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Smart Kitchen Cabinet for Aware Home

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Abstract

This paper presents the design and development of a "Smart Kitchen Cabinet" which identifies the grocery items in the kitchen store. The Kitchen Cabinet is augmented with sensors to measure the weight of an item which is updated to a database whenever grocery items are placed or taken out for cooking. The jars in the kitchen cabinet are tagged with Radio-frequency identification (RFID) tag for identifying and tracking the location. The optimal placement of jars (containing different ingredients) attached with RFID tags and antennas are tested for maximum read performance and the experimental results are presented. The system also generates automated shopping list when an item reaches the defined threshold level, which is based on requirement and consumption pattern of family members.
Smart Kitchen Cabinet for Aware Home
Karuppiah Pal Amutha, Chidambaram Sethukkarasi, Raja Pitchiah
National Ubiquitous Computing Research Centre
Centre for Development of Advanced Computing
Chennai, India
{palamuthak, ctsethu, rpitchiah}@cdac.in
Abstract— This paper presents the design and development of
a “Smart Kitchen Cabinet” which identifies the grocery items
in the kitchen store. The Kitchen Cabinet is augmented with
sensors to measure the weight of an item which is updated to a
database whenever grocery items are placed or taken out for
cooking. The jars in the kitchen cabinet are tagged with Radio-
frequency identification (RFID) tag for identifying and
tracking the location. The optimal placement of jars
(containing different ingredients) attached with RFID tags and
antennas are tested for maximum read performance and the
experimental results are presented. The system also generates
automated shopping list when an item reaches the defined
threshold level, which is based on requirement and
consumption pattern of family members.
Keywords-Ubiquitous Computing; Pervasive Computing;
Smart artifact; Smart Kitchen Cabinet; RFID.
I. INTRODUCTION
The kitchen is a very important place of a home and
cooking is one of the day to day activities. The usual
difficulty in a kitchen during cooking is finding the items to
be out of stock.
The growing popularity of automated systems indicates
the demand of the household devices to be smart and
automated to support us in our daily activities. The kitchen
is one ideal place where automation at various levels can be
done. Daily kitchen activities include stocking kitchen
cabinet in relation to necessary dietary regiment, likes, and
needs, tastes etc.
Smart Kitchen Cabinet is an innovative appliance that
incorporates interactive services. It is an embedded system
which consists of a touch screen Liquid Crystal Display
(LCD), load sensors, Radio-frequency identification (RFID)
reader and tags to provide complete awareness about
ingredients and availability information for better kitchen
management. The features of the cabinet are: inventory
management of grocery items, automatic shopping list
preparation, item identification and tracking and balanced
diet cooking.
The contribution of the paper is as follows: 1) a location
sensing and tracking algorithm for grocery items used at
home using UHF RFID and load sensors. 2) Development of
an embedded inventory management for kitchen groceries.
3) Deployment results in actual kitchen environment. 4)
Engineering of Smart Kitchen Cabinet. 5) Optimal
placement of RFID antenna and Tags.
The remaining of the paper is organized as: Section II
discusses about the related work in this area. The system
architecture and overview are described in Section III. The
entire system testing and field reports are presented in
Section IV. The limitations of the smart kitchen cabinet are
mentioned in Section V. Conclusion and future work are
described in Section VI.
II. RELATED WORK
A prototype [1] called “Smart Kitchen” that enables
traditional meal preparation and healthy cooking by raising
awareness about facts on nutrition’s present in food
ingredients. The sensors are used to detect cooking activities
and provide feedback to the user about nutrition information
Context-aware cooking [2] is implemented using
augmented cutting board and sensor enriched knife. The
cutting board is fixed with load and acceleration sensors to
identify the type of food used. Microphone is used to
recognize the cutting sound and a camera to identify the
object.
Instrumented kitchen to automatically capture, share,
and exploit semantically annotated cooking experiences has
been realized in [4]. All necessary information should be
observed from the user's natural course of actions during the
preparation process, such that even users without any
knowledge about ontologies are able to create and benefit
from semantically represented recipes.
Bonanni, et al. [5] presented an augmented reality
interface based on a model of the user, the task and the
environment that projects information on the status of work
surfaces, storage and tools directly on the objects and spaces
where users direct their attention. This prototype uses range
finder to measure the surface temperature of food in pans on
the range. The temperature of the water in the tap is sensed
using heatsink and represented by different light colors. 4D
FridgeCam is an augmented reality interface that projects
the contents of the refrigerator directly onto the door in such
a way as to add location and time-based information.
Augmented Cabinetry is an active inventory system that
reduces the time required to locate items in the kitchen
cabinets.
The accurate location sensing of different objects in a
smart shelf using Ultra-high frequency (UHF) RFID
technology is presented in [7]. Reference tags are kept in the
shelf for location identification. The Received Signal
Strength Indicator (RSSI) value along with the tag
interference level is used for locating an object.
Experimental tests for detecting the pharmaceutical
items in a small cabinet using UHF RFID operating at
860MHz to 868MHz are demonstrated in [9]. They achieved
a maximum of 61% full detection rate by using four
antennas in a cabinet.
Our development work describes practical
implementation of embedding intelligence into existing
kitchen cabinet for load sensing, location tracking and
automatic shopping list preparation resulting in inventory
management of grocery items in the kitchen. The RFID read
performance with different type of grocery items used in
kitchen environment is also presented. The system achieves
better read performance by using two antennas and the
placement of tags as described in Section IV.
III. OVERVIEW AND ARCHITECTURE
A. System Design
This section comprises technical description of the
system. RFID tags are used for identifying the item and
load sensors are used for measuring the weight of items as
well as locating the items in the cabinet. The RFID tags are
attached to the containers and the load sensors are kept under
the partitions of the cabinet. RFID antennas are mounted
inside the cabinet. The placement of RFID antennas and tags
[3] is tested for maximum read performance. When a
container arrives/departs, the algorithm identifies the
occurred event and updates in the database. The entire
application is ported into an embedded platform based on
Intel Atom processor. The user can interact with the system
through a GUI.
Figure 1. System Overview.
The functional representation of the system is shown in
Figure 1. The sensors sense the environment and send the
data to the application. The application analyzes the data,
decide the action to be taken (IN/OUT) and update in the
database. The user can interact with the system through the
touch screen monitor. The entire system is deployed in a
kitchen of C-DAC, Guest house (Figure 2) for testing
purpose. State transition diagram of smart kitchen cabinet is
shown in Figure 3. It shows the entire activity of the system.
User has to set all necessary fields like family setting,
reminder - menu setting and mapping tagID with an item.
Once initial settings are done, the system is ready to
function. Monitoring weight state is continuously running in
the system, if any weight variation occurs, the state is
transited to the finding item state which finds and detects
the item.
Figure 2. Deployment of Smart Kitchen Cabinet in C-DAC, Guest House.
Detected item and its weight are stored in the database along
with partition. When an item is found to be below the
specified criteria level, the shopping list state is triggered.
This state generates the shopping list and sent to the user
through e-mail/SMS/printing according their request.
Figure 3. State Transition diagram of Smart Kitchen Cabinet
Load
Sensor
Antenna
B. Identifying and Tracking Algorithm
The System monitors the weight over the partitions (A
and B shown in Figure 2) continuously. The weight
measurement is done with a minimum delay between each
cycle for the load sensor to get stabilized. An event is said to
occur when the difference of the weight of current cycle (ith
cycle) and previous cycle (i-1th cycle) exceeds a specified
threshold ((Wi -Wi-1 )>Th). The occurred event may be any
one of the following 1) Arrival of a new item 2) departure of
an existing item 3) False Variation. If the difference value is
below the threshold value, then it might be of false variation
due to user’s hand pressure. It represents no action has to be
taken. If the weight output of the current cycle is greater
than the previous weight value, then an item has arrived.
The current RFID scan (Tn) is compared with the previous
RFID scan (To) to identify the arrived tag. (Tn To) gives
the common items in current and previous RFID scan.
Subtracting (Tn ∩ To) with the current RFID scan (Tn)
gives the tag ID of the new arrived item. In case of arrival
event, the database is updated with the arrived tag
information and weight value. Otherwise, an item has
departed from the cabinet. Again, the new and old RFID
scan list is compared to find the departed tag. The item is
marked as “OUT” in the database. The pseudo-code of the
algorithm is described in Figure 4. The available items are
listed out with the details like location and quantity to the
user.
//Wi-1 Weight output of i-1th cycle.
//Wi Weight output of ith cycle.
//To Represents set of existing tags (Set of tags read in
previous scan).
//Tn Represents set of tag read by current scan.
//Th Threshold.
ELSE END
performed be action to //No
pressure hand todueError //
ELSE
IF END
ELSE END
0 = Weight Item
) T (T - T = Item Departed
item existingan ofDepart //
ELSE
IF END
W- W= Weight Item
) T (T - T = Item Incoming
arrivedbeen has item NewA //
THEN ) W> IF(W
THEN Th)) W- IF((W
ono
1-ii
onn
1-ii
1-ii
Figure 4. Pseudo-code of Identifying and Tracking Algorithm.
C. Inventory Management Functions for kitchen
Inventory management includes managing the grocery
items, efficient utilization of grocery items and
communication to the user [6].
1) Monthly requirement of a family:
The amount of item (Cqty ) used per day is found out
from the weight variation and logged in the database. From
the Cqty, the required consumption (Rqty ) of an item for a
particular time period (T) is calculated using the following
formula.
T
i1 Cqty(i) Rqty (1)
2) Automatic Shopping List Preparation
Rqty *N Cq (2)
A Minimum weight can be set for every item as a
threshold limit called as critical quantity (Cq), calculated
using formula (2). It denotes the quantity to be stocked for
minimum number of days (N). For an example N=2 means
the available quantity should serve the family for at least 2
days. When the quantity of an item goes below critical
quantity; it is automatically added in the shopping list along
with the quantity (Sqty) need to buy. The Sqty is
calculated with the help of formula (3). Ts represents
shopping interval and Aqty represents available quantity.
Aqty - Ts *Rqty Sqty (3)
Once the shopping list is generated, it should be
communicated to the user either by message/mail as per
their request. The list is also displayed in the GUI.
D. User Interface
The user can interact with the system through Graphical
User Interface. The following settings can be made through
the GUI. User has to enter the details such as number of
adults in the family , number of children in the family,
number of guest (adult and children), number of days the
guest will stay, alert service such as SMS or E-Mail or both
and alert time.
User has to set the details like mapping tag ID with the
item, brand name, empty container weight, the quantity to
be stocked for minimum number of days and Shopping
interval. The reminders can also be set by the user. Recipe
preparation tips are provided. The system suggests a suitable
recipe that can be prepared from the available groceries.
IV. TESTING OF SMART KITCHEN CABINET
A. RFID Testing
1) Materials Used: A cabinet made up of metal and
wood with 4 partitions, a wooden Cabinet, UHF RFID
reader from SIRIT operating at 860MHz to 868MHz, omni
directional RFID antennas from Poynting, UHF passive
RFID Tags operating at 860MHz to 869MHz, load sensors
from loadstar, plastic containers of different height and size,
porcelain containers, glass containers, Tupperware
containers, stainless steel containers and grocery items are
used. The cost of the materials is listed in Table I.
TABLE I. MATERIAL COST
Material Quantity Cost (INR)
RFID Reader 1 109031
RFID Antenna 2 11975.97
RFID Tag 20 Approx 998.00
Load Sensor 16 318560.8
Atom Processor Kit 1 70,000
Cabinet 1 25,000
2) Test 1:
The test had been carried out in the Cabinet (100 x 39 x
70 cm) made up of both metal and wood (shown in Figure
6). The test was conducted in our UBICOM laboratory.
Scenario 1:
Initially, the test was carried out with empty containers
with tag attached on the side of the containers. Later the
containers were loaded with grocery items. The tag
detection rate of the containers was tested for different
antenna position. Initially a circularly polarized antenna was
mounted on the top of rack1 facing downwards. The racks
were divided into grids and tested the detection rate in each
grid. The read performance was around 77.7% in rack1,
20% in rack 2, 33% in rack 3 and 25% in rack4 shown in
Figure 5.
Figure 5. RFID detection result for single antenna
Figure 6. Cabinet made up of metal and wood.
a) Scenario 2:
There were several undetected tags in scenario 1 due to
tag collision and tag orientation. In order to increase the
detection rate, two antennas were used. One antenna was
placed on the right side of the rack 2 and another on the left
side of rack 3. The detection rate was 85.7% in rack 1,
100% in rack 2, 85.7% in rack 3 and 62.5% in rack 4 shown
in Figure 7.
In glass and porcelain containers, the tag was read by the
reader when it is fixed with some air gap as shown in Figure
8 a) and b). In stainless steel containers, the tag was not read
by the reader at any position. The RFID detection rate in
different containers, materials and the effect of placement of
antenna and tags are listed in table. II.
Figure 7. RFID detection result for two antennas
TABLE II. RFID READ PERFORMANCE
Container
Material Content
loaded in
Container
Tag
Placement
in
Container
Antenna
Placement in
Cabinet
Read
Performan
ce
Stainless
Steel Anything Anywhere Anywhere Very poor
Plastic sugar, salt Fix tag with
air gap (may
be in the
cap, or with
some space
in side )
In the rack
where the
antenna is
kept
Good
Plastic oil Side In the rack
where the
antenna is
not there
Poor
Plastic oil Side In the rack
where the
antenna is
kept
Good
Plastic Items like
dhal, rice,
flour etc
Side Any rack Good
Small Size
Plastic
Containers
Anything Side In the rack
where the
antenna is
Poor
not there
Small Size
Plastic
Containers
Anything Side In the rack
where the
antenna is
kept
Good
Glass Anything Side with air
gap Anywhere Good
Porcelain Anything Side Anywhere Good
Figure 8. Tag attached with air gap in (a) Glass container and (b)
Porcelain container.
b) Scenario 3:
We have found experimentally, that by adhering to the
following conditions given below, the detection rate is
improved to 100% (as shown in Figure 9).
Small containers should be kept closer to the antenna.
The sugar, salt and oil containers should be kept in the
racks where the antennas are placed.
Ensure that the containers are kept in such a way that
it should not touch each other and also the corners of
the rack.
The grocery item inside the container should be below
the level of the tag.
Figure 9. RFID Detection Result for scenario 3.
The RFID connection between the reader and the tags
were found to be not stable in the containers having sugar,
salt and oil.
3) Test 2:
The system is deployed in C-DAC guest house shown in
Figure 2. The cabinet (100 x 37 x 62 cm) is made up of
wood and has two partitions. The problems faced during the
field testing and the solutions followed are listed below:
Generation of shopping list:
When a container was taken out of the cabinet, the
tagged grocery item was added to the shopping list. This was
a logical error in the shopping list preparation module. The
error was corrected by measuring the weight of the tagged
grocery item after the container is placed again.
RFID tags Arrive and Depart event problem:
Our application was based on events generated by the
RFID reader. The containers with salt, sugar, and oil items,
generate arrive and depart events frequently; even though
the containers were kept stable in the RF field.
The tag ID and weight variation information was
maintained in separate queues. When a container was kept
inside the cabinet, the arrive event occurred. The arrived tag
ID and the change in weight were added in the RFID queue
and load sensor queue respectively. As per the queue
concept, the first element of both the queues were taken out
and updated in the database. Because of occurrence of false
arrive and depart events, the queue concept could not be
successfully used for our application.
Solutions:
The following modifications were made in our
application: If both queues had an item, then the database
would be updated otherwise it might be because of false
arrive/depart event. Therefore, the elements were removed
from both queues. Even after the modification, the problem
was not solved completely.
We used the polling method for RFID tag identification.
When there was a change in weight, a new RFID scan was
initiated and compared with the previous scan to detect the
occurrence of an event.
Weight vs Er ror Percentage
0
10
20
30
40
50
60
70
80
0.01 0.1 1 10 100
Weight (Kg)
Error Percentage (%)
Figure 10. Weight versus Error Rate measurement.
The user’s hand pressure was also measured by the
load sensor, while keeping the container inside the
cabinet. Hence a delay was introduced in weight
measurement for the load sensor to get stabilized.
RFID connection was not stable in sugar, rice, and salt
containers due to water content.
Load sensor accuracy problem:
Solutions
Tested load sensors with standard weights. Error rate was
very high for small weights and decreasing to zero
approximately for the weights above 1 Kg as shown in
Figure 10. It was observed that the error rate was better for
the maximum load capacity of the load sensor.
V. LIMITATIONS
When an item in the container is changed, the user needs
to update it. RFID connection is not stable in sugar, oil and
salt containers. The placement of tags on these items can be
determined experimentally for better read performance.
More accurate load sensors can be used to improve the
accuracy of the measurement system.
VI. CONCLUSION AND FUTURE WORK
Development of Smart Kitchen Cabinet is an effort
towards kitchen automation using ubiquitous computing
technologies. The system identifies the grocery items in the
kitchen store. The Kitchen Cabinet is embedded with
sensors to measure the weight of an item which is updated
to a database whenever grocery items are placed or taken
out for cooking. Based on the database information the
various services offered by Kitchen Cabinet such as
inventory management and automatic shopping list
preparation are useful and helping us to manage the kitchen
activities effectively. The optimal placement of RFID
antennas and tags are analyzed for the particular cabinet and
the results are presented.
The smartness of the cabinet can be further extended by
adding more functionality like Nutrition-aware cooking and
personalized cooking. Image processing techniques could
also be explored to identify the grocery item inside the
container.
ACKNOWLEDGMENT
The work is developed under National Ubiquitous
Computing Research Project, funded by Department of
Information Technology, Government of India. We would
like to thank Department of Information Technology for
providing us an opportunity to develop this smart artifact.
REFERENCES
[1] Jen-hao Chen, Peggy Pei-yu Chi, Hao-hua Chu, Cheryl Chia-Hui
Chen, and Polly Huang A Smart Kitchen for Nutrition-Aware
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[2] Matthias Kranz, Albrecht Schmidt, Alexis Maldonado, Radu Bogdan
Rusu, Michael Beetz, Benedikt Hornler, and Gerhard Rigoll,
“Context aware kitchen utilities,” ACM ,Proceedings of the 1st
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[3] Rajesh Kumar Kushwaha and Ramji Gupta, “Optimization of
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[4] Michael Schneider, “The Semantic Cookbook: Sharing Cooking
Experience in the Smart Kitchen,” 3rd IET International Conference
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[6] http://www.invatol.com/
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Optimization of Antenna and Tag Position for RFID Based People Management System
  • Rajesh Kumar Kushwaha
  • Ramji Gupta
Rajesh Kumar Kushwaha and Ramji Gupta, "Optimization of Antenna and Tag Position for RFID Based People Management System," Proceedings of ASCNT2010, pp. 22-30.