Health Monitoring and Assistance to Support Aging in Place
Diane J. Cook
(The University of Texas at Arlington, USA
Abstract: To many people, home is a sanctuary. For those people
who need special medical care, they may need to be pulled out of their
home to meet their medical needs. As the population ages, the percentage
of people in this group is increasing and the effects are expensive as
well as unsatisfying. We hypothesize that many people with disabilities
can lead independent lives in their own homes with the aid of at-home automated
assistance and health monitoring. In order to accomplish this, robust methods
must be developed to collect relevant data and process it dynamically and
adaptively to detect and/or predict threatening long-term trends or immediate
crises. The main objective of this paper is to investigate techniques for
using agent-based smart home technologies to provide this at-home health
monitoring and assistance. To this end, we have developed novel inhabitant
modeling and automation algorithms that provide remote health monitoring
for caregivers. Specifically, we address the following technological challenges:
1) identifying lifestyle trends, 2) detecting anomalies in current data,
and 3) designing a reminder assistance system. Our solution approaches
are being tested in simulation and with volunteers at the UTA's MavHome
site, an agent-based smart home project.
Key Words: multiagent systems, artificial intelligence, smart
1 Introduction and Motivation
Since the beginning, people have lived in places that provide shelter
and basic comfort and support, but as society and technology advance there
is a growing interest in improving the intelligence of the environments
in which we live and work. The MavHome (Managing an adaptive
versatile Home) project is focused on providing such environments
[Das et al., 2002]. We take the view-point of
treating an environment as an intelligent agent, which perceives the state
of the environment using sensors and acts upon the environment using device
controllers in a way that can optimize a number of different goals including
maximizing comfort of the inhabitants, minimizing the consumption of resources,
and maintaining safety of the environment and its inhabitants. In this
paper we discuss methods by which we can adapt a smart home environment
such as MavHome to perform health monitoring and assistance for persons
with disabilities and for aging adults.
As Lanspery and Hyde [Lanspery et al., 1997] state,
''For most of us, the word `home' evokes powerful emotions [and is] a refuge''.
They note that older adults and people with disabilities want to remain
in their homes even when their conditions worsen and the home cannot sustain
In a national survey, researchers found that 71% of the respondents
felt strongly that they wanted to remain in their current residence as
long as possible, and another 12% were somewhat likely to remain there
[AARP, 2000]. Nearly 1/4 of the respondents expected
that they or a member of their household would have problems getting around
their house in the next five years. Of these respondents, 86% stated that
they had made at least one modification to their home to make it easier
to live there, and nearly 70% believe that the modifications will allow
them to live in the current homes longer than would have otherwise been
possible. A separate study supported these results and found that the most
common modifications were an easy-to-use climate control system and a personal
Zola [Zola, 1997] maintains that the problems of
aging and disability are converging. Improvements in medical care are resulting
in increased survival into old age, thus problems of mobility, vision,
hearing, and cognitive impairments will increase [Pynoos,
2002, Parr and Russell, 1997]. As the baby boomers
enter old age, this trend will be magnified. By 2040, 23% will fall into
the 65+ category [Lanspery et al., 1997]. An AARP
report [AARP, 2000, AARP, 2003]
strongly encourages increased funding for home modifications that can keep
older adults with disabilities independent in their own homes.
While use of technology can be expensive, it may be more cost effective
than the alternative [Grayons, 1997]. Nursing home
care is generally paid either out-of-pocket or by Medicaid. Typical nursing
home costs are about $40,000 a year, and the $197 billion of free care
offered by family members comes at the sacrifice of independence and job
opportunities by the family caregivers.
In this paper, our goal is to assist the elderly and individuals with
disabilities by providing home capabilities that will monitor health trends
and assist in the inhabitant's day to day activities in their own homes.
The result will save money for the individuals, their families, and the
2 Overview of the MavHome Smart Home
We define an intelligent environment as one that is able to acquire
and apply knowledge about its inhabitants and their surroundings in order
to adapt to the inhabitants and meet the goals of comfort and efficiency
[Cook and Das, 2004]. These capabilities rely
upon effective prediction, decision making, robotics, wireless and sensor
networking, mobile computing, databases, and multimedia technologies. With
these capabilities, the home can adaptively control many aspects of the
environment such as climate, water, lighting, maintenance, and multi-media
entertainment. Intelligent automation of these activities can reduce the
amount of interaction required by inhabitants, reduce energy consumption
and other potential wastages, and provide a mechanism for ensuring the
health and safety of the environment occupants [Das
and Cook, 2004b].
As the need for automating these personal environments grows, so does
the number of researchers investigating this topic. Some design interactive
conference rooms, offices, kiosks, and furniture with seamless integration
between heterogeneous devices and multiple user applications in order to
facilitate collaborate work environments [AIRE Group,
2004, Fox et al., 2000, Romn
et al., 2002, Streitz et al., 1999]. Abowd and
Mynatt's work [Abowd and Mynatt, 2005] focuses on
ease of interaction with a smart space, and work such as the Gator Tech
Smart House [Helal et al., 2005] focuses on application
of smart environments to elder care.
Mozer's Adaptive Home [Mozer, 2005] uses neural
network and reinforcement learning to control lighting, HVAC, and water
temperature to reduce operating cost. In contrast, the approach taken by
the iDorm project [Hagras et al., 2004] is to use
a fuzzy expert system to learn rules that replicate inhabitant interactions
with devices, but will not find an alternative control strategy that improves
upon manual control for considerations such as energy expenditure.
These projects have laid a foundation for our work. However, unlike
related projects, we learn a decision policy to control an environment
in a way that optimizes a variety of possible criteria, including minimizing
manual interactions, improving operating efficiency, and ensuring inhabitant
health and safety. We also ensure that our software need not be redesigned
as new devices are registered, new spaces are tested, or new inhabitants
move into the environment. To accomplish this goal, our intelligent environment
must harness the features of multiple heterogeneous learning algorithms
in order to identify repeatable behaviors, predict inhabitant activity,
and learn a control strategy for a large, complex environment.
The MavHome architecture shown in Figure 1 consists
of cooperating layers [Cook and Das, 2004, Das
and Cook, 2005]. Perception is a bottom-up process. Sensors monitor
the environment using physical components (e.g., sensors) and make information
available through the interface layers. The database stores this information
while other information components process the raw information into more
useful knowledge (e.g., patterns, predictions). New information is presented
to the decision making applications (top layer) upon request or by prior
arrangement. Action execution flows top-down. The decision action is communicated
to the services layer which records the action and communicates it to the
physical components. The physical layer performs the action using powerline
control, and other automated hardware, thus changing the state of the world
and triggering a new perception.
All of the MavHome components are implemented and are being tested in
two physical environments, the MavLab workplace environment and an on-campus
Figure 1: MavHome architecture (left) and MavPad sensor layout
Powerline control automates all lights and appliances, as well as HVAC,
fans, and miniblinds. Perception of light, humidity, temperature, smoke,
gas, motion, and switch settings is performed through a sensor network
developed in-house. Inhabitant localization is performed using passive
infrared sensors yielding a detection rate of 95% accuracy [Youngblood
et al., 2005a].
Communication between high-level components is performed using CORBA,
and each component registers its presence using zero configuration (ZeroConf)
technologies. Implemented services include a PostgreSQL database that stores
sensor readings, prediction components, data mining components, and logical
proxy aggregators. Resource utilization services monitor current utility
consumption rates and provide usage estimates and consumption queries.
MavHome is designed to optimize a number of alternative functions, but
for this evaluation we focus on minimization of manual interactions with
devices. The MavHome components are fully implemented and are automating
the environments shown in Figure 2 [Youngblood
et al., 2005b]. The MavLab environment contains work areas, cubicles,
a break area, a lounge, and a conference room. MavLab is automated using
54 X-10 controllers and the current state is determined using light, temperature,
humidity, motion, and door/seat status sensors.
Figure 2: The MavLab (left) and MavPad (right) environments
The MavPad is an on-campus apartment hosting a full-time stu dent
occupant. MavPad is automated using 25 controllers and provides sensing
for light, temperature, humidity, leak detection, vent position, smoke
detection, CO detection, motion, and door/window/seat status sensors. Figure
1 shows the MavPad sensor layout.
3 Core Technologies
To automate our smart environment, we collect observations of manual
inhabitant activities and interactions with the environment. We then mine
sequential patterns from this data using a sequence mining algorithm. Next,
we predict the inhabitant's upcoming actions using observed historical
data. Finally, a hierarchical Markov model is created using low-level state
information and high-level sequential patterns, and is used to learn an
action policy for the environment. Figure 3 shows how
these components work together to improve the overall performance of the
smart environment. Here we describe the learning algorithms that play a
role in this approach.
3.1 Mining Sequential Patterns Using ED
In order to minimize resource usage, maximize comfort, and adapt to
inhabitants, we rely upon machine learning techniques for automated discovery,
prediction, and decision making. A smart home inhabitant typically interacts
with various devices as part of his routine activities. These interactions
may be considered as a sequence of events, with some inherent pattern of
recurrence. Agrawal and Srikant [Agrawal and Srikant,
1995] pioneered work in mining sequential patterns from time-ordered
transactions, and our work is loosely modeled on this approach.
Figure 3: Integration of AI techniques into MavHome architecture.
Typically, each inhabitant-home interaction event is characterized as
a triple consisting of the device manipulated, the resulting change that
occurred in that device, and the time of interaction. We move a window
in a single pass through the history of events or inhabitant actions, looking
for episodes (sequences) within the window that merit attention. Candidate
episodes are collected within the window together with frequency information
for each candidate. Candidate episodes are evaluated and the episodes with
values above a minimum acceptable compression amount are reported. The
window size can be selected automatically using the size that achieves
the best compression performance over a sample of the input data.
When evaluating candidate episodes, the Episode Discovery (ED) algorithm
[Heierman and Cook, 2003] looks for patterns that
minimize the description length of the input stream, O, using the
Minimum Description Length (MDL) principle [Rissanen,
1989]. The MDL principle targets patterns that can be used to minimize
the description length of a database by replacing each instance of the
pattern with a pointer to the pattern definition.
Our MDL-based evaluation measure thus identifies patterns that balance
frequency and length. Periodicity (daily, every other day, weekly occurrence)
of episodes is detected using autocorrelation and included in the episode
description. If the instances of a pattern are highly periodic (occur at
predictable intervals), the exact timings do not need to be encoded (just
the pattern defini tion with periodicity information) and the resulting
pattern yields even greater compression. Although event sequences with
minor deviations from the pattern definition can be included as pattern
instances, the deviations need to be encoded and the result thus increases
the overall description length. ED reports the patterns and encodings that
yield the greatest MDL value.
Deviations from the pattern definition in terms of missing events, extra
events, or changes in the regularity of the occurrence add to the description
length because extra bits must be used to encode the change, thus lowering
the value of the pattern. The larger the potential amount of description
length compression a pattern provides, the more representative the pattern
is of the history as a whole, and thus the potential impact that results
from automating the pattern is greater.
In this way, ED identifies patterns of events that can be used to better
understand the nature of inhabitant activity in the environment. Once the
data is compressed using discovered results, ED can be run again to find
an abstraction hierarchy of patterns within the event data. As the following
sections show, the results can also be used to enhance performance of predictors
and decision makers that automate the environment.
3.2 Predicting Activities Using ALZ
To predict inhabitant activities, we borrow ideas from text compression,
in this case the LZ78 compression algorithm [Ziv and Lempel,
1978]. By predicting inhabitant actions, the home can automate or improve
upon anticipated events that inhabitants would normally perform in the
home. Well-investigated text compression methods have established that
good compression algorithms also make good predictors. According to information
theory, a predictor with an order (size of history used) that grows at
a rate approximating the entropy rate of the source is an optimal predictor.
Other approaches to prediction or inferring activities often use a fixed
context size to build the model or focus on one attribute such as motion
[Cielniak et al., 2003, Philipose
et al., 2004].
LZ78 incrementally processes an input string of characters, which in
our case is a string representing the history of device interactions, and
stores them in a trie. The algorithm parses the string x1,
x2, . . . , x i into substrings
w1, w2, wc(i) such
that for all j > 0, the prefix of the substring wj
is equal to some wi for 1 < i < j.
Thus when parsing the sequence of symbols aaababbbbbaabccddcbaaaa,
the substring a is created, followed by aa, b, ab,
bb, bba, and so forth.
Our Active LeZi (ALZ) algorithm enhances the LZ78 algorithm by recapturing
information lost across phrase boundaries. Frequency of symbols is stored
along with phrase information in a trie, and information from multiple
context sizes are combined to provide the probability for each potential
symbol, or inhabitant action, as being the next one to occur. In effect,
ALZ gradually changes the order of the corresponding model that is used
to predict the next symbol in the sequence. As a result, we gain a better
convergence rate to optimal predictability as well as achieve greater predictive
accuracy. Figure 4 shows the trie formed by the Active-LeZi
parsing of the input sequence aaababbbbbaabccddcbaaaa.
To perform prediction, ALZ calculates the probability of each symbol
(inhabitant action) occurring in the parsed sequence, and predicts the
action with the highest probability.
Figure 4: Trie formed by ALZ parsing the highest probability
To achieve optimal predictability, we use a mixture of all possible
higher-order models (phrase sizes) when determining the probability estimate.
Specifically, we incorporate the Prediction by Partial Match strategy of
exclusion [Bell et al., 1990] to gather information
from all available context sizes in assigning the next symbol its probability
We initially evaluated the ability of ALZ to perform inhabitant action
prediction on synthetic data based on six embedded tasks with 20% noise.
In this case the predictive accuracy converges to 86%. Real data collected
based on six students in the MavLab for one month was much more chaotic,
and on this data ALZ reached a predictive performance of 30% (although
it outperformed other methods). However, when we combine ALZ and ED by
only performing predictions when the current activity is part of a sequential
pattern identified by ED, ALZ performance increases by 14% [Gopalratnam
and Cook, 2004, Gopalratnam and Cook, 2005].
3.3 Decision Making Using ProPHeT
In our final learning step, we employ reinforcement learning to generate
an automation strategy for the intelligent environment. To apply reinforcement
learning, the underlying system (i.e., the house and its inhabitants) could
be modeled as a Markov Decision Process (MDP). This can be described by
a four-tuple < S, A, Pr, R >, where S
is a set of system states, A is the set of available actions, and
R : S -> R is the reward that the learning agent
receives for being in a given state. The behavior of the MDP is described
by the transition function, Pr : S × A ×
S -> [0, 1], representing the probability with which action at
executed in state st leads to state st+1
With the increasing complexity of tasks being addressed, recent work
in decision making under uncertainty has popularized the use of Partially
Observable Markov Decision Processes (POMDPs). Recently, there have been
many published hierarchical extensions that allow for the partitioning
of large domains into a tree of manageable POMDPs [Pineau
et al., 2001, Theocharous et al., 2001].
Figure 5: Hierarchical model constructed from static (left)
and dynamic (right) smart home data
Research has shown that strategies for new tasks can be learned faster
if policies for subtasks are already available [Precup
and Sutton, 1997]. Although a Hierarchical POMDP (HPOMDP) is appropriate
for an intelligent environment domain, current approaches generally require
a priori construction of the hierarchical model. Unlike other approaches
to creating a hierarchical model, our decision learner, ProPHeT, actually
automates model creation by using the ED-mined sequences to represent the
nodes in the higher levels of the model hierarchy.
The lowest-level nodes in our model represent a single event observed
by ED. Next, ED is run multiple iterations on this data until no more patterns
can be identified, and the corresponding abstract patterns comprise the
higher-level nodes in the Markov model. The higher-level task nodes
point to the first event node for each permutation of the sequence that
is found in the environment history. Vertical transition values are labeled
with the fraction of occurrences for the corresponding pattern permutation,
and horizontal transitions are seeded using the relative frequency of transitions
from one event to the next in the observed history. As a result, the n-tier
hierarchical model is thus learned from collected data. An example hierarchical
model constructed from MavHome test data is shown on the left in Figure
Given the current event state and recent history, ED supplies membership
probabilities of the state in each of the identified patterns. Using this
information along with the ALZ-predicted next action, ProPHeT maintains
a belief state and selects the highest-utility action.
To learn an automation strategy, the agent explores the effects of its
decisions over time and uses this experience within a temporal-difference
reinforcement learning framework [Sutton and Barto, 1998]
to form control policies which optimize the expected future reward. The
current version of MavHome receives negative reinforcement (observes a
negative reward) when the inhabitant immediately reverses an automation
decision (e.g., turns the light back off) or an automation decision contradicts
ARBITER-supplied safety and comfort constraints.
Before an action is executed it is checked against the policies in the
policy engine, ARBITER. These policies contain designed safety
and security knowledge and inhabitant standing rules. Through the policy
engine the system is prevented from engaging in erroneous actions that
may perform actions such as turning the heater to 120 o F or from violating
the inhabitant's stated wishes (e.g., a standing rule to never turn off
the inhabitant's night light).
4 Initial Case Study
As an illustration of the above techniques, we have evaluated a week
in an inhabitant's life with the goal of reducing the manual interactions
in the MavLab. The data was generated from a virtual inhabitant based on
captured data from the MavLab and was restricted to just motion and lighting
interactions which account for an average of 1400 events per day.
ALZ processed the data and converged to 99.99% accuracy after 10 iterations
through the training data. When automation decisions were made using ALZ
alone, interactions were reduced by 9.7% on average. Next, ED processed
the data and found three episodes to use as abstract nodes in the HPOMDP.
Living room patterns consisted of lab entry and exit patterns with light
interactions, and the office also reflected entry and exit patterns. The
other patterns occurred over the remaining 8 areas and usually involved
light interactions at desks and some equipment upkeep activity patterns.
The hierarchical Markov model with no abstract nodes reduced interactions
by 38.3%, and the combined-learning system (ProPHeT bootstrapped using
ED and ALZ) was able to reduce interactions by 76%, as shown in Figure
Experimentation in the MavPad using real inhabitant data has yielded
similar results. In this case, ALZ alone reduced interactions from 18 to
17 events, the HPOMDP with no abstract nodes reduced interactions by 33.3%
to 12 events, while the bootstrapped HPOMDP reduced interactions by 72.2%
to 5 events. These results are graphed in Figure 6 (right).
Figure 6: Interaction reduction
5 Using a Smart Home to Assist Elderly and Disabled
The data mining, prediction, and multiagent technologies available in
MavHome can be employed to provide health care assistance in living environments.
Specifically, models can be constructed of inhabitant activities and used
to learn activity trends, detect anomalies, intelligently predict possible
problems and make health care decisions, and provide automation assistance
for inhabitants with special needs.
A variety of approaches have been investigated in recent years to automate
caregiver services. Many of the efforts offer supporting technologies in
specialized areas, such as using computer vision techniques to track inhabitants
through the environment and specialized sensors to detect falls or other
crises. Some special-purpose prediction algorithms have been implemented
using factors such as measurement of stand-sit and sit-stand transitions
and medical history [Cameron et al., 1997, Najafi
et al., 2002, Najafi et al., 2003], but are limited
in terms of what they predict and how they use the results. Remote monitoring
systems have been designed with the common motivation that learning and
predicting inhabitant activities is key for health monitoring, but very
little work has combined the remote monitoring capabilities with prediction
for the purpose of health monitoring. Some work has also progressed toward
using typical behavior patterns to provide reminders, particularly useful
for the elderly and patients suffering from various types of dementia [Kautz
et al., 2002, Pollack et al., 2003].
Our smart environment can identify patterns indicating or predicting
a change in health status and can provide inhabitants with needed automation
assistance. Collected data includes movement patterns of the individual,
periodic vital signs (blood pressure, pulse, body temperature), water and
device usage, use of food items in the kitchen, exercise regimen, medicine
intake (prescribed and actual), and sleep patterns [Das
and Cook, 2004a, Das and Cook, 2004b]. Given
this data, models can be constructed of inhabitant activities and use to
learn lifestyle trends, detect anomalies, and provide reminder and automation
5.1 Capability 1: Identify lifestyle trends
Our ED algorithm is designed to process data as it arrives. Because
of this feature, trends in the data including increasing / decreasing pattern
frequency, introduction of patterns, and change in pattern details can
be automatically detected [Heierman, 2004]. When changing
patterns include health-specific events (vital signs, medication intake,
or events targeted by the caregiver), a report will be given to the inhabitant
and caregiver of these trends.
5.2 Capability 2: Detect anomalies in current data
The ED data mining algorithm and ALZ predictor can work together to
detect anomalies in event data. ED identifies the most significant and
frequent patterns of inhabitant behavior, as well as the likelihood that
the current state is a member of one of these patterns. Whenever the current
state falls within one of these patterns, ALZ can determine the probability
distribution of next events. As a result, when the next event has a low
probability of occurrence, or when the expected next event does not occur
at the expected time, the result is considered an anomaly.
When an anomaly occurs, the home will first try to contact the inhabitant
(through the interactive display for a lesser critical anomaly, or through
the sound system for a more critical anomaly). If the inhabitant does not
respond and the criticality of the anomaly is high, the caregiver will
5.3 Capability 3: Design reminder assistance system
Reminders can be triggered by two situations. First, if the inhabitant
queries the home for his next routine activity, the activity with the highest
probability will be given based on the ALZ prediction. Second, if a critical
anomaly is detected, the environment will initiate contact with the inhabitant
and remind him of the next typical activity. Such a reminder service will
be particularly beneficial for individuals suffering from dementia.
As described in the initial MavHome design, automation assistance is
always available for inhabitants, which is beneficial if some activities
are difficult to perform. A useful feature of the architecture is that
safety constraints are embedded in the Arbiter rule engine. If the inhabitant
or the environment is about to conflict with these constraints, a preventative
action is taken and the inhabitant notified. This can prevent accidents
such as forgetting to turn off the water in the bathtub or leaving the
house with doors unlocked.
The MavHome software architecture has successfully monitored and provided
automation assistance for volunteers living in the MavPad site. We are
currently collecting health-specific data in the MavHome sites and will
be testing in the living environments of recruited residents at the C.C.
Young Retirement Community in Dallas, Texas.
This work is supported by US National Science Foundation under ITR grant
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