Enabling Calorie-Aware Cooking in a Smart Kitchen

Transcription

1 Enabling Calorie-Aware Cooking in a Smart Kitchen Pei-Yu (Peggy) Chi UbiComp Lab Graduate Institute of Networking and Multimedia National Taiwan University 2008/06/27 oral exam of Master thesis

2 cooking in a home kitchen Europe: 52%, the US: 44% favor home-cooked meals [Datamonitor Co.] Changing Cooking Behaviors & Attitudes: Beyond Convenience, Commercial Report (2006) 2

3 cooking in a home kitchen Do people cook healthily? Europe: 52%, the US: 44% favor home-cooked meals 3

4 actors in healthy cooking amounts of fat amounts of cholesterol nutrition balance Do people cook healthily? the size of home-cooked meals is increasing over intake NO. time of cooking (loss of nutrition) proper calorie amounts of salt cooking style (fry, bake, steam ) [Nielson and Popkin] Patterns and Trends in ood Portion Sizes, Journal of the American Medical Association (2003) 4

5 Healthy cooking vs. chronic diseases amounts of fat amounts of cholesterol nutrition balance diabetes obesity myocardial infarction hypertension cerebral embolism time of cooking (loss of nutrition) proper calorie amounts of salt cooking style (fry, bake, steam ) 5

6 Healthy cooking vs. chronic diseases amounts of fat amounts of cholesterol nutrition balance diabetes obesity myocardial infarction? Average daily needs: (for 30-yr-old)? emale 2000 kcal Male 2500 kcal hypertension cerebral embolism time of cooking (loss of nutrition) proper calorie amounts of salt cooking style (fry, bake, steam ) 6

7 Calorie calculation in home cooking each food ingredient beef item beef weight unit calorie calorie 140g 390 kcal 546 measure its weight 140g oil 10g 900 kcal (ave) SUM 930 look up calorie table 390 kcal per 100g calculate proportion 390 * (140/100) = 546 kcal sum up the overall calorie beef 546kcal + oil 90kcal + =930kcal adjust the amounts decrease beef 7

8 Calculate by servings Daily food pyramid 8

9 Unaffordable efforts Calculate by servings item beef oil Calorie calculation weight unit calorie 140g 390 kcal 10g 900 kcal SUM calorie cause family cooks to regard their lack of skills and self-efficacy to healthy cooking give up to learn and put into practice [Bandura] Social oundations for Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs (1986) 9

10 Calorie-aware kitchen for experienced family cooks ubiquitous computing real-time calorie awareness overcome difficulties persuade to cook within proper calorie User performs an action Awareness display Calorie-aware Kitchen pour oil oil 90kcal 10

11 Contribution Design a smart kitchen that can sense food ingredients during the cooking process Apply to applications that can promote healthy cooking for average family cooks Nutrition-fact-aware Calorie-aware Nutrition-balance-aware 11

12 Outline Contextual Inquiry Design Consideration Prototype Design Awareness Display Calorie Tracker User Study uture Work 12

13 Contextual inquiry 4 experienced family cooks (aged 28,30,58,65) 4 days in their home kitchen Ask them to cook a dinner as usual indings: 1. Desire to cook healthily, but no time to learn 2. Main activities occur over the kitchen surface. They add ingredients based on experience or preference, but unsure about healthiness 3. ocus solely on cooking 4. No standard procedure to instruct them 13

14 Design consideration Offer just-in-time calorie information No effort to weight ingredients indings of contextual inquiry: (1) Reduce efforts (3) ocus on cooking Calorie recommendation (1) Reduce efforts Simple information presentation grasp with quick glances (3) ocus on cooking Help decision making (to adjust calorie use or not) but not enforce cooking habits (2) Experienced (4) No standard 14

15 Prototype design Calorie Tracker Awareness Display camera sensing weighing sensing 15

16 Calorie-aware display stove counter calorie bar spatial mapping of kitchen surface recommend (this meal) current use 16

17 (video) 17

18 Prototype design Calorie Tracker Awareness Display camera sensing weighing sensing 18

19 Calorie tracker Awareness Display Container1, bacon, 130kcal Calorie-change 50g, (x,y) Weight-change Weight Change Detector Weighing-sensing Surface to detect ingredient change Camera Sensing weight noise filtering 19

20 Weighing sensors To detect food ingredient requency: 8 samples/sec Resolution: 1g weight time 20

21 21 Positioning A. Schmidt et al., Context Acquisition Based on Load Sensing, UbiComp 2002 ) 0 ( ) 0 ( ) 0 ( ) 0 ( ) 0 ( ) 0 ( max max x x x x X X y y x x + = + = = = (1) (2) (3) (4)

22 Camera sensor A basic webcam (320x240) To filter weight noise and define region Weight noises E.g. cutting, stirring foods ilter by video analysis technique using HSV color histogram comparison weight time 22

23 Video analysis incorrect weight change event taken image time analysis 23

24 Video analysis real weight change event taken image time analysis 24

25 Calorie tracker Awareness Display Container1, bacon, 130kcal Calorie-change Container1, bacon, 50g Ingredient Inference Engine 50g, (x,y) Ingredient-change Weight-change commonsense ood Labeling Weight Change Detector Weighing-sensing Surface to detect ingredient change Camera Sensing weight noise filtering 25

26 Inference algorithm (1/3) put on a cutting board weight change weight (g) position (x,y) on the surface no container on (x,y)? in/decrease increase new container 26

27 Inference algorithm (2/3) put on some chicken weight change weight (g) position (x,y) on the surface no in/decrease increase container on (x,y)? increase yes in/decrease new container no match (new ingredient) weight matching a match ood labeling transference food increase 27

28 Inference algorithm (3/3) remove chicken slices weight change weight (g) position (x,y) on the surface no container on (x,y)? yes in/decrease increase new container no match (new ingredient) increase weight matching a match in/decrease decrease add to candidate list ood labeling transference food decrease food increase 28

29 Calorie tracker Awareness Display Container1, bacon, 130kcal Calorie-change Container1, bacon, 50g Calorie Calculator Ingredient Inference Engine 50g, (x,y) Ingredient-change Weight-change Nutritional Database commonsense ood Labeling Weight Change Detector Weighing-sensing Surface to detect ingredient change Camera Sensing weight noise filtering Accuracy: 92% of calorie amounts can be determined 29

30 Outline Contextual Inquiry Design Consideration Prototype Design Awareness Display Calorie Tracker User Study uture Work 30

31 User study Participants 3 family cooks over 5 years of cooking experiences Cook in our lab kitchen Design a fixed dinner menu, as they were cooking for their families 31

32 User study (cont d) Procedures Phase 1: pretest [2 cooking sessions] No calorie feedback Collect their usual calorie use amiliarize with our kitchen Phase 2: testing [3 cooking sessions] With calorie feedback Observe the effects Day 1 Day 2 Day 3 Day 4 Day 5 Pretest with no feedback Testing with calorie feedback 32

33 Environment awareness display camcorder sensing interface stove counter User sink fridge acilitator2 acilitator1 to monitor the sensing process to introduce and assist users 33

34 Results: calorie used Calorie over or under the recommended value Pretest with no feedback Test with calorie feedback Calorie difference (kcal) tried to balance between taste and health Cooking session P1 P2 P3 34

35 P1 P2 P3 measure spaghetti to certain level take out condensed soup adjust the tofu package 35

36 Results: how users reduced calorie Top 3 ingredients with largest amounts of calorie decrease Top1 Top2 Top3 Ingredient (kcal/100g) oil (883) spaghetti (363) sauce (632) P1 Calorie decrease 61.2% 16.4% 6.9% Ingredient (kcal/100g) condensed soup (328) bacon (372) butter (730) P2 Calorie decrease 75.5% 10.7% 6.2% Ingredient (kcal/100g) meat sauce (256) tofu (79) oil (883) Major calorie reductions are from high-calorie ingredients P3 Calorie decrease 34.8% 26.0% 19.3% 36

37 Qualitative results Users learned calorie information about food ingredients This kind of instant feedback is effective to remind me of what I already know about using the condensed soup and some high-calorie ingredients such as UHT cream I should really be aware of using less of an (high-calorie) ingredient and not all in the whole package May affect their shopping decision I would also consider the amounts of ingredients in my shopping. or example, now I have ideas about buying the appropriate size of salmon, and I will be careful Awareness may not easily lead to habitual change It s not easy to change cooking habits It may be better to learn when we started to learn cooking 37

38 uture work (1/2) Target inexperienced cook May be more effective as a concept building for beginning learners Redesign interaction Incorporate users as participants of sensing game user adjust the amounts until he confirms ask user to put on certain amount of ingredient kitchen sensing expectation show results of his measurement [gain points!] Nintendo Co., Cooking Mama,

39 uture work (2/2) Provide calorie logs and trends to users Long-term and large-scale user study Consider social behaviors of meal consumption Cooking tradeoff: tasty and healthy Kitchen: cannot track meal consumption Our previous work: diet-aware dining table 39

40 Calorie-aware kitchen [ home cooking ] ubiquitous computing Calorie tracker weight sensing camera sensing real-time calorie awareness overcome difficulties persuade to healthy cooking User performs an action Awareness display Calorie-aware Kitchen pour oil oil 90kcal 40

41 Thank you Any question? Enabling Calorie-Aware Cooking in a Smart Kitchen Pei-Yu (Peggy) Chi peggychi@csie.org UbiComp Lab Graduate Institute of Networking and Multimedia National Taiwan University 41