OBJECTIVE REMOTE ASSESSMENT OF ACTIVITIES OF DAILY LIVING:

ANALYSIS OF MEAL PREPARATION PATTERNS

TRACY BARGER, MAJD ALWAN, STEVE KELL, BEVERELY TURNER, SARAH WOOD, AMIT NAIDU

MEDICAL AUTOMATION RESEARCH CENTER

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ABSTRACT

With the rapid elder population growth, there is both need and

market for effective tools that assess an elder’s ability to maintain

independence and a healthy lifestyle. The Activities of Daily Living

[ADL] and the Instrumental Activities of Daily Living [IADL] scales

provide such measures of functionality; however, current methods of

evaluation are limited to self-report or intermittent observations. The

approach proposed here enables objective, continuous and non-intrusive

evaluation of the ADL/IADL measures in the individual’s home.

This work shows how readings from sensors installed within a

person’s residence can be analyzed and combined to provide a

probabilistic evaluation on the ADL/IADL scales. The event criteria

needed to evaluate the specific ADL/IADL questions are discussed

along with the data-mining techniques used to isolate important events

and determine relationships among sensor outputs, including model-

based clustering and association rules. The question regarding the

person’s ability to prepare meals is used as an example to illustrate the

approach on real data collected from the “Smart House” site.

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NEED AND MARKET POTENTIALS

• A large percentage of elders live alone; about fifty percent of those

are 75 and older

• Over 5.5 million people above the age of 75 are limited by chronic

conditions

• There is a need for products to monitor elders to warn of problems

• Approximately 23 million people are working caregivers for elders

• Working caregivers benefit from products allowing them to provide

necessary care more efficiently

• Independent and Assisted Living Facilities also benefit from such a

system to reduce burdens on staff

• Use of the MARC Smart House technologies to monitor the

ADL/IADL scales would provide tremendous aid to caregivers

• The system, estimated at $1000, and the estimated monthly

subscription fee of $15.00 per month are likely to be met with

acceptance by adult children and other caregivers

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TECHNOLOGY SUMMARY

• The ADL/IADL scales score basic daily activities (independently

bathe, dress, toilet, ambulate, prepare meals, eat, etc.)

• Inference of the occurrence of the ADL/IADL events

§ Analyzing which sensors would be needed to determine each

event

§ The combination of required sensor readings was mapped to each

ADL/IADL

§ Analyzing the data to examine whether an event actually

occurred

• The “mixture of distributions” concept groups sensor firings into

clusters representing different activities

• Multiple event approach provides redundancy, reducing uncertainty

and increasing confidence in the inferences made

• Association rules find sets of events that occur frequently together

and may collectively represent an activity

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TECHNOLOGY SUMMARY

• Several kitchen events were used determine meal preparation

§ Opening of cabinets and appliances (cereal, cookware, dishes,

flatware, freezer, microwave, and stove)

§ Sensor in a mat in front of the stove

§ Temperature Sensor above the stove

• Each type of event was grouped according to time of day using

mixture models to form clusters

• The number of other event groups that occurred within fifteen

minutes following an event was calculated to form a matrix

displaying the average number of times the second group occurred

within fifteen minutes of the first

• Hierarchical clustering (complete link) was then applied to the

matrix to form sets of groups .

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RESULTS AND ANALYSIS

• Three potential clusters were identified:

§ During breakfast preparation on workdays

§ During breakfast preparation on stay-at-home days

§ During dinner preparation

• Majority of days appear quite similar

• The sensor above the stove recording temperature was used to help

confirm meal preparation times

§ In all but three cases, the rising temperatures corresponded to the

estimated meal preparation times

• Motion activity in the kitchen was used to further confirm the times

of meal preparation

• The time in the kitchen compared to the estimated meal times, and the

total length of time in the kitchen intersecting with the meal times

was calculated for each day

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RESULTS AND ANALYSIS

The graph below depicts the events associated with

Breakfast on workdays.

5 10 15 20 25 30Day

22000

24000

26000

28000

Tim

e of

Day

(sec

)

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RESULTS AND ANALYSIS

The graph below shows the estimated times of meal preparation for

each day as well as times of temperature rising.

0 10 20 30Day

20000

40000

60000

80000

DinTempStDinTempEndBreakTempStBreakTempEndDinStDinEndOffBreakStOffBreakEndWorkBreakStWorkBreakEnd

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SUMMARY OF MEAL TIMES AND PATTERNS

• Breakfast analysis

§ Workday breakfast prep averaged between 6:39 and 7:04 AM

§ Involved about 18 minutes in the kitchen and 12.75 kitchen

events

• Off days average start/stop times were 9:05 and 9:58

§ Approximately 52 minutes spent in the kitchen

§ Temperature sensor rose on nine off days indicating hot

breakfast

• Dinner preparation occurred on average between 19:17 and 20:11

§ About 51 minutes spent in the kitchen

§ Stove temperature rose on nineteen of the 31 days

• Analysis showed preparation of dinner occurred during

twenty-nine of the thirty-one days; breakfast was prepared

daily

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CONCLUSIONS AND FUTURE DIRECTIONS

• Meal preparation example of ADLs demonstrated using Smart

House technologies

§ Analysis of sensor data showed relatively consistent patterns

of breakfast and dinner preparation

§ Identified patterns could be used as a baseline to detect

changes, which may indicate health problems

§ Future analysis will examine pattern change or consistency

over time

• The Smart House team is currently outfitting the test site to enable

the detection of remaining ADL/IADL activities

• We intend to implement the automatic inference engine for all

these activities

• The ADL/IADL scale is one example of daily activities evaluation

using MARC Smart House technologies. Similar approach could

be used to evaluate other measures or activities

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THE SMART HOUSE SITE