Maps for Easy Paths (MEP): Accessible Paths Tracking and Reconstruction

Abstract

MEP (Maps for Easy Paths) is a project for the enrichment of geographical maps with information about accessibility of urban pedestrian pathways, targeted at people with mobility problems. In this paper, we describe the tools developed to collect data along the paths travelled by target people and the algorithms for a good quality reconstruction of the path developed to overcome the intrinsic limitation of the sensors available on mobile devices. Experimental results show the feasibility of the approach.

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Maps for Easy Paths (MEP): Accessible Paths Tracking
and Reconstruction
S. Comai1,*, E. De Bernardi1, M. Matteucci1, F. Salice1
1Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano – Piazza L. da Vinci 32, 20133 Milano,
Italy
Abstract
MEP (Maps for Easy Paths) is a project for the enrichment of geographical maps with information about accessibility of
urban pedestrian pathways, targeted at people with mobility problems. In this paper, we describe the tools developed to
collect data along the paths travelled by target people and the algorithms for a good quality reconstruction of the path
developed to overcome the intrinsic limitation of the sensors available on mobile devices. Experimental results show the
feasibility of the approach.
Keywords: City accessibility, path reconstruction, motor impairments, mobile application.
Received on 28 September 2016, accepted on 07 December 2016, published on 31 January 2017
Copyright © 2017 S. Comai et al., licensed to EAI. This is an open access article distributed under the terms of the Creative
Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and
reproduction in any medium so long as the original work is properly cited.
doi: 10.4108/eai.31-8-2017.153050
1. Introduction
According to World Health Organization, about 15% of the
world’s population has some form of disability and
traveling through cities is one of the main concerns for
people with mobility impairments [19]. Some help could
come from an adaptive navigating system capable of
considering their needs and taking into account the
(mapped) accessibility of urban routes. Nevertheless,
mapping accessible paths in a sustainable way is still an
open challenge. Indeed, the most cumbersome activity in
providing a map enriched with accessibility information is
gathering such information through field surveys, typically
done manually by users or volunteers.
Maps for Easy Paths (MEP) is an ongoing project [11]
aiming to overcome the limitations of current collaborative
approaches in mapping accessible routes by easing the
surveying effort through the collection of motion data from
sensors commonly available in mobile devices. The
accessibility of city routes, e.g., sidewalks, walkways, etc.,
is defined through the active contribution and participation
*Corresponding author. Email: sara.comai@polimi.it
of target users, which include people with permanent or
temporary motor disabilities and, possibly, active citizens.
To ease target users and volunteers in data collection, we
developed a set of tools to track, and automatically
reconstruct, paths travelled by target users. In particular,
we developed a mobile application called MEP Traces that
automatically stores mobile sensors data such as position
estimates from GNSS satellites (Global Navigation
Satellite System) and motion data coming from
accelerometers, magnetometers, and gyroscopes. When
users travel through the city, they just need to start the app
at the beginning of their journey and stop it when they
arrive. The underlying idea is that the route travelled by a
person with some sort of disability can be considered
accessible also for other persons having the same (or a
lower) type of disability. More in general, we assume that
a path taken mostly by people with disabilities can be
perceived as a friendlier route; this allows us to
automatically identify accessible paths without the need of
an ad-hoc field survey simply because the traveller who
captures the data has been register to have some specific
sort of disability.
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Once sensors data of a route have been collected, they are
processed by means of different algorithms: sensor fusion
techniques improve the sparseness of GNSS data; mapping
on cartography can improve the quality of the paths;
clustering techniques can merge different traces over the
same route.
After reporting related works in Section 2, in Section 3
we provide an overview of the MEP project: in particular,
we describe the MEP Traces application and the overall
process to extract the accessible paths. In Section 4
experimental results of a survey done in Cernobbio (Como,
Italy) are reported, while in Section 5 we draw our
conclusions and outline future plans.
2. Related work
Several collaborative projects proposed in the literature
aim to improve city accessibility, through the Web or, more
recently, through smartphones/tablets applications, as
surveyed in [5]. Different types of barriers, but also of
facilitators, have been identified and classified in several
studies [10][12], such works are at the basis of our analysis
for the collection of data about accessibility of city
pedestrian pathways.
Considering the status of Web/Android/iOS applications
available to the public, almost all of them focus on
accessibility of points of interest (e.g., museums,
restaurants, etc.). Among them, Wheelmap [20] is a map
for finding and marking wheelchair accessible places of
daily life, based on Open Street Map.
Only some of available apps include also information
about condition of sidewalks and pedestrian crosswalks, or
about the presence of cobblestones, curb ramps, and street
lighting, such as RotaAccesivel [17], Comuni per tutti [6]
and Mapability [9]. These proposals are very general and
try to address all the disabilities. However, the collection
of data is quite heavy, being mainly manual.
In the literature, solutions for the identification of
accessible paths and sidewalk conditions have been
considered only by few approaches, like, e.g.,
[4][8][14][18]. Cardonha et al. [4] adopted an approach, in
part, similar to MEP: the Breadcrumb application was
developed to periodically capture a sequence of
measurements based on the device geo-location (i.e.,
longitude and latitude) without any need for user
intervention. To enhance the quality of the collected data,
Breadcrumb applies a simple moving average of the last 10
estimates of the velocity of the device to identify
slowdowns as obstacles. Compared to Breadcrumb, in our
approach we try to extract as much as information as
possible from the available sensors fusing the GNSS
(Global Navigation Satellite System, at present GPS and
GLONASS) with the inertial data in order to reconstruct
the exact path of the user, supposed to be accessible as
explained in Section 3.
Karimi et al. [8] propose a routing module which tracks
the shuttles available in the main campus of the University
of Pittsburgh and, given an accessibility map built
manually, they provide turn-by-turn directions
distinguishing among sidewalks along a street, along a
building, and crosswalks along a building. Also [14]
collects GPS data to determine the users’ trajectories and
provides an algorithm to determine an accessible path
between two locations for users with a certain disability:
however, to the best of our knowledge, only a prototype has
been produced. Finally, also the authors in [18] consider
sidewalks, by providing a mobile application to capture
pictures and upload data about some observable aspects of
sidewalk conditions such as holes, presence of steps, etc.
3. MEP Traces and Path Reconstruction
In the MEP project we adopted a user-centered design
approach involving target users from the early phases of
the project being them the main actors of the data collection
besides being the beneficiaries of the collected data.
3.1. Requirements of the Application
Users’ requirements were collected with focus groups
involving both manual and electric wheelchair users, as
well as elderly people with mobility issues. The main
requirements that emerged from the focus groups include
easiness in using the app, interactive interfaces, easy to
click and to understand icons and interactive buttons. With
respect to this last point, some of the wheelchair users of
the focus group had finger movement limitations, for
example when performing zoom in and out or in typing
with digital keyboards: simple single click commands are
therefore required.
Regarding the information to be collected along the
pathways, and therefore to display on the map, they
highlighted that they would prefer an app telling them the
accessible paths to follow, and that they would not like to
hear about obstacles. Among possible accessible elements,
they are interested in accessible toilets, transportation
stops, and parking lots, as well as any building or point of
interest of the city. In this project, we have mainly focused
on the paths and on the algorithms for their reconstruction;
however, our tools include the possibility to notify and
consider obstacles and accessible elements.
In case of obstacles, it should be possible to signal them
together with pictures that may give a better idea of the
obstacle for the specific disability; simple and not-long-to-
fill obstacles’ evaluation forms should be offered by the
application. Finally, personalized maps according to
typical disabilities (e.g., manual vs. electric wheelchair)
should be provided; the collected data should therefore take
into account also the users’ characteristics, so that, for
example, a path travelled by a user requiring step-free
accessibility can be considered accessible also for users
able to climb low curbs.
The whole mapping process should take into account the
different kinds of users: not only the interface should be
suitable for users with motor impairments, but also proc-
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Figure 1. Overview of the process for data collection and processing
essing algorithms should take into account mobility
problems: in particular, when using sensor fusion
techniques, they should be suitable for data collected on
wheelchairs and cannot exploit step detection or similar
techniques to improve the reconstruction of the path.
3.2. Data Collection and Processing
Overview
Figure 1 describes the process for the collection of paths
data and for their reconstruction; when a user starts a route,
s/he activates a mobile app called MEP-Traces to collect
along the whole path data needed to its reconstruction.
Such data include GNSS positions estimates, motion
sensors data (e.g., accelerometer, gyroscope, etc.) and,
possibly, images; all the data are stored on the device SD-
card and then uploaded on the server in a PostGIS spatial
database [16] for further processing. On the server, since
the accuracy in positioning of GNSS data is quite low for
mobile device GNSS receivers, we fuse GNSS positions
with motion data to provide a better estimate of the path,
especially in those parts of the route where GNSS satellites
are not visible. The output is a path, which is further
corrected exploiting the cartography and possibly merged
with other paths on the same route, and is positioned in a
geographical map. All the collected data are displayed in a
different application, called MEP-App, for the target users.
Besides collecting sensors’ data along the path in an
implicit way, without the intervention of the user, both
MEP-Traces and MEP-App allow also the notification of
explicit data. In particular, it is
possible to notify (geolocalized) obstacles met along the
path. Moreover, it is possible to enrich maps also with
accessible elements (e.g., parking lots for disabled people,
accessible transport, accessible entrances and presence of
elevators, etc.). The users must explicitly signal such data.
3.3. MEP-Traces Application
MEP-Traces is the application for the collection of data
from common hardware sensors like GPS, accelerometer,
magnetometer, gyroscope, and barometer, embedded in the
current generation of smartphones and tablets. Data are
collected simultaneously, at the highest possible frequency,
and locally stored in the mobile device SD-card.
Figure 2 shows some snapshots of our Android
prototype: after registration, it provides a simple menu to
start the recording of the route, manage user’s profile, send
collected data, and exit the application (Figure 2.a). The
main task of MEP-Traces is to track the user with motor
impairments while s/he is travelling, with the idea of
mapping only accessible paths. Some information, like
available memory, and battery level can also be checked
(Figure 2.b). This is used to warn the user when critical
levels are reached, and to promptly save the acquisitions
not to miss important data for processing. Obstacles, as
well as accessible elements, can be notified with a simple
click among predefined obstacle types (Figure 2.c); then,
some characteristics, like the type (temporary or
permanent) and the criticality level (low/accessible with
some help, medium, high/not accessible at all), can be
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specified (Figure 2.d). Optionally, some pictures and a
description can be included. In a similar way, also
accessible elements are notified.
a) b)
c) d)
(c) (d)
Figure 2. Some snapshots of the MEP Traces app
(a) main menu, (b) sensor recording, (c) obstacle
type selection, (d) obstacle description and
notification.
The application has been developed to dynamically
recognize all the motion sensors in the device (e.g., step
detector, orientation, proximity, rotation vector, etc.), but
only accelerometer, gyroscope and magnetic field sensors
are acquired by default. To retrieve the device position, the
GPS sensor is used. The application automatically starts the
sensor monitoring as soon as the GPS geo-location is
obtained. For each acquisition phase, a specific folder is
created, to store the files containing all the information of
the sensors changes during the movement. Collected data
need to be explicitly sent by the user to the server for
further processing and sharing. Before sending them, we
minimize the upload effort by compressing each
acquisition folder. The upload operation is forced to
happen with a connection between the device and our
server over WiFi using the SFTP (SSH File Transfer
Protocol), as in Figure 3. During this task, the acquisitions
uploaded correctly to the server (after a client/server check)
are automatically deleted from the mobile device while the
upload proceeds.
a)
b)
Figure 3. MEP-Traces upload interface example: (a)
automatic selection of all the acquisitions, (b) data
compression and connection/uploading task
3.4. System Architecture
The back-end of the application exploits Policloud [15], the
cloud infrastructure of Politecnico di Milano.
The back-end server begins a multithreading elaboration
as soon as each single acquisition (paths, obstacles) is
completely uploaded and available. An articulated
workflow precisely defines the intervention’s sequence to
build accessible paths over the map.
Figure 4 shows the MVC (Model-View-Controller)
block schema.
Figure 4. The MVC design pattern general block
schema.
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The Model represents the core element: it contains the
whole knowledge, i.e., the logic, data, state and rules of the
application. The View builds a visual representation of the
Model, while the Controller operates as a link between
them.
The main service on the server starts by creating a
Controller, responsible of the workflow management and
aiming at building an accessible path. In particular, it is
composed of the following three main elements:
1. An Observer element, having the goal of
discovering new elements and adding them to the
data set.
2. A Cluster element, performing the clustering of
many acquired data sets, and aiming at
representing sets of paths on the same route as a
single accessible path.
3. One or many Worker element/s, managing the
sequential/parallel multithreading process of the
main workflow, to finalize the insertion of the
reconstructed accessible path into the PostGIS
database.
The first two processes run over two or more separate
threads (depending on the CPU architecture); the third
process is activated when there is at least one task, i.e., new
data have been uploaded.
Initially, the Observer monitors the main folder where
data are uploaded, providing an immediate response if a
new acquisition has to be processed. The compressed
acquisition files are passed to a Worker that begins the
elaboration process. First, a decompression procedure is
applied to the data, as described in Figure 5. For each
acquisition file, the system checks every two seconds if the
uploaded file size changes; if it changes, it waits until the
upload is completed. Then, a corruption check is
performed. The decompression procedure extracts all the
files that contain relevant information of sensor data for the
path acquired with MEP-Traces.
Figure 5. Decompression procedure.
After the decompression phase, the main workflow
(graphically depicted in Figure 6) starts. Given a path
composed of a sequence of GPS points, the first task
computes the geometry of each GPS point and inserts it in
a specific table into PostGIS, together with other
parameters such as: the device id, the email account and the
name of the user, the timestamp of the acquisition, the UTC
(Universal Time Coordinated), city, latitude and longitude.
Then, GPS points are fused with motion data such as
accelerometer, magnetometer, etc. provided by the device
sensors to improve path reconstruction quality; motion data
are correlated with GPS data by means of timestamps (this
process is detailed in Section 3.5). The output geometry of
the MEP-Fusion algorithm is a sequence of corrected
points. The last step of the Worker implements a
cartographic correction of the fused GPS data (explained in
detail in Section 3.6).
Figure 6. Worker general process task.
The three steps represent the pre-processing stage for the
MEP-Clustering algorithm task (described in Section 3.7).
Each GPS point of the cartographic correction is stored in
the database in order to keep track of the cities already
clustered. The clustering process of a specific city is
repeated every time new points affecting the cluster are
uploaded.
All the algorithms run on our server as a 24/7 service in
a remote Unix Machine with 4GB of RAM, and a dual core
CPU (4 parallel threads). The main Worker can elaborate
in parallel 4 paths.
3.5. Path Reconstruction
Data collected with the MEP-Traces application are used
by the MEP-Fusion engine to reconstruct the path followed
by MEP-Traces users. The approach used in the
reconstruction is based on the fusion of information
coming from multiple sensors to overcome issues related
to the poor quality of the mobile sensors [13]: indeed, the
GPS and the internal Inertial Measurement Unit of the
mobile device could single-handedly provide an absolute
position and orientation for the device, but measurement
noise produces inaccurate results.
Being the application targeted to users with disabilities,
including those with motor impairments, methods often
used to track pedestrian movements using mobile devices,
which are based on step detection, are ineffective. For this
reason, our solution is based on (and extends) the
ROAMFREE sensor fusion library [7]. ROAMFREE,
which stands for Robust Odometry Applying Multisensor
Fusion to Reduce Estimation Errors, is a framework
developed at the Artificial Intelligence and Robotics lab of
Politecnico di Milano originally designed to fuse
measurements coming from an arbitrary number of
sensors, including images, in order to determinate the
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position and orientation of a mobile robot. Details of the
approach can be found in [1]. Since the ROAMFREE
library is able to reconstruct the trajectory using the
absolute reference frame of the GNSS and the orientation
provided by the Earth magnetic field recorded by the
magnetometer, the device during the route can be held
freely by the user. However, a swinging device, produces
less accurate results. Experimental results show that
accuracy depends on GPS accuracy, since the other sensors
provide relative positions; instead, the approach improves
the path reconstruction in case of missing or sparse GPS
measurements [1].
3.6. Path Correction
Even though the MEP-Fusion engine operates to clean GPS
trajectories by fusing mobile sensors data, the
reconstructed paths can still present some intrinsic errors
when the reconstructed path is mapped on a cartographic
map: in many cases, paths cross buildings. Fusing each
single point corrected by MEP-Fusion algorithm with static
information contained into the OSM (OpenStreetMap)
database can lead to interesting improvements for this kind
of problem.
The Global Navigation Satellite System (GNSS)
trajectory-building superposition correction developed at
the Environmental and Civil Engineering Department at
Politecnico di Milano [3][13], employs a real time
triggering technique over our GPS points table. A
dedicated trigger is activated whenever new points are
inserted into the database, performing a simple check of the
points coordinates’ superposition with respect to the
coordinates of the buildings around it. If the trigger detects
the occurrence of a superposition, the point rigidly projects
outside the building’s area considering the closest “road
pipe” as reference.
Figure 7. The “bring-outside” correction.
Figure 7 shows the initial situation where the trigger
detects a new measured point overlying the building. A
number of possible candidates on the border of the building
are identified. The corrected point is associated to the one
that reflects the closed road pipe to which it belongs in the
original path. Applied to the whole path, this method
produces the results shown in Figure 8.
a)
b)
Figure 8. Cartographic correction of GNSS
trajectories
All the points occurring over a building are rigidly shifted
to its border. The original measured points can even occur
over large portions of buildings, due to large errors in the
surveys and there may be critical situations that can lead to
a completely unwanted result: for example, in Figure 8 (b)
the obtained path is discontinuous. To solve such problems,
in case of discontinuity, it is possible to shift all the points
to the closest sidewalk instead of to the border of the
building.
3.7. Paths Clustering
As explained in Section 3.4, the Worker element performs
all the pipeline tasks from the decompression phase of the
acquisition, passing through the MEP-Fusion algorithm
and the geographic correction of the GNSS trajectories.
Each process tries to perform a path reconstruction as
reliable as possible with respect to the original user walk.
When many acquisitions are stored on the database, the
paths visualization of the same road is represented as
overlapped trajectories, as shown in Figure 9: the
sequences of green points represent different paths
performed by different people. The main idea of the paths
clustering is to generate a set of nodes, belonging to a
connected graph, obtained from the learning of the
trajectories points (in Figure 9 they are represented by blue
points). As a result, a single trajectory can be extracted
clustering different paths sampled from a single accessible
route.
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Figure 9. Neurons computation from trajectories.
The MEP-Clustering engine implements the Adaptive
Incremental Growing Neural Gas Network algorithm [2]
with slightly modifications in order to adapt it to the
geospatial dataset. Indeed, despites Neural Gas Network
approach represents a very effective method to perform
topology learning, it lacks an automatic procedure to detect
an a priori suitable number of neurons and adaptation steps,
requiring a human intervention.
The Growing Neural Gas Network approach adds new
units over time starting from a small network; then a local
statistical measure (obtained throughout the adaptation
step) can be evaluated. Using the computed statistical
measure, the network topology can be generated
incrementally adopting the competitive Hebbian learning
method [2]. The final dimension of the network depends on
the local statistical behaviour of the input.
Topology learning process starts by placing randomly
two units into the space that has to be learned.
Successively, an input signal, i.e., a sample from a path, is
generated according to its probability distribution.
According to that signal, the two closest units can be
identified. Each unit contains a local counter, responsible
for the tracking of the distance between the inputs and the
unit itself. At each step, the local counter of the closest unit
is updated by incrementing the aging of all emanated edges.
Each edge in the Growing Neural Gas has an associated
age used to remove the old ones in order to keep the
topology dynamically updated. With this method, while the
decision of introducing a new neuron is taken upon a fixed
parameter, such decision is computed upon an adaptive free
distance threshold. An excessive growth of neuron
numbers is avoided by considering the use of a
probabilistic criterion; in such a way, a new topology arises
to preserve memory constraints.
Be better understand the whole process, let us consider a
simple case where a new input point has to be processed to
generate (or update) a previous neuron. Three different
cases (see Figure 10) perfectly relates to the three possible
situations of new neurons creation:
1. The input point is far enough from n1: a new
neuron joins the graph; the sample’s position and
a sample node join locally to the new neuron
sample’s list. Considering the new sample the
threshold of the new node is updated.
2. The input point is close enough to n1 but far
enough from n2: a new neuron joins the graph at
the sample’s position and a new link connects
node n1 with the newly created node. The sample
point gets into the new neuron sample’s list and
the threshold of the node n1 is updated.
3. The input point is close to both n1 and n2: then,
move n1 and its neighboring neurons
toward the input point;
increase the age of n1’s emanating edges;
link n1 and n2 with a new edge whose age
is equal to 0;
remove old edges from the graph if they
exist;
update the threshold of both n1 and n2
accordingly.
When the number of neurons increases above a
maximum nodes threshold, a merging process tries to
reduce the dimension of the graph by fusing some elements
together. The merging process starts by creating a new
empty graph. Two randomly picked nodes get out from the
node list of the old graph and join to the new graph. These
two nodes correspond to the initialization vector supplied
as input of the Adaptive Incremental Growing Neural Gas
Network algorithm to accomplish the final goal.
Figure 10. Green square points correspond to the
three possible cases representing the creation of a
new neuron or the assignment to an existing one of
the input point. The black square points are the
samples assigned to a neuron node (the blue one).
4. Experimental results
The experimental activity done in Cernobbio (Como, Italy)
consisted in two days of acquisition, with MEP-Traces
installed on different Android smartphones and tablets
(used devices are listed in Table 1). All the collected GPS
data are graphically shown in Figure 11.
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Figure 11. GPS data collected in Cernobbio (Como,
Italy) with MEP Traces.
To evaluate the quality of the data acquired from mobile
devices, the collected GPS data have been compared with
the data of a high-cost geodetic device – in our case a Leica
GPS 1200 receiver. Figure 12 shows the acquisitions for
the same path of the geodetic device (in black) and the low
cost GNSS receiver of a Google Nexus 6P GPS sensor (in
purple); it can be noticed how it is affected by a lot of noise.
To provide a better estimation of the correct path, we have
fused our GPS data with the motion sensors data.
Figure 12. Details of two different acquisitions with
a geodetic device – Leica GPS 1200 receiver (black
route) and a low cost GNSS receiver - Nexus 6P
(purple route).
Figure 13 shows the application of the cartographic
correction of GNSS trajectories on the data collected in
Cernobbio. In blue and yellow lines represent, respectively,
roads and building borders downloaded from the OSM
database. It is possible to notice how data points over
buildings have been successfully shifted at their borders.
Figure 14 (a) visualizes the result of the MEP-Clustering
algorithm described in Section 3.6. The yellow points
indicate the neurons generated by the algorithm. The union
of the neurons forms a single trajectory. The more a
centroid is colored in yellow, the more the neuron owns
points grouped in its cluster. Figure 14 (b) shows the
neurons over the map of Cernobbio.
The 3D representation of the neurons in Figure 15 shows
a Gaussian representation of the clustering result where the
mean and variance of the data points at each single neuron
is computed and plotted.
a)
b)
Figure 13. Cartographic correction of GNSS
trajectories. From left to right (a) raw dataset of
Cernobbio, (b) cartographic GNSS correction.
a)
b)
Figure 14. a) MEP-Clustering algorithm result and b)
cluster visualization in Cernobbio
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Figure 15. Gaussian 3D visualization of neurons
using MEP-Clustering algorithm.
The average battery consumption of MEP-Traces has also
been computed for each device. The application is designed
to run in background trying to use the minimal Android
system resources, allowing the user to do any other task
(e.g., calls, receiving SMS and emails, using the internet,
etc.). Table 1 shows the total acquisition time and the total
length of the walked path (expressed in meters).
Table 1. Average battery consumption for each
device in using MEP-Traces during the experiments
in Cernobbio (Como, Italy) with total acquisition time
and the total length (in meters).
Android device
Acquisition
time
Walk
[m]
Average
1h battery
consumption
Samsung GT-
I9505
1h 13' 54''
3792
3%
Motorola
XT1092
2h 07' 54''
6312
16%
Huawei MT7-
TL10
4h 43' 42''
14314
7%
Nexus 5
42' 31''
1862
3%
Motorola
XT1092
2h 10' 47''
6730
5%
Nexus 7 2013
20' 55''
971
5%
Nexus 7 2013
1h 33' 20''
1070
24%
Nexus 7 2012
06' 08''
152
2%
Samsung GT-
I9070
2h 22' 45''
3796
15%
Nexus 5
1h 16' 06''
4074
5%
16h 38' 02''
43073
A total acquisition time of 16h 38' 02'' was done, reaching
about 43 Km as the total length of the walked path. The
battery consumption of MEP-Traces has also been
computed considering one hour of acquisition: the battery
consumption of the application is about 5-6% in one hour
varying among the different devices. For some devices
(omitted from our computation) we have used power
banks; in such cases the battery consumption was 0%.
All the acquisitions were taken with the application
running in background. In order to consider a common user
in a daily device usage, mobile data connection was
enabled. Most of the used devices were personal devices,
therefore the consumption could have been affected also by
other applications running on them. Several factors may
affect the results of the battery consumption and for
personal devices it is difficult to have homogeneous
conditions. Indeed, results may depend also from the
Android OS version installed on the device, the Linux
Kernel version and its optimization, the hardware device
composition like CPU and RAM, the read/write SD-card
speed, etc. However, since the applications are thought to
be used by any user with any Android device, these data
can be considered as approximations of possible
behaviours.
5. Conclusions and Future Work
In this paper we have described the results of data
acquisitions done in Cernobbio (Como, Italy) for the MEP
(Maps for Easy Path) project. The tools developed for the
project have been illustrated and in particular the app MEP-
Traces has been described in more detail: it retrieves raw
GNSS data from low cost GPS sensor installed on
commercial mobile devices, together with other sensor data
like accelerometer, magnetometer etc. Then the entire path
is reconstructed. Each reconstructed path is associated with
the user’s profile (e.g., wheelchair type, requirements like
“no-step”, etc.), to build accessible paths for different
users’ types. Consistency and reliability of the collected
data can be increased if more users trace the same routes.
At this aim, we are improving the path reconstruction using
clustering techniques on a set of paths. The visualization of
the collected data with the cartographic correction of the
GNSS and the MEP-Clustering algorithm applied on our
collected dataset have also been discussed.
Experiments have shown that the MEP-Traces
application performance running in background on
different devices is good, with a battery consumption of
about 5-6% for an hour of acquisition.
Future works of the project include the MEP-Fusion
optimization on our main real time computing service and
a heatmap visualization of the paths clustering fusing also
reported obstacles. This last step aims to compute in real
time the visualization of the accessibility level, by
colouring from red to green the clustered neurons
belonging to a path when a user notifies an obstacle during
his/her traveling in the city.
Acknowledgements. This research is funded by the
Polisocial Award program, granted by Politecnico di Milano,
Italy. We wish to thank the whole MEP team and the Policloud
initiative for the cloud infrastructure.
Maps for Easy Paths (MEP): Accessible Paths Tracking and Reconstruction
EAI Endorsed Transactions on
Internet of Things
10 2016 - 01 2017 | Volume 3 | Issue 9 | e3

S. Comai et al.
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EAI Endorsed Transactions on
Internet of Things
10 2016 - 01 2017 | Volume 3 | Issue 9 | e3