Improving Activity Monitoring Through a Hierarchical Approach
Xavier Rafael-Palou, Eloisa Vargiu, Guillem Serra and Felip Miralles
Barcelona Digital Technology Center, Barcelona, Spain
Keywords:
Activity Monitoring, Telemoniroting, Sensor-based approach, Ambient Assisted Living.
Abstract:
Performance of sensor-based telemonitoring and home support systems depends, among other characteristics,
on the reliability of the adopted sensors. Although binary sensors are quite used in the literature and also in
commercial solutions to identify user’s activities, they are prone to noise and errors. In this paper, we present
a hierarchical approach, based on machine learning techniques, aimed at reducing error from the sensors.
The proposed approach is aimed at improving the classification accuracy in detecting if a user is at home,
away, alone or with some visits. It has been integrated in a sensor-based telemonitoring and home support
system. Results show an overall improvement of 15% in accuracy with respect to a rule-based approach. The
system is part of the BackHome project and is currently running in 2-healthy-users’ home in Barcelona and in
3-end-users’ home in Belfast.
1 INTRODUCTION
Activity monitoring is an increasingly important re-
search area due to the fact that it can be applied to
many real-life, human-centric problems, such as el-
dercare and healthcare. Recent studies have shown
that physical activities in daily life are an important
predictor of risk of hospital readmission and mortal-
ity in patients with chronic diseases (Yohannes et al.,
2002) (Pitta et al., 2005).
Monitoring users’ activities allows therapists,
caregivers, and relatives to become aware of user con-
text by acquiring heterogeneous data coming from
sensors and other sources. Moreover, activity mon-
itoring provides elaborated and smart knowledge to
clinicians, therapists, carers, families, and the pa-
tients themselves by inferring user habits and be-
haviour. Various methods of subjective and objec-
tive physical activity assessment tools have been de-
veloped. Subjective methods, such as diaries, ques-
tionnaires and surveys, are inexpensive tools. How-
ever, these methods often depend on individual ob-
servation and subjective interpretation, which make
the assessment results inconsistent (Meijer et al.,
1991). On the other hand, objective techniques use re-
mote monitoring techniques relying on sensors, such
as home-automation, wearable and/or environmental
ones (Warren, 2000).
A lot of telemonitoring systems have been pro-
posed in the literature (Carneiro et al., 2008) (Cor-
chado et al., 2010) (Mitchell et al., 2011) and are cur-
rently adopted in real environments (Scanaill et al.,
2006), enabling the healthcare provider to get feed-
back on monitored people and their health status pa-
rameters.
Sensor-based telemonitoring systems rely on a
conjunction of sensors, each one devoted to monitor a
specific status, a specific activity or activities related
to a specific location. Sensor technology can range
from vital signal devices –such as blood pressure
monitors, heart rate monitors and devices which can
measure body temperature– to sensors which can de-
tect presence in a room or detect a door being opened
(Nugent et al., 2008). Once all of the data have been
recorded it is then necessary for data processing to
take place to identify if the person requires a form
of assistance since an unusual activity has been rec-
ognized. Of course, this requires a certain degree of
intelligence which should take into consideration the
current state of the environment, the performed ac-
tivity and/or some physiological data (Cook and Das,
2007). Due to issues regarding personal privacy, tech-
nical installations, and costs of technology the most
adopted sensors are anonymous binary sensors (Nu-
gent et al., 2008). Binary sensors do not have the
ability to directly identify people and can only present
two possible values as outputs (“0” and “1”). Typical
examples of binary sensors deployed within smart en-
vironments include pressure mats, door sensors, and
movement detectors. A number of studies reporting
159
Rafael-Palou X., Vargiu E., Serra G. and Miralles F..
Improving Activity Monitoring Through a Hierarchical Approach.
DOI: 10.5220/0005437701590168
In Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell-
2015), pages 159-168
ISBN: 978-989-758-102-1
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
the use of binary and related sensors have been under-
taken for the purposes of activity recognition (Tapia
et al., 2004). Nevertheless, sensor data can be con-
sidered to be highly dynamic and prone to noise and
errors (Ranganathan et al., 2004).
The increasing social demand for intelligent tele-
monitoring systems makes necessary to put more em-
phasis in activity recognition methods that deal with
environments prone to errors (Jafari et al., 2005). In
this paper, we make a step forward in this direction
by introducing a novelty and effective discriminative
method based on machine learning. The goal is to dis-
cover the very initial but crucial information regard-
ing the user location and recognition whether s/he is
alone or with visits under complex, noisy and unstable
environments. The method has been evaluated at two
healthy user homes in Barcelona where it is currently
running. Moreover, the system has been installed and
it is working in 3-end-users’ home in Belfast. Evalua-
tion is performed by using a novelty technique to gain
further user objectivity based on the information col-
lected from an activity tracking mobile application.
The rest of the paper, is organized as follows. Sec-
tion 2 illustrates the implemented sensor-based tele-
monitoring and home support system. In Section 3,
we present the adopted approach aimed at improving
habit recognition whereas in Section 4, we summarize
our preliminary results that show the improvement in
adopting the proposed solution. In Section 5, we re-
call relevant work related to the partial detection of
some of the activities that concern us and with set-
tings similar to those presented in this work. Section
6 ends the paper with conclusions and future work.
2 THE SENSOR-BASED
TELEMONITORING AND
HOME SUPPORT SYSTEM
To monitor users’ activities, we develop a sensor-
based telemonitoring and home support system (SB-
TMHSS) able to monitor the evolution of the user’s
daily life activity. The implemented system is able to
monitor indoor activities by relying on a set of home
automation sensors and outdoor activities by using
Moves
1
. Information gathered by the SB-TMHSS is
also used to provide context-awareness by relying on
ambient intelligence (Casals et al., 2014). Monitoring
users’ activities through the SB-TMHSS gives us also
the possibility to automatically assess quality of life
of people (Vargiu et al., 2014). In this Section, we
1
http://www.moves-app.com/
briefly describe the SB-TMHSS, the interested reader
may refer to (Miralles et al., 2014) for further details.
The high-level architecture of the SB-TMHSS is
depicted in Figure 1. As shown, its main components
are: home; healthcare center; middleware; and intel-
ligent monitoring system.
Figure 1: Main components of the SB-TMHSS.
At home, a set of sensors are installed. In particu-
lar, we use presence sensors (i.e., Everspring SP103),
to identify the room where the user is located (one
sensor for each monitored room); a door sensor (i.e.,
Vision ZD 2012), to detect when the user enters or ex-
its the premises; electrical power meters and switches,
to control leisure activities (e.g., television and pc);
and pressure mats (i.e., bed and seat sensors) to mea-
sure the time spent in bed (wheelchair). The system
is also composed of a network of environmental sen-
sors that measures and monitors environmental vari-
ables like temperature, but also potentially danger-
ous events like gas leak, fire, CO escape and pres-
ence of intruders. All the adopted sensors are wire-
less z-wave
2
. They send the retrieved data to a col-
lector (based on Raspberry pi
3
). The Raspberry pi
collects all the retrieved data and securely redirects
them to the cloud where they will be stored, pro-
cessed, mined, and analyzed. The proposed solution
relies on z-wave technology for its efficiency, porta-
bility, interoperability, and commercial availability.
In fact, on the contrary of other wireless solutions
(e.g., ZigBee), z-wave sensors are able to communi-
cate with any z-wave device. Moreover, we adopt a
solution based on Raspberry pi because it is easy-to-
use, cheap, and scalable. We are also using the user’s
smartphone as a sensor by relying on Moves, an app
for smartphones able to recognize physical activities
2
http://www.z-wave.com/
3
http://www.raspberrypi.org/
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and movements by transportation. Among the activ-
ity trackers currently on the market, we select Moves
because it does not need user intervention being al-
ways active in background. The user interacts with
the overall system through a suitable interface aware
of end-user needs and preferences.
The middleware, which acts as a SaaS, is com-
posed by a secure communication and authentication
module; API module to enable the collector transmit-
ting all the data from sensors to make them available
to the intelligent monitoring system; and further utili-
ties such as load balancing and concurrency.
In order to cope with the data necessities of the
actors of the system (i.e., therapists, caregivers, rela-
tives, and end-users themselves), an Intelligent Mon-
itoring system has been designed. It is aimed to con-
tinuously analyzing and mining the data through 4-
dimensions: detection of emergencies, activity recog-
nition, event notifications, and summary extraction.
In order to cope with these objectives, the Intelli-
gent Monitoring system is composed of the follow-
ing modules: PP, the pre-processing module to encode
the data for the analysis; ED, the emergency detection
module to notify, for instance, in case of smoke and
gas leakage; AR, the activity recognition module to
identify the location, position, activity- and sleeping-
status of the user; EN, the event notification module to
inform when a new event has been detected; and SC,
the summary computation module to perform sum-
maries from the data.
The healthcare center receives notifications, sum-
maries, statistics, and general information belonging
to the users through a web application.
3 THE HIERARCHICAL
APPROACH
The SB-TMHSS described in the previous section is
aimed at recognizing activities and habits of a user
who lives alone. One of the requirements of the
implemented SB-TMHSS was to be cheap and no-
intrusive. In other words, we use the minimum num-
ber of sensors depending on the user’s home configu-
ration, avoiding camera or wearable sensors. In par-
ticular, we decided to not use a camera for privacy
reason and in accordance with the requirements com-
ing from the end-user of the proposed system. More-
over, the sensors are wireless and rely on wi-fi con-
nection to send data to the collector. Let us note that
we decided to do not adopt a wired solution because
is more expansive and intrusive. These requirements
imply that we have to take into account with errors
and noise coming from this configuration and to find
a solution to avoid them. In fact, sensors are not 100%
reliable: sometimes they loose events or detect them
several times. When sensors remain with a low bat-
tery charge they get worse. Moreover, also the Rasp-
berry pi may loose some data or the connection with
Internet and/or with the sensors. Also the Internet
connection may stop working or loose data. Finally,
without using a camera or wearable sensors we are
not able to directly recognize if the user is alone or
if s/he has some visits. Although, as said, a wireless
solution is not 100% reliable, .
Figure 2: An example of the sliding window approach,
where M means “motion event” and D means “door event”.
In order to solve this kind of limitations with the
final goal of improving the overall performance of
the SB-TMHSS, we propose an approach based on
machine learning techniques. In this initial solution,
we only consider motion and door sensors. The in-
telligent monitoring system continuously and concur-
rently listens for new data in a given window, accord-
ing to a sliding window approach (Datar et al., 2002).
For each window, data are pre-processed by the PP
and analyzed. As an example, let us consider the Fig-
ure 2 where once the current window recognizes a
door event at time tb, it looks for the previous one
in the window or before (in the example ta). Then,
the period from that door events (i.e, tb ta) is clas-
sified by the hierarchical classifier. Seemly, when the
event tc has been recognized, the period from tb and
tc is classified. Finally, the period from tc to the end
of the window is classified. In case of no door events
have been recognized, the period from ta to the end
of the window is classified.
The hierarchical classifier, depicted in Figure 3, is
composed of two levels. The upper is aimed at recog-
nizing if the user is at home or not, whereas the lower
is aimed at recognizing if the user is really alone or if
s/he received some visits.
The goal of the classifier at the upper level is to
improve performance of the door sensor. In fact, it
may happen that the sensor registers a status change
(from closed to open) even if the door has not been
opened. This implies that the SB-TMHSS may reg-
ister that the user is away and, in the meanwhile, ac-
tivities are detected at user’s home. On the contrary,
the SB-TMHSS may register that the user is at home
and, in the meanwhile, activities are not detected at
user’s home. To solve, or at least reduce, this prob-
lem, we built a supervised classifier able to recognize
ImprovingActivityMonitoringThroughaHierarchicalApproach
161
Figure 3: The hierarchical approach in the activity recogni-
tion module.
if the door sensor is working well or erroneous events
have been detected. First, we revise the data gathered
by the SB-TMHSS searching for anomalies, i.e.: (1)
the user is away and at home some events are detected
and (2) the user is at home and no events are detected.
Then, we validated those data by relying on Moves,
installed and running on the user smartphone. In fact,
Moves, among other functionality, is able to localize
the user. Hence, using Moves as an “oracle” we build
a dataset in which each entry is labeled depending on
the fact that the door sensor was right (label “1”) or
wrong (label “0”).
The goal of the classifier at the lower level is to
identify whether the user is alone or not. The input
data of this classifier are those that has been filtered
by the upper level, being recognized as positives. To
build this classifier, we rely on the novelty detection
approach (Markou and Singh, 2003) used when data
has few positive cases (i.e., anomalies) compared with
the negatives (i.e., regular cases); in case of skewed
data. Let us recall here that novelty detection is the
identification of new or unknown data that a machine
learning system has not been trained with and was not
previously aware of. In particular, we rely on the ap-
proach presented in (Sch
¨
olkopf et al., 2001) that tries
to estimate a function f that is positive on the dataset
and negative on the complement. The functional form
of f is given by a kernel expansion in terms of a poten-
tially small subset of the training data; it is regularized
by controlling the length of the weight vector in an as-
sociated feature space. The expansion coefficients are
found by solving a quadratic programming problem,
which we do by carrying out sequential optimization
over pairs of input patterns.
4 PRELIMINARY
EXPERIMENTAL RESULTS
The SB-TMHSS presented in this paper is part of
BackHome
4
, an European R&D project that aims to
provide telemonitoring and home support using Brain
Computer Interfaces (BCI) and other assistive tech-
nologies to improve autonomy and quality of life of
disabled people (Vargiu et al., 2012). The system is
currently running in five homes, two in Barcelona and
three in Belfast. In Barcelona installations have been
made at healthy-users’ home, whereas in Belfast at
BackHome end-user’s home (Edlinger et al., 2015).
To train and test the proposed approach, we con-
sider a window of 4 months for training and evalua-
tion (training dataset) and a window of 1 month for
the test (testing dataset). Experiments have been per-
formed at each level of the hierarchy. First, we per-
formed experiments to identify the best supervised
classifier to be used at the upper level of the hierar-
chy. Subsequently, we applied the novelty detection
algorithm on the data filtered by the classifier at the
upper level, to validate the classifier at the lower one.
Finally, we measure the performance of the overall
approach.
4.1 Is the User at Home?
First of all we build the training dataset with door
events (gathered by the door sensor) in a window of
4 months. Those data have been then validated by re-
lying on the information coming from Moves. The
entries are manually labeled in two classes Correct
data and User not at home according to the following
criteria:
Correct data: 0 if the data gathered from the door
sensor differs from the data gathered from Moves;
1 otherwise.
User not at home: 0 if the user is at home; 1 oth-
erwise.
4
http://www.backhome-fp7.eu/backhome/index.php
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Table 1: Results for the high-level classifier during the training (T) and evaluation (E) phase.
Classifier Parameter(s) Accuracy (T) Accuracy (E)
LR C = 0.005 0.945 ± 0.09 0.885
SVM γ = 0.01, C = 1.0 0.945 ± 0.09 0.885
SVM γ = 1.0, C = 0.452 0.853 ± 0.12 0.943
SVM γ = 0.05, C = 0.452 0.930 ±0.11 0.885
SVM γ = 0.01, C = 0.257 0.945 ±0.09 0.885
RF n estimators = 5 0.943 ± 0.09 0.942
RF max f eatures = 12 0.930 ± 0.09 0.942
AB n estimators = 15 0.918 ± 0.10 0.823
First, we define and implement a rule-based sys-
tem to verify if an approach based on rules may
help in improving the overall performance. The re-
sults coming from the rule-based system have been
then compared with those manually validated using
Moves. Unfortunately, results show a very few im-
provement with an accuracy of 77%. Thus, we decide
to implement a supervised classifier.
The data labeled as 1 for the class Correct data
have been used to extract the following features:
number of motion events divided by their dura-
tion, calculate after a door event (from t = i door
event to t = i + 1 door event) [Feature 1];
number of motion events divided by their dura-
tion, calculated before a door event (from t = i1
door event to t = i door event) [Feature 2];
number of motion events happened during minute
before a door event (t = i) [Feature 3];
number of motion events happened during the 2
minutes before a door event (t = i) [Feature 4];
number of motion events happened during the 5
minutes before a door event (t = i) [Feature 5];
number of motion events happened during minute
after a door event (t = i + 1) [Feature 6];
number of motion events happened during the 2
minutes after a door event (t = i + 1) [Feature 7];
number of motion events happened during the 5
minutes after a door event (t = i + 1) [Feature 8].
The dataset is then used to train four well-
representative and successful families of supervised
classifiers (Fern
´
andez-Delgado et al., 2014): a Lo-
gistic Regression (LR) classifier, a Support Vector
Machine (SVM), a Random-Forest (RF) and an Ad-
aBoost (AB).
The dataset has been divided in a training and
in an evaluation set and a 10-fold cross-validation
method has been used. The classifiers have been then
tested with an independent dataset. Table 1 shows re-
sults during the training phase and evaluation phase.
As shown, the best performance has been obtained by
relying on the SVM (with γ = 1.0 and C = 0.452), see
Figure 4, which shows different combination of the
parameters kernel coefficient (γ) and penalty parame-
ter of the error term (C).
The best classifier has been then used with the
testing dataset and, on average, it obtained a F
1
of
0.97 and an accuracy of 0.968. Finally, it showed an
improvement of 20% with respect to the rule-based
approach.
4.2 Is the User Alone?
The data filtered by the classifier at the upper level,
belonging to the monitored window of 4 months, are
the training dataset of the classifier at the lower level.
The corresponding dataset is composed by 57 normal
instances (i.e., the user was alone) and 8 anomalies
(i.e., the user received visits).
First of all, also in this case, we defined and imple-
mented a rule-based classifier to verify if a rule-based
approach could solve this problem. The adopted rules
together with the number of anomalies detected by
each one are the following:
Number of movement events every 60 seconds
greater than 6: 23 anomalies detected;
Number of movement events every 60 seconds
greater than 10: 1 anomaly detected;
Simultaneous movement events: 20 anomalies de-
tected;
Simultaneous movement events in less 2 seconds:
23 anomalies detected;
Maximum number of movement events in 60 sec-
onds greater than 6 or simultaneous moves in less
2 seconds: 28 anomalies detected;
Maximum number of movement events in 60 sec-
onds greater than 6 or simultaneous moves: 21
anomalies detected.
Since the rule-based approach is not able to cor-
rectly recognized anomalies, we use, also in this case,
an SVM classifier (one-class SVM with RBF, non lin-
ear). The following features have been considered:
ImprovingActivityMonitoringThroughaHierarchicalApproach
163
Figure 4: Cross-validation results of the different setting of parameters in the SVM classifier.
maximum number of motion events in intervals of
60 seconds [Feature 1];
maximum number of motion events in intervals of
120 seconds [Feature 2];
maximum number of motion events in intervals of
180 seconds [Feature 3];
number of motion events happened in a range of
5 seconds [Feature 4];
number of motion events happened in a range of
2 seconds [Feature 5];
number of motion events happened simultane-
ously [Feature 6];
minimum number of seconds between two con-
secutive motion events [Feature 7];
average of seconds between two consecutive mo-
tion events [Feature 8].
The classifier has been trained by considering the
normal instances and then evaluated introducing the
anomalies. Figure 5 shows the results obtained con-
sidering two features at time. In particular, for each
pair of features the frontier, the training observations
and the observations in case of normal instances (reg-
ular) or anomalies (abnormal) have been shown. Ta-
ble 2 shows the overall results .
Results during the evaluation phase show that the
system is able to correctly recognize all the anoma-
lies. According to the obtained results, we select the
classifier with the regularization parameter (ν) = 0.01
and γ = 0.1.
Similarly to the classifier at the upper level, the
system has been tested with the data coming from
the 1-month window of monitored events. Results
showed an average accuracy of 0.94.
4.3 Overall Results
Finally, we tested the performance of the overall hi-
erarchical approach. Once both classifiers have been
Table 2: Results for the classifier at the lower level. The
table reports the classification error calculated as the ratio
between the number of detected anomalies and the number
of instances in the dataset.
Parameter(s) Error (T)
ν = 0.01, γ = 0.1 0.0701
ν = 0.01, γ = 0.5 0.0877
ν = 0.01, γ = 1 0.1578
ν = 0.05, γ = 0.1 0.0877
ν = 0.05, γ = 0.5 0.0877
ν = 0.05, γ = 1 0.1403
ν = 0.1, γ = 0.1 0.1052
ν = 0.1, γ = 0.5 0.1052
ν = 0.1, γ = 1 0.1403
ν = 0.5, γ = 0.1 0.4912
ν = 0.5, γ = 0.5 0.5087
ν = 0.5, γ = 1 0.4912
trained, we tested the performance of the overall ap-
proach with the testing dataset corresponding to a
window of 1 month. We compared the overall results
with those obtained by using the rule-based approach
in both levels of the hierarchy. Results are shown
in Table 3 and point out that the proposed approach
outperforms the rule-based one with a significant im-
provement.
Table 3: Results of the overall hierarchical approach with
respect to the rule-based one.
Metric Rule-based Hierarchical Improv.
Accuracy 0.80 0.95 15%
Precision 0.68 0.94 26%
Recall 0.71 0.91 20%
F
1
0.69 0.92 23%
To highlight the performance of the proposed ap-
proach, let us consider the Figure 6 that shows a com-
parison between the real data, labeled during the val-
idation phase (on the left), and the data classified by
relying to the approach proposed in this paper (on the
right).
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Figure 5: Feature analysis of select novelty outlier method.
Figure 6: Comparison between real labeled data and data classified by the hierarchical approach.
Thanks to the proposed approach, the system is
able to recognize if the user is at home, away and/or
if s/he had some visits. An example of results in rec-
ognizing if the user is at home or away and if s/he
received some visits is given in Figure 7.
Figure 7: Example of results in recognizing if the user is at
home or away or if s/he received some visits.
5 RELATED WORK
There is a large literature on recognition of activities
at home (Van Kasteren et al., 2008) (Ye et al., 2012).
At the same time, we find a great variability in the
settings of the experiments either in the number of
sensors and their type, individuals involved or the du-
ration thereof. Also noteworthy is the large amount of
recognition techniques (supervised, either generative
or discriminative; and unsupervised). This diversity
makes it extremely difficult to compare performances
and draw conclusive findings from those studies. De-
spite this high variability, it is noteworthy to mention
that we have not found extensive studies that analyze
altogether the detection of visits, presence or absence
ImprovingActivityMonitoringThroughaHierarchicalApproach
165
of users at home using wireless binary sensors. Even
so; we report a number of different papers with differ-
ent approaches related to the partial detection of some
of the activities that concern us and with settings sim-
ilar to those in our work.
A former study (Tapia et al., 2004) already points
out some of the difficulties in discriminating daily
life activities based only on binary sensors activi-
ties. The automatic recognition system was based
on rules defined from the context and the duration
of the activities to identify. The data of the study
were obtained from 14 days of monitoring activities at
home. Although promising accuracies were achieved
for some activities, detection tasks such as ”leaving
home” were nothing less than satisfactory with 0.2 of
accuracy. This was because the activities were rep-
resented by rules directly defined on the firings out-
putted by single sensors (i.e. door switches); so they
did not contemplate that could be activated for other
reasons and in varying times, which made reduce their
discriminating power.
A more exhaustive work regarding the use of
switch and motion sensors for tracking people inside
home is found in (Wilson and Atkeson, 2005). Tests
were done with up to three simultaneous users. High
performances were reported by the trained tracking
models. However it is interesting to note that this
type of sensors experimented occasional lag between
“entering” a room and triggering a sensor; making to
decrease the performance of the tracking models.
In (Krishnan and Cook, 2014) a more complex
template learning model (SVM) was used to auto-
matically recognize among 11 different home activ-
ities. The proposed technique was integrated in dif-
ferent window sliding strategies (e.g. weighting sen-
sor events, dynamic window lengths, or two levels of
window lengths). They used 6 months of data from
3 different homes in which activities such as “enter-
ing” or “leaving home” were monitored. From the
best experimental settings the authors claimed accu-
racy for “entering” home about 0.80 of f1-score but
around 0.4 for “leaving” home tasks.
In a more extensive work (Cook, 2010) they use
Na
¨
ıve Bayes (NB), Hidden Markov (HMM) models
and Conditional Random Fields (CRF) for the ac-
tivity recognition problem. In this study, 7 smart
environments were used and 11 different data sets
were obtained. Several activities were attempted to
be recognized. Among others, we highlight “enter-
ing” and “leaving home” as relevant for our approach.
Although they did not report specific accuracies for
these activities, authors claimed an overall recogni-
tion performance on the combined dataset of 0.74 for
the NB classifier, 0.75 for the HMM model, and 0.72
for the CRF using 3-fold cross validation over the set
of annotated activities.
In (Ord
´
onez et al., 2013), authors proposed a hy-
brid approach to recognize ADLs from home envi-
ronments using a network of binary sensors. Among
the different activities recognized “leaving” was one
of them. The hybrid system proposed was composed
by using an SVM to estimate the emission proba-
bilities of an HMM. The results showed how the
combination of discriminative and generative models
is more accurate than either of the models on their
own. Among the different schemes evaluated, the
SVM/HMM hybrid approach obtains a significant 0.7
of f1-score a notable better performance than the rest
of approaches.
Detecting “multiple” people in single room by us-
ing binary sensors was already studied in an early
work (Wilson and Atkeson, 2004). In that work, au-
thors proposed a method based in Expectation Maxi-
mization Montecarlo algorithm. In a more recent ar-
ticle (Nait Aicha et al., 2013), high accuracy (0.85)
were reported on detecting visits at home using bi-
nary sensors. In that approach, they used an HMM al-
gorithm over the room events although not all rooms
of the home were monitored.
6 CONCLUSIONS AND FUTURE
WORK
Community based living, often alone with intermit-
tent care, creates possible scenarios of risks for all in-
dividuals. When cognitive changes are likely to have
taken place it is crucial to understand what the risks
may be and monitor these. To monitor users at home,
we develop a sensor-based system which is able to
gather data and report on the stability and evolution
of the user’s daily life activity. Unfortunately, per-
formance of sensor-based telemonitoring and home
support systems depends, among other issues, on the
reliability of the adopted sensors. It is particularly
true in the case of a wireless and binary sensors are
adopted. To solve this problem, we presented a hier-
archical approach, based on machine learning tech-
niques, aimed at improving the recognition of the
presence of the user at home and, being interested in
monitoring people that live alone, if the user is alone
or received some visits. The system has been devel-
oped under the umbrella of the BackHome project and
is currently running in 2-healthy-users’ home and in
3-end-users’ home. Results clearly show an high dis-
criminative performance improvement of 15% with
respect to a rule-based solution. Let us also stress the
fact that the proposed approach can be used as a pre-
ICT4AgeingWell2015-InternationalConferenceonInformationandCommunicationTechnologiesforAgeingWelland
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processing phase in every activity recognition system,
being completely independent from that.
As for the future work, we are currently setting-up
new experiments aimed at comparing the hierarchical
approach with a multi-class classifier and with an en-
semble (not hierarchical) of classifiers. Moreover, we
are improving the approach in order to be totally auto-
matic, by using data from Moves as feature instead of
to validate the initial dataset. We are also interested in
studying if we can generalize the proposed approach
to adopt it for all the user of the system, or if we have
to use a personalized approach for each different user
(or a small group of them).
ACKNOWLEDGEMENTS
The research leading to these results has received
funding from the European Community’s, Seventh
Framework Programme FP7/2007-2013, BackHome
project grant agreement n. 288566.
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