Dining Scenario #3: Afternoon Tea - Two Users - Random Activities

The third dining scenario involves the afternoon tea setting and two users, but with random dietary activities. Random activities mean that the table participants are more or less free to perform any impromptu dietary behaviors for 913 seconds over the table within the bound of our assumptions described in Section 2.4. The results are shown in Table 5.1.

Based on our measurements, the recognition accuracy is 79.49%. Table 5.2 in page 31 shows the recognition accuracy for each of the two dietary behaviors. The eat events have

5.4. DINING SCENARIO #3: AFTERNOON TEA - TWO USERS - RANDOM

AC-TIVITIES 31

better recognition accuracy than the transfer events, because they can be directly deduced by personal container’s Weight-Change event.

To determine the causes for the misses in activity recognition, we videotaped the af-ternoon tea scenario. By analyzing the video in combination with the system event logs, we derive four main leading causes shown in Table 6. They are described as follows.

Table 5.2: The accuracy of activity recognition under afternoon tea scenario #3 Dietary Behavior # of Actual Events Recognition Accuracy

Transfer event 41 70.73%

Eat event 37 89.19%

Table 5.3: Causes of miss recognition in afternoon tea scenario #3. There are 78 activities analyzed from the video log. The number of misses counts both false positives and false negatives.

Causes of misses # of misses of

transfer events

# of misses of eat

events Total

(c1) Event interference within the

weigh-ing cell’s weight stabilization time 6 2 8

(c2) Weight matching threshold 2 0 2

(c3) Slow RFID sample rate 3 0 3

(c4) Noise from weighing cell 1 2 3

Total of misses 12 4 16

(c1) Event interference within the weighing cell’s weight stabilization time: for activities such as putting down an object on the table, it takes about 1.5 seconds for our weighing sensor to output a stable weight value. If two events occur on the same cell and their time interval is less than the weighing cell’s stabilization time, our system cannot differentiate these two Weight-Change events. Instead, our system will incorrectly recognize them as a single Weight-Change event. Consider the case

32 CHAPTER 5. EXPERIMENTAL SET-UP AND RESULTS

where user A puts down the tea pot at cell X while user B immediately (within 1.5 seconds) grabs a sugar cube from the sugar jar on the same cell X. There are actually two Weight-Change events of amount (Δw1) and of amount (−Δw2). However, due to two events interfering with each other within the weight stabilization time, our system can only detect one erroneous Weight-Change event of amount|Δw1−Δw2|.

(c2) Weight matching threshold: the current threshold value is set to be four grams to filter out noises in the weight readings from weighing cells. However, in some cases, such as transferring one cube of sugar, this threshold value may still be too large. As a result, it may lead to false weight matching involving unrelated weight transfers of similar amounts. Consider the example that user A is removing a cube of sugar from the sugar jar. This results in a Weight-Change of approximately four grams in the sugar jar. At the same time, user B is transferring food weighted approximately eight grams. Eight grams is twice as much as four grams, but they are still within the weight matching threshold. Therefore, this leads to false weight matching. To address this issue, we may change the weight matching threshold to be a percentage of transferred weight rather than an absolute value of four grams.

(c3) Slow RFID sample rate: we have found cases when a user picks up a cup and quickly puts it down. This interval is less than the amount of time the RFID reader performs one round of reading over nine antennas. Therefore, a Weight-Change event is generated without any corresponding RFID-Presence event. This leads to false inference.

(c4) Noises from weighing cells: although we ask users not to touch the table, some still do during the experiment out of personal habits. This leads to erroneous generation of Weight-Change events.

5.5. DINING SCENARIO #4: CHINESE-STYLE DINNER - THREE USERS -

RAN-DOM ACTIVITIES 33

5.5 Dining Scenario #4: Chinese-style dinner - Three users - Random activities

The fourth dining scenario involves the Chinese-style dinner setting, three users, and ran-dom dietary activities for 1811 seconds. Similar to the third scenario, three participants perform impromptu dietary behaviors within the bound of our assumptions described in Section 2.4. The results are shown in Table 4. Based on our measurements, the recogni-tion accuracy is 83.33%. Note that increasing number of table participants only slightly increases the activity rate. The reason is that as the number of table participants increases, out of politeness they try to go the dishes less frequently to avoid in-the-air conflicts over the dishes.

Table 5.4 in page 34 shows the recognition accuracy (for the transfer and eat events) and weight accuracy for each of dietary behaviors. The weight accuracy is computed as the ratio between the measured and the actual weight transferred or consumed during dietary behaviors. Both the recognition and weight accuracy for the food transferring behaviors are between 80 85%, except for dish A, which is fluid-covered food. The reason for lower accuracy on transferring fluid-covered food is that juices from the fluid-covered food can easily drip from the chopsticks during food transfer (from a very lousy chopstick user). The juice dripping leads to erroneous generation of Weight-Change events with both positive and negative values, causing mismatches in the weight matching algorithm.

Furthermore, the weight accuracy of transferring dish A is low at 68.42%, because these transfer recognition misses can accumulate to a large weight sum. Similar to the afternoon tea scenario, the eat events have better recognition accuracy because they can be directly deduced from the personal container’s Weight-Change event.

To determine the causes for the misses in activity recognition, we videotaped the Chinese-style dinner scenario and analyzed the video in combination with the system

34 CHAPTER 5. EXPERIMENTAL SET-UP AND RESULTS

Table 5.4: The accuracy of the Chinese-style dinner scenario #4

Dietary Behavior # of times Recognition Accuracy Weight Accuracy

Transfer dish A events 19 73.68% 68.42%

Transfer dish B events 29 79.31% 78.75%

Transfer dish C events 23 82.61% 79.19%

Transfer rice events 12 83.33% 81.88%

Transfer soup events 19 84.21% 80.16%

Eat events 60 88.33% 91.23%

Overall 162 83.33% 82.62%

event logs. We derive five main leading causes shown in Table 8. They are described as follows.

Table 5.5: Causes of miss recognition in Chinese-style dinner scenario #4. There are 162 activities analyzed from the video log. The number of misses counts both false positives and false negatives.

Causes of misses # of misses of

transfer events

# of misses of eat

events Total

(c1) Segmented weight-change events 5 0 5

(c2) Eating before transferring food on

personal containers 5 5 10

(c3) Weight matching ambiguity 7 0 7

(c4) Noises from weighing cells 3 2 5

(c5) Slow RFID sample rate 3 0 3

Total of misses 23 7 30

(c1) Segmented Weight-Change events: during a lousy food transfer where a user drops a part of food back into the container or on the table, the weight matching algorithm fails because of the difference between weight change values of the container and the personal plate. In addition, such category also includes a case (which didn’t happen in our experiment) that a user holding a personal bowl in the air and scoop