# Location Estimation in a Smart Home: System Implementation and Evaluation Using Experimental Data

Authors: Youcef Rahal; Hélène Pigot; Philippe Mabilleau
Journal: International Journal of Telemedicine and Applications
 (2008)

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## Abstract

In the context of a constantly increasing aging population with
cognitive deficiencies, insuring the autonomy of the elders at
home becomes a priority. The DOMUS laboratory is addressing
this issue by conceiving a smart home which can both assist
people and preserve their quality of life. Obviously, the ability to
monitor properly the occupant’s activities and thus provide the
pertinent assistance depends highly on location information inside
the smart home. This paper proposes a solution to localize the
occupant thanks to Bayesian filtering and a set of anonymous
sensors disseminated throughout the house. The localization
system is designed for a single person inside the house. It could
however be used in conjunction with other localization systems
in case more people are present. Our solution is functional in real
conditions. We conceived an experiment to estimate precisely its
accuracy and evaluate its robustness. The experiment consists
of a scenario of daily routine meant to maximize the occupant’s
motion in meaningful activities. It was performed by 14 subjects,
one subject at a time. The results are satisfactory: the system’s
accuracy exceeds 85% and is independent of the occupant’s
profile. The system works in real time and behaves well in
presence of noise.


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## Body

## 1. Introduction

The
outburst  of aging population in the
recent and forthcoming years lays new challenges to provide assistance to the
elders. Moreover, many elders may present degenerative diseases in their later
years which can affect their cognitive abilities. For example, in Canada
[1], people older than
65 will represent at least 25% of the population in 2030, and in 2021 the
number of patients with Alzheimer and other related diseases will reach 592 000 persons
(compared to 364 000 in 1992).
Therefore, it becomes urgent to find a compromise between the need
for constant care (at home or in institutions) and the need to lighten the load
on caregivers. Also, it is highly important to ensure that the ministered care
is personalized and efficient. In this regard, the Smart Assistive Home concept
is an adequate solution. Indeed, it is now possible to provide a safe
environment where the occupants (elders with cognitive deficiencies) can both
be autonomous and feel familiar. The smart home can also be a good alternative
to people who suffered cranial trauma. This population is generally young and
benefits from less specialized institutions than the elders.The DOMUS laboratory, presented in paper [2], plans to address the above
issues by conceiving and testing a smart home. It is located at Université de
Sherbrooke, Canada. The experimental apartment consists of a bedroom, a
bathroom, a living room, a dining room, and a kitchen. The house is filled with
different kinds of sensors in order to provide an accurate information about
the occupant's location and activities. It can also interact with the occupant
via different effectors such as touch screens, audio speakers, and controllable
lights. Robust user-system interaction is ensured thanks to pervasive
computing.The occupant's location is probably one of the most
important data needed to monitor the occupant's activities. Indeed, this
information is useful when interacting with the occupant and in preventing
dangers (by detecting falls, e.g.). In the context of DOMUS, this information
is vital in order to infer the activities already performed or those being
processed, and to provide assistance  where the
occupant is. Our goal is to build a robust and accurate localization system
using the available set of sensors already installed in the smart home. It
covers the case when a single occupant is present at home, which is the most
relevant case when assisting people with cognitive deficiencies. We first
analyze the current localization techniques regarding certain constraints. We
then present the formalism of the method we selected and the experimental
setup. Finally, we present the experiment we conducted, meant to thoroughly
evaluate our system, and we discuss the results obtained. The current paper
extends [3], focuses on
evaluation, and thus completes the corresponding results and discussion
sections. We do not discuss herein higher levels of assistance meant to detect
falls, infer current activities, or interact with the occupant, and which can
be implemented using the information provided by our localization system.


## 2. Techniques and Constraints

Only a few AI and robotics localization techniques can
be applied in a small-scale environment [4]. Moreover, depending on the experimental constraints,
the amount of available solutions is drastically reduced. These constraints
are derived from two principles applied within the
DOMUS technology. First, the occupant's privacy must be guaranteed. Second, the
technology must be unobtrusive. This leads to the four following constraints.(i)
The use of video cameras is prohibited in order to protect the
occupant's privacy.(ii)
The sensors
should be dissimulated in the house to provide a familiar environment.(iii)
The use of tags
worn by the occupant is avoided. This reduces anxiety and the occupant's
feeling of being constantly monitored.(iv)
The most
economic solutions are preferred.The first constraint rejects video localization
systems such as the one described in [5]. Considering the third constraint, solutions such as
radio frequency identification (RFID) tags [6] or Wi-Fi engines [7] are also pushed aside. This is also the case with
infrared (IR) or ultrasonic badges [8]. The cost constraint eliminates floor-based sensors,
as described in [9].
In fact, this leaves us with inexpensive solutions which collect anonymous
data. This includes devices such as IR detectors and a few other sensors which
are already installed in the DOMUS home. Even in quantity, a system based on
such sensors is quite affordable compared to the aforementioned solutions. We
discuss the full list in Section 4.1.Localization errors are induced when relying directly on the sensors, because the latter may sometimes send false information. This may happen because of an intrinsic
error, which can be due to the sensor's error rate or to an occasional error in
the experimental setup as a whole. External factors can also cause false sensor
information. For instance, a draft can close a door and thus trigger a false
event. So can pets sometimes. Therefore, the reliability of the localization
system depends on our ability to analyze the sensor data. In this regard,
recent researches show that sensor fusion is an efficient way to reinforce the
validity of the location data. Whether in robotics [10] or in indoors localization [11], sensor fusion is achieved
through probabilistic methods such as Bayesian filtering.


## 3. Particle Filters

Bayes filters are efficiently used to estimate a
person's location using a set of fixed sensors. In this method, the last known
position and the last sensor event are both used to estimate a new location.
The method represents an interesting compromise between accuracy and
performance, and can be implemented in different ways. Fox et al. describe a
few techniques to implement Bayesian filters and compare their performances
[11]. Based on the
results of that comparison, we decided to implement a localization system using
the particle filters approach. Indeed, Kalman
filters is an accurate method. However, it becomes
inappropriate when different types of sensors are considered. This is the same
for multihypothesis tracking. Also, grid-based approaches are robust but their
poor efficiency excludes them. Finally, topological approaches do not meet the
criteria, except for the robustness. On the other hand, particle filters are an
efficient technique which is accurate, robust, and easy to implement. This
technique is also adequate when different types of sensors are used, which is
our case. We will briefly describe the technique. Refer to [11, 12] for further reading.At any moment, the estimate of the occupant's location
can be modeled  as the belief in the fact that he/she is located at positionxt at instant t, given a series of previous sensor observations o0,…,ot from instant 0
to instant t : Bel(xt)=p(xt∣ot,ot−1,…,o1,o0). Given the Markov postulate which stipulates that
only the last observation ot−1 is relevant,
the expression of Bel(xt) is simplified
using Bayes conditional probabilities formula. In the discrete case, it
becomes (1)Bel(xt)=αp(ot∣xt)∑xt−1p(xt∣xt−1)Bel(xt−1), where α is a
normalization constant. p(ot∣xt) represents the
probability of observing an event ot given that the
occupant is at position xt at instant t. It is the perceptual model. In practice, each sensor
is associated to a space-dependent probability density function that represents
the likelihood of receiving an event from that sensor depending on the occupant
location. On the other hand, p(xt∣xt−1) represents the
probability that the occupant moves from xt−1 to xt between
instants t−1 and t. It is the dynamic model. In practice, it represents
the motion profile of the occupant. For instance, a fast-moving occupant has a
wider dynamic probability density than a slower one. This function should also
include the environmental layout. In a more rigorous approach, it should even
depend on time to reflect the changes in activities during the day or the week.The algorithm based on particle filters estimates the
location thanks to a set ofn positions
(particles). At first, these particles are drawn randomly and uniformly on the
available space. An equal weight is devoted to each particle (1/n for the sake of
normalization). These particles model Bel(x0) which, in this
case, is uniform. When an event occurs (at t=1), Bel(x1) is computed
according to (1). This operation changes the weights of the sample. The
distribution obtained is then used to draw a new sample with equal weights. The
new sample is from now on more centered on the latest event position. This
operation is repeated every time a new event is observed. At any time, the
estimate (belief) of the occupant's position in a place is simply the addition of
the weights of all the particles confined in that location. Consequently, we
can observe in practice a cloud of particles “following” the occupant.Before using this technique for location estimation in
the smart home, we need to make an inventory of all the sensors we use and then
attribute probability density functions to them. We also need to model the
occupant's motion. The next section deals with these aspects.


## 4. Experimental Configuration

### 4.1. The Sensors

The list of sensors we consider and which are already
installed and plugged in the DOMUS apartment include the
following.(i)
IR presence
detectors.(ii)
Tactile carpets
(as seen later in Section6.1, for the purpose of this study, these sensors
are used only as location reference and not to infer the occupant's position).(iii)
Smart light
switches. An event is received every time the occupant turns the lights on or
off.(iv)
Electric
contacts on doors (including closets, drawers, pantries, etc.). An event is
received every time a door is opened or closed.(v)
Pressure
detectors. These can be placed under the mattress, for example, in order to
detect if the occupant is lying on the bed.The number of installed sensors varies depending on
the room and the areas of interest (see Table1). For example, in the bedroom
there is only one IR detector that covers the entire room area whereas two are
installed in the kitchen: one covers the kitchen globally while the other is
directed at the stove only. It is worth noting that none of these sensors would
be able to give identification about the person who triggered it—as opposed
to devices such as video cameras. They are also quite unobtrusive. That is
clearly an advantage of our approach since it provides the required privacy
imposed by our constraints.Table 1
List of sensors per room.
Entrance hallLiving roomDining roomKitchenBathroomBedroomTotalIR11223110Tactile
carpets13263318Light
switches1113118Door
contacts3003051048Pressure
detectors0000011


### 4.2. Probability Densities

We assign a probability density function to each
sensor. This function depends on the sensor type and, mainly, on the range the
sensor covers. For instance, IR detectors have a wide probability density,
taking into account the fact that they cover large portions of a room (of course, this depends
on how they are installed; they can also be installed to cover small
regions). Moreover, they can be triggered more
easily than the other sensors. Light switch sensors are generally more reliable
and are triggered in a limited area. So are the door contacts, the tactile
carpets, and the pressure detectors. That is why we assigned to them more
compact probability densities than to IR detectors. We list the attributes of
these functions in Table2. These functions are either 2D square functions
defined as(2)p(x,y)={1(xmax−xmin)(ymax−ymin),xmin<x<xmax,ymin<y<ymax,0,elsewhere, or 2D circular functions as(3)p(x,y)={1πR2,(x−x0)2+(y−y0)2<R2,0,elsewhere.Table 2
Density functions per sensor type.
SensorDensity function typeTypical density function range (m2)IRSquare20Tactile
carpetsSquare0.8Light
switchesCircular1Door
contactsCircular1.5Pressure
detectorsSquare2We do not add noise to these functions per se.
However, we make sure that, at every step, about 10% of the particles are drawn
randomly from all the available space. We noticed
that adding noise to density functions could result in a static particle
cloud after successive sensor events in the same place. In other words, the
cloud can converge in a relatively small area and remains there regardless of
new sensor events. By adding the uniform noise, we ensure that there are enough
particles in the environment to avoid the cloud's immobilization. As for the
dynamics, the occupant's motion is modeled by a 2D normal density which
standard deviation encompasses half of the apartment's area. Although this
function is quite rough, it models remarkably well the fact that, between
instantst−1 and t, the likelihood that the occupant covers a distance d quickly
decreases when d increases.


## 4.1. The Sensors

The list of sensors we consider and which are already
installed and plugged in the DOMUS apartment include the
following.(i)
IR presence
detectors.(ii)
Tactile carpets
(as seen later in Section6.1, for the purpose of this study, these sensors
are used only as location reference and not to infer the occupant's position).(iii)
Smart light
switches. An event is received every time the occupant turns the lights on or
off.(iv)
Electric
contacts on doors (including closets, drawers, pantries, etc.). An event is
received every time a door is opened or closed.(v)
Pressure
detectors. These can be placed under the mattress, for example, in order to
detect if the occupant is lying on the bed.The number of installed sensors varies depending on
the room and the areas of interest (see Table1). For example, in the bedroom
there is only one IR detector that covers the entire room area whereas two are
installed in the kitchen: one covers the kitchen globally while the other is
directed at the stove only. It is worth noting that none of these sensors would
be able to give identification about the person who triggered it—as opposed
to devices such as video cameras. They are also quite unobtrusive. That is
clearly an advantage of our approach since it provides the required privacy
imposed by our constraints.Table 1
List of sensors per room.
Entrance hallLiving roomDining roomKitchenBathroomBedroomTotalIR11223110Tactile
carpets13263318Light
switches1113118Door
contacts3003051048Pressure
detectors0000011


## 4.2. Probability Densities

We assign a probability density function to each
sensor. This function depends on the sensor type and, mainly, on the range the
sensor covers. For instance, IR detectors have a wide probability density,
taking into account the fact that they cover large portions of a room (of course, this depends
on how they are installed; they can also be installed to cover small
regions). Moreover, they can be triggered more
easily than the other sensors. Light switch sensors are generally more reliable
and are triggered in a limited area. So are the door contacts, the tactile
carpets, and the pressure detectors. That is why we assigned to them more
compact probability densities than to IR detectors. We list the attributes of
these functions in Table2. These functions are either 2D square functions
defined as(2)p(x,y)={1(xmax−xmin)(ymax−ymin),xmin<x<xmax,ymin<y<ymax,0,elsewhere, or 2D circular functions as(3)p(x,y)={1πR2,(x−x0)2+(y−y0)2<R2,0,elsewhere.Table 2
Density functions per sensor type.
SensorDensity function typeTypical density function range (m2)IRSquare20Tactile
carpetsSquare0.8Light
switchesCircular1Door
contactsCircular1.5Pressure
detectorsSquare2We do not add noise to these functions per se.
However, we make sure that, at every step, about 10% of the particles are drawn
randomly from all the available space. We noticed
that adding noise to density functions could result in a static particle
cloud after successive sensor events in the same place. In other words, the
cloud can converge in a relatively small area and remains there regardless of
new sensor events. By adding the uniform noise, we ensure that there are enough
particles in the environment to avoid the cloud's immobilization. As for the
dynamics, the occupant's motion is modeled by a 2D normal density which
standard deviation encompasses half of the apartment's area. Although this
function is quite rough, it models remarkably well the fact that, between
instantst−1 and t, the likelihood that the occupant covers a distance d quickly
decreases when d increases.


## 5. Implementation

Our system is implemented in Java and connects as a
client to a server which aggregates sensors and forwards events as soon as they
occur. We also implemented a GUI that displays the client's state and the
current prediction of the system. Figure1 shows typical use of our system in
real conditions. These are based on the observation of its behavior during the
execution of a short scenario, similar to the one we describe in Section 6.1.
We perform the scenario and we observe the system's predictions, knowing the
occupant's location. In Figure 1(a), we start the
system. The occupant is already in the living room. However, since no sensor
event is detected yet, the occupant is likely to be anywhere in the house. Consequently,
the particle cloud is uniformly spread. In Figure 1(b), the occupant moves
towards the kitchen and the living room IR sensor is activated. The system
consequently computes that the occupant is in the living room with a
probability of 91%. The particle cloud is contained in the corresponding area.
In Figure 1(c), the occupant enters the kitchen and the IR sensor there is activated.
The system concludes that the occupant is in the kitchen at 91%. Finally, the
occupant opens the fridge door and a door-contact event is received. The cloud
is quickly centered on the corresponding area. Here also, the probability that
the occupant is in the kitchen is high: 81%. The probability is less than in
the previous case because the probability density function (the perceptual
model) associated to the fridge's door-contact sensor encompasses a small zone
outside the kitchen. The particle cloud is very dynamic and smoothly follows
the occupant's path. The computation time (the one needed to infer the
occupant's location when receiving a new event) is inferior to 1
second and thus the system is responsive in real
time.The localization
system in a real condition situation. The points
show the particle cloud. The shaded areas indicate where the occupant is likely
to be present.
(a)
The system at
start(b)
The occupant moves in the
living room(c)
The occupant enters the
kitchen(d)
The occupant opens the
fridge door


## 6. System Evaluation

While the preliminary results are encouraging, it is
important to thoroughly evaluate the system's accuracy and robustness. It
becomes then necessary to monitor how it behaves in real-life situations.
Therefore, we conceived an experiment to collect data with people moving inside
the apartment, one person at a time. The subjects have no prior familiarity
with the apartment.


### 6.1. The Experiment Scenario

Each subject performs a scenario of about 50 minutes
long. It consists of the contracted routine of getting back to home in the
evening and leaving in the following morning. This scenario maximizes the
occupant's motion in every room of the apartment, all the while allowing to
perform meaningful activities. It also maximizes the number of activated
sensors during tasks execution. This ensures that our system is able to locate
the person with accuracy in real conditions. The broad lines of the scenario
are(1)
entering the
house;(2)
washing hands
in the bathroom;(3)
preparing a
sandwich in the kitchen;(4)
eating the
sandwich in the dining room;(5)
preparing
coffee in the kitchen;(6)
reading a
magazine in the living room while drinking coffee;(7)
going to the
bathroom;(8)
lying in the
bedroom (the subject is allowed to read while being on the bed);(9)
getting up and
making toilette in the bathroom;(10)
leaving the
house.To avoid cognitive load, we ask the subjects to
perform this scenario by periods of about 10 minutes each. For example, the
three first steps of the scenario fit into such a period. At the end of each
period, we stop the data collection and explain the next set of tasks. We
validated the entire scenario during a preexperimentation with 3 team members.
This helped us adjust the steps and make the scenario more fluent.The accuracy of the location estimation is checked
thanks to a video camera and the tactile carpets. The camera is located in the
kitchen and is used to record the subject's activities during the preparation
of the sandwich. Five tactile carpets are installed on the kitchen's floor. The
analysis of the video validates the tactile carpets accuracy
(since we use the tactile carpets in the framework
of a complex acquisition system, the accuracy we measure is not rigorously
equal to the one we would obtain if the carpets were used separately from this
system). Knowing this accuracy, we use the tactile
carpets in every room of the apartment as reliable position indicators. We
compare this reference position information with the output of our system. This
gives the system's accuracy, that is, the accuracy of the location information
resulting of Bayesian filtering using the rest of the sensors.


### 6.2. The Sample

The sample of the 14 subjects who participated in the
experiment is composed of 10 females and 4 males. Their age distribution is
shown on Figure2. The age is well distributed between 22 and 73, with a mean
value of 50 years old. There is however a slight majority of people older than
50. To reflect the population targeted by the research at DOMUS, we recruited
at least half of the subjects from that age group.Figure 2
Distribution of the age of the subjects.


## 6.1. The Experiment Scenario

Each subject performs a scenario of about 50 minutes
long. It consists of the contracted routine of getting back to home in the
evening and leaving in the following morning. This scenario maximizes the
occupant's motion in every room of the apartment, all the while allowing to
perform meaningful activities. It also maximizes the number of activated
sensors during tasks execution. This ensures that our system is able to locate
the person with accuracy in real conditions. The broad lines of the scenario
are(1)
entering the
house;(2)
washing hands
in the bathroom;(3)
preparing a
sandwich in the kitchen;(4)
eating the
sandwich in the dining room;(5)
preparing
coffee in the kitchen;(6)
reading a
magazine in the living room while drinking coffee;(7)
going to the
bathroom;(8)
lying in the
bedroom (the subject is allowed to read while being on the bed);(9)
getting up and
making toilette in the bathroom;(10)
leaving the
house.To avoid cognitive load, we ask the subjects to
perform this scenario by periods of about 10 minutes each. For example, the
three first steps of the scenario fit into such a period. At the end of each
period, we stop the data collection and explain the next set of tasks. We
validated the entire scenario during a preexperimentation with 3 team members.
This helped us adjust the steps and make the scenario more fluent.The accuracy of the location estimation is checked
thanks to a video camera and the tactile carpets. The camera is located in the
kitchen and is used to record the subject's activities during the preparation
of the sandwich. Five tactile carpets are installed on the kitchen's floor. The
analysis of the video validates the tactile carpets accuracy
(since we use the tactile carpets in the framework
of a complex acquisition system, the accuracy we measure is not rigorously
equal to the one we would obtain if the carpets were used separately from this
system). Knowing this accuracy, we use the tactile
carpets in every room of the apartment as reliable position indicators. We
compare this reference position information with the output of our system. This
gives the system's accuracy, that is, the accuracy of the location information
resulting of Bayesian filtering using the rest of the sensors.


## 6.2. The Sample

The sample of the 14 subjects who participated in the
experiment is composed of 10 females and 4 males. Their age distribution is
shown on Figure2. The age is well distributed between 22 and 73, with a mean
value of 50 years old. There is however a slight majority of people older than
50. To reflect the population targeted by the research at DOMUS, we recruited
at least half of the subjects from that age group.Figure 2
Distribution of the age of the subjects.


## 7. Results and Discussion

First of all, we observe the distribution of the
system's belief. As shown in Figure3, the mean belief is 88% with a standard
deviation of 8% for all the data from our sample. There is an upper limit to
the system's belief which is close to 90%. It is due to the 10% of noise
particles that we draw randomly at every iteration. These are uniformly
distributed in the smart home, and therefore the concentration of particles in
a room cannot significantly  exceed 90%.Figure 3
Distribution of the belief of our system, regrouping the data from the whole
sample.The
events  with a low belief usually occur when the
occupant goes from a room to another. With the following events, the belief
gets significantly higher, showing that the particle
cloud replaces itself quickly and correctly. Moreover, the global system's
accuracy is 85%. This is the percentage of events where the system predicts
accurately the position of the occupant, independently from the value of the
system's belief. There is however an expected correlation between the system's
accuracy and its belief. Events with a high belief are less prone to be false,
and vice versa. We can therefore increase the system's accuracy by rejecting
the events with the smallest beliefs. Figure4 shows the
variation of accuracy
with the value of the rejection cut on the system's belief. By rejecting events
with belief less than 80%, the accuracy of the system becomes 88%. This cut
rejects only 6% of true events and up to a third of false ones. The cut can of
course be more drastic: by rejecting events with a belief less than 90%,
accuracy can be increased to 95%. However, the cut at 80% is more reasonable,
since this value is the mean belief for false events. In working conditions,
one can vary the cut depending on how critical the location information has to
be, that is, depending on the context and on the application that needs the
information.Figure 4
Variation of the accuracy depending on the value of the rejection cut on the system's
belief.The most important limitation is that, as expected,
our system gives incoherent results in case more than one person are present in
the house. Indeed, when two people (or more) activate sensors simultaneously,
the particle cloud tends to alternate between their respective locations. An
upper-level solution is then necessary in order to identify and handle this
trend. This limitation is a direct consequence of the fact that the information
we collect is anonymous. It is because we avoid using devices that compromise
the occupant's familiarity with the environment or make her/him feel monitored
by wearing an RFID tag, for example (such systems
also have a drawback if the occupant decides to withdraw the tag.).
In this regard, our system is destined to be used to locate a single person in
the house. When visitors are present, our system could be used in conjunction
with another system: for example, requiring that visitors wear an RFID tag in
order to differentiate them from the occupant. In this case, locating the
occupant may also be regarded as less important than when the occupant is alone
at home (DOMUS is designed for only one person with cognitive deficiencies per
smart home. Visitors are mainly caregivers or family members).In order to further evaluate our system, we study how
it behaves regarding other experimental aspects, such as the analysis of
possible correlations with the occupant's profile. We also investigate the best
sensor  configuration and their comparison per
activity, the occupant's dynamics, the behavior in presence of noise, and
finally the performance of the system.


### 7.1. Correlation with the Occupant's Profile

One of the main objectives of our study is to evaluate
whether or not the system should be personalized according to the occupant's
profile. The only profile-dependent parameter that influences the Bayesian
filtering formula is the dynamics. Since the subjects were healthy, without
notable variations, the most accurate variable that relates to the dynamics is
the age. Therefore, the hypothesis is that if the system depends on the
occupant's age or on their dynamics, consequently on their profile, the system
would require adjustments upon deployment to fit them. Figure5 shows that
there is no significant correlation between accuracy and age (correlation
coefficient ρ=0.36) and that
accuracy remains stable from a subject to another. This result leads to two
interesting conclusions. First, the data collected shows sufficient consistency
to allow us to limit the number of subjects to 14. In fact, increasing this
number will not give more confidence in the results. Second, it gives us the
ability to deploy this system immediately in new homes, since it is
profile-independent. This is good news regarding both economic and time
constraints  (obviously, a configuration phase is required
for all deployments, and would be of approximately equal lengths given an
apartment size and a required number of sensors).Figure 5
Variation of accuracy with the subjects' age.


### 7.2. Sensor Configuration

In the purpose of addressing economic concerns, we
study which sensor configurations are optimal. This information is important if
one has to limit the redundancy, even though the latter could be useful to
provide a robust environment. We therefore measure the system's accuracy using
different sets of sensors. The complete information is in Table3. In order to
achieve this comparison, we use data from only one representative subject, whom
we choose based on the mean age (50±17 years old) and
the duration of the experiment (48±8 minutes). The
values of the accuracy and mean belief using all the sensors are then slightly
different than the mean ones presented in the previous section. The infrared
sensors alone give comparable results with those obtained using all the
sensors. That is due to the fact that their number (there is at least one such
sensor in each room) and their spatial configuration (each one covers roughly
the area of a room) dominate the global sensor configuration. However, it is
worth noting that infrared sensors are not the most reliable type, for they are
more prone to false activations, due to animal presence or a heat disturbance,
for example. Consequently, deploying the system in a real setting would probably
require at least one other type of sensors to ensure reliability.Table 3
Accuracy and mean belief using different sensors.
Sensor setAccuracy (%)Mean belief (%)All8788IR8889Light
switches5075Light
switches and door contacts7780


### 7.3. Sensors per Activity

The sensors are compared based on an activity
criterion. We select two activities: walking and preparing a sandwich. Other
activities—such as dining, reading and sleeping—do not generate enough
sensor data with the present scenario to be considered for this comparison.
Table4 shows the results we obtain using different sets of sensors. For the
walking activity, the IR sensors are the most accurate, because the occupant
seldom activates other sensors while moving around the house. However, when
preparing a sandwich, the occupant often opens the fridge and various drawers
in the kitchen. Therefore, the door contact sensors are almost as accurate as
the IR ones. This becomes useful for localization in case the activity being
performed is known. In the previous section, we saw that IR sensors are at the
core of this localization system, although they can present reliability issues.
In case of preparing dinner, door contacts can be as accurate as and even more
reliable than IR sensors, consequently more appropriate to locate the occupant
during that activity.Table 4
Accuracy and mean belief per activity, using different
sensors.
ActivitySensor setAccuracy (%)Mean belief (%)WalkingAll8888IR8789Light switches3375Light switches and door contacts3872Preparing sandwichAll9089IR9190Light switches1060Light switches and door contacts8587


### 7.4. The Dynamic Model

In order to test the effects of the dynamics on the
model, we analyze the system's accuracy while modifying the Gaussian dynamic
function. The system's accuracy remains stable on a long range of the
Gaussian's size (Figure6). However, when the standard deviation of the
Gaussian becomes too small, the particle cloud inertia increases and the model
fails to reflect a natural motion. On the contrary, larger functions seem to
behave very well. But this changes when coping with noise.Figure 6
Variation of the system's accuracy with the occupant's dynamics, modeled by the
Gaussian function'sσ.


### 7.5. Behavior in Presence of Noise

The experimentation took place in a laboratory.
Therefore, even if the setting is similar to that of an apartment, disturbances
are controlled. The subject is always alone and performing predefined
activities. It is consequently important to analyze the behavior of the
localization system in case of noise. First of all, since no significant noise
was present in the data we collected, we generate random noise in order to
complete our tests. Table5 shows how the accuracy and mean belief are affected
when contaminating the data with random sensor noise. The system is
exceptionally stable and remains accurate at 84% even in the presence of 2.5%
noise. Increasing the percentage of noise to 5% does not affect the
localization accuracy. It is worth noting that since there was no observable
noise in the data, 1% of noise is already a conservative value.Table 5
Variation of accuracy and mean belief with noise.
Noise (%)Accuracy (%)Mean belief (%)08888188882.5848558485In a second step, we analyze the impact of the noise
when changing the dynamic model. We thus introduce 2.5% noise while modifying
the dynamics (Figure7). Small Gaussians still fail to reproduce the occupant's
dynamics. However, in the presence of noise, larger Gaussians become
problematic too. The larger Gaussians model faster motion, therefore failing to
reject false events even if they are significanty distant than the actual
occupant's location. The Gaussian's size has then to be comparable to that of
the apartment to ensure the best system's accuracy.Figure 7
Variation of the system's accuracy with the dynamics, in presence of 2.5%
noise.


### 7.6. The Ideal Number of Particles

Finally, for the sake of optimization, we want to
infer the best parameters' settings for the algorithm. Therefore, we study how
the number of particles used may affect the prediction accuracy
(Figure8). As
expected, the more particles are used, the best the prediction is. However, an
increase in the number of particles leads to an increase in computing time.
Therefore, the cloud dynamics fails to replicate the occupant's motion. The
system's accuracy is fairly stable when the particle number is in the range 500
to 2000. Therefore, the smallest value (500) becomes the best choice since it
is the closest to reproduce the occupant's motion in real time.Figure 8
Variation of accuracy depending on the number of particles used in the
algorithm.


## 7.1. Correlation with the Occupant's Profile

One of the main objectives of our study is to evaluate
whether or not the system should be personalized according to the occupant's
profile. The only profile-dependent parameter that influences the Bayesian
filtering formula is the dynamics. Since the subjects were healthy, without
notable variations, the most accurate variable that relates to the dynamics is
the age. Therefore, the hypothesis is that if the system depends on the
occupant's age or on their dynamics, consequently on their profile, the system
would require adjustments upon deployment to fit them. Figure5 shows that
there is no significant correlation between accuracy and age (correlation
coefficient ρ=0.36) and that
accuracy remains stable from a subject to another. This result leads to two
interesting conclusions. First, the data collected shows sufficient consistency
to allow us to limit the number of subjects to 14. In fact, increasing this
number will not give more confidence in the results. Second, it gives us the
ability to deploy this system immediately in new homes, since it is
profile-independent. This is good news regarding both economic and time
constraints  (obviously, a configuration phase is required
for all deployments, and would be of approximately equal lengths given an
apartment size and a required number of sensors).Figure 5
Variation of accuracy with the subjects' age.


## 7.2. Sensor Configuration

In the purpose of addressing economic concerns, we
study which sensor configurations are optimal. This information is important if
one has to limit the redundancy, even though the latter could be useful to
provide a robust environment. We therefore measure the system's accuracy using
different sets of sensors. The complete information is in Table3. In order to
achieve this comparison, we use data from only one representative subject, whom
we choose based on the mean age (50±17 years old) and
the duration of the experiment (48±8 minutes). The
values of the accuracy and mean belief using all the sensors are then slightly
different than the mean ones presented in the previous section. The infrared
sensors alone give comparable results with those obtained using all the
sensors. That is due to the fact that their number (there is at least one such
sensor in each room) and their spatial configuration (each one covers roughly
the area of a room) dominate the global sensor configuration. However, it is
worth noting that infrared sensors are not the most reliable type, for they are
more prone to false activations, due to animal presence or a heat disturbance,
for example. Consequently, deploying the system in a real setting would probably
require at least one other type of sensors to ensure reliability.Table 3
Accuracy and mean belief using different sensors.
Sensor setAccuracy (%)Mean belief (%)All8788IR8889Light
switches5075Light
switches and door contacts7780


## 7.3. Sensors per Activity

The sensors are compared based on an activity
criterion. We select two activities: walking and preparing a sandwich. Other
activities—such as dining, reading and sleeping—do not generate enough
sensor data with the present scenario to be considered for this comparison.
Table4 shows the results we obtain using different sets of sensors. For the
walking activity, the IR sensors are the most accurate, because the occupant
seldom activates other sensors while moving around the house. However, when
preparing a sandwich, the occupant often opens the fridge and various drawers
in the kitchen. Therefore, the door contact sensors are almost as accurate as
the IR ones. This becomes useful for localization in case the activity being
performed is known. In the previous section, we saw that IR sensors are at the
core of this localization system, although they can present reliability issues.
In case of preparing dinner, door contacts can be as accurate as and even more
reliable than IR sensors, consequently more appropriate to locate the occupant
during that activity.Table 4
Accuracy and mean belief per activity, using different
sensors.
ActivitySensor setAccuracy (%)Mean belief (%)WalkingAll8888IR8789Light switches3375Light switches and door contacts3872Preparing sandwichAll9089IR9190Light switches1060Light switches and door contacts8587


## 7.4. The Dynamic Model

In order to test the effects of the dynamics on the
model, we analyze the system's accuracy while modifying the Gaussian dynamic
function. The system's accuracy remains stable on a long range of the
Gaussian's size (Figure6). However, when the standard deviation of the
Gaussian becomes too small, the particle cloud inertia increases and the model
fails to reflect a natural motion. On the contrary, larger functions seem to
behave very well. But this changes when coping with noise.Figure 6
Variation of the system's accuracy with the occupant's dynamics, modeled by the
Gaussian function'sσ.


## 7.5. Behavior in Presence of Noise

The experimentation took place in a laboratory.
Therefore, even if the setting is similar to that of an apartment, disturbances
are controlled. The subject is always alone and performing predefined
activities. It is consequently important to analyze the behavior of the
localization system in case of noise. First of all, since no significant noise
was present in the data we collected, we generate random noise in order to
complete our tests. Table5 shows how the accuracy and mean belief are affected
when contaminating the data with random sensor noise. The system is
exceptionally stable and remains accurate at 84% even in the presence of 2.5%
noise. Increasing the percentage of noise to 5% does not affect the
localization accuracy. It is worth noting that since there was no observable
noise in the data, 1% of noise is already a conservative value.Table 5
Variation of accuracy and mean belief with noise.
Noise (%)Accuracy (%)Mean belief (%)08888188882.5848558485In a second step, we analyze the impact of the noise
when changing the dynamic model. We thus introduce 2.5% noise while modifying
the dynamics (Figure7). Small Gaussians still fail to reproduce the occupant's
dynamics. However, in the presence of noise, larger Gaussians become
problematic too. The larger Gaussians model faster motion, therefore failing to
reject false events even if they are significanty distant than the actual
occupant's location. The Gaussian's size has then to be comparable to that of
the apartment to ensure the best system's accuracy.Figure 7
Variation of the system's accuracy with the dynamics, in presence of 2.5%
noise.


## 7.6. The Ideal Number of Particles

Finally, for the sake of optimization, we want to
infer the best parameters' settings for the algorithm. Therefore, we study how
the number of particles used may affect the prediction accuracy
(Figure8). As
expected, the more particles are used, the best the prediction is. However, an
increase in the number of particles leads to an increase in computing time.
Therefore, the cloud dynamics fails to replicate the occupant's motion. The
system's accuracy is fairly stable when the particle number is in the range 500
to 2000. Therefore, the smallest value (500) becomes the best choice since it
is the closest to reproduce the occupant's motion in real time.Figure 8
Variation of accuracy depending on the number of particles used in the
algorithm.


## 8. Conclusion

We presented in this paper the localization system we
put in place in the DOMUS laboratory. This system detects a single person's location
by means of various anonymous sensors installed in the smart home. We set an
experimental scenario in order to evaluate the accuracy, with people performing
significant tasks in the smart home. The results obtained with these data are
very satisfactory. The algorithm based on Bayesian filtering shows a mean
localization accuracy of 85%. The system is fast and robust regarding noise.
Moreover, since it is profile-independent, it can easily be deployed in the
future homes that are being conceived in the laboratory. It will be interesting
to observe the behavior of our system once it gets integrated in applications
meant to provide high levels of assistance. Above all, it is crucial to measure
the localization accuracy needed for different sets of applications. A possible
continuation of this work would be to check the possibility to locate two or
more people with the same experimental setup (the presence of pets is also
interesting to investigate). This would enable to respect the cost and
anonymousness constraints when several people are present. A multiagent
approach is being investigated at DOMUS and already is giving promising
results. Moreover, using data from people with cognitive deficiencies, in a
real setting, would help in consolidating the results from the present study.



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*Source: 142803-2008-04-16.xml*