion with drink bites in roll axes

24 Fork (8,764 bites) Spoon (1,986 bites) Single hand (9,241 bites) Both hand (2,441 bites) Ax Ay Az Yaw Pitch Roll

Bite differentiation Ground truth Computer detected Food (Ax,Ay,Az,Yaw,Pitch,Roll)Drink(Ax,Ay,Az,Yaw,Pitch,Roll) Food75%,72%,68%,72%,43%,64%25%,28%,32%,27%,57%,36% Drink13%,10%,12%,40%,19%,5.6%87%,90%,88%,60%,81%,94% Accuracy81%,81%,78%,66%,62%,79% 25 Ground truth Computer detected FoodDrink Food70%30% Drink5%95% Accuracy83%  Bite differentiation of food and drink bites using all 6 motion axes.  Accelerometer and gyroscope motions confusion table for food & drink bites recognition.

Confusion matrices for the five types of bites according to utensil, for each axis Overall accuracy for recognizing for the 4 different types of utensils :19- 48% and Drink: 80%  Confusion Accelerometer motion axes. 26 Ground truth (Ax,Ay,Az) Computer detected (Ax,Ay,Az) %ForkSpoonDrinkBoth handSingle hand Fork23,20,2149,56,514,5,613,9,1012,10,1.4 Spoon19,14,1820,64,604.3,5,514,8.5,98,9,9 Drink1,1,15,4.5,681,84,826,6,6.57,5,5 Both hand8,6,730,36,3118,28,3728,18,1617,12,10 Single hand15,11,1021,27,2820,28,3519,15,1125,19,17 Accuracy42,41,39 %

 Confusion gyroscope motion axes. 27 Ground truth (Yaw, Pitch, Roll) Computer detected (Yaw,Pitch,Roll) %ForkSpoonDrinkBoth handSingle hand Fork42,14,4931,33,1714,25,86,13,127,14,14 Spoon40,9,3738,40,2011,25,135,11,147,15,16 Drink28,3,1.5 10,10,1.6 51,48,7110,27,181.5,12,8 Both hand41,6,417,19,628,31,409,31,326,13,19 Single hand36,8,3529,27,1220,31,187,16,1810,19,18 Accuracy30, 31, 38

 Confusion combining all 6 motion axes. 28 Ground truth (Ax, Ay, Az, Yaw, Pitch, Roll) Computer detected (Ax,Ay,Az,Yaw,Pitch,Roll) %ForkSpoonDrinkBoth handSingle hand Fork Spoon Drink Both hand Single hand Accuracy 46

Bite detection Tested on 22,383 total bites Detection rate: 48% Positive predictive value: 75% No higher performance for different axes and different combinations of axes 29

Conclusions Food and drink bites appear to have different wrist motion patterns Different types of utensils for food bites also appear to have different wrist motion patterns, however, they are not consistent enough to enable differentiation via template matching Original threshold-based algorithm: 77% true detections, 86% PPV Template matching algorithm: 46% true detection, 75% PPV Template matching is too rigid for detecting bites; there is too much variability in appearance; interestingly, it yielded the close PPV in the threshold-based algorithm suggesting it might be useful for suppressing false positives 30

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Thank you! Q uestions? 36