=Paper=
{{Paper
|id=Vol-2262/ekaw-poster-28
|storemode=property
|title=Towards Reconstruction of Human Trajectories in Indoor Environments
|pdfUrl=https://ceur-ws.org/Vol-2262/ekaw-poster-28.pdf
|volume=Vol-2262
|authors=Guillaume Bernard,Cyril Faucher,Karell Bertet
|dblpUrl=https://dblp.org/rec/conf/ekaw/BernardFB18
}}
==Towards Reconstruction of Human Trajectories in Indoor Environments==
https://ceur-ws.org/Vol-2262/ekaw-poster-28.pdf
Towards reconstruction of human trajectories in
indoor environments
Guillaume Bernard1 , Cyril Faucher1 , and Karell Bertet1
L3i laboratory, University of La Rochelle, France
cyril.faucher@univ-lr.fr
Abstract. In this paper, we propose to study how to reconstruct the
human trajectory in complex environments (indoor) such as museums
that are often not equipped with WIFI or other monitoring techniques.
We have set up a micro-localization infrastructure Bluetooth Low En-
ergy. We propose to use a sampling method for finding position stops
(point in a trajectory), because a deep learning method is not adapted
to this case.
Keywords: trajectory · micro-localization · sampling.
1 Introduction
The reconstruction of human trajectories in indoor environments is a major chal-
lenge for complex museums who want to know which are the most visited rooms
or how long visitors stay in front of a work. This work also aims to contribute
to the dynamic adaptation of scenarios such as interactions between a human
and a robot (Nao) by providing information like spatio-temporal sequences [4].
In outdoor, the reconstruction of trajectories is facilitated by the use of GPS,
but in indoor the GPS does not work and on the other hand most museums
do not have WIFI. We have endeavored to reconstruct human trajectories using
a device based on Bluetooth Low Energy (BLE) and more specifically the use
of the iBeacon protocol. In the rest of the paper, we present micro-localization
techniques for indoor environment before moving to the identification of visitor
stops. Then we discuss experimentation that allowed to validate the principle of
discretization by sampling. Finally we will conclude by giving some perspectives
to this work.
2 Micro-localization techniques for indoor environments
2.1 Bluetooth signal
We choose to use badges (credit card size) that have a BLE chip that emits
the iBeacon protocol. The people you want to follow are wearing this badge.
The iBeacon protocol makes it possible to recover the Bluetooth signal strength
(RSSI in dB) without the need for pairing between the signal transmitter and
the receiver [2, 3].
2 G. Bernard et al.
2.2 Technical architecture for collecting Bluetooth signal
Fig. 1 shows the architecture composed of collectors (Raspberry PI3 micro-
computer) that are positioned in a building. They detect in real time badges
nearby and record the power they capture (RSSI signal). The values are stored
in a MongoDB database (via FileBeats and Logstash). Data are analyzed in real
time by an algorithm in Python that calculates visitor stops and trajectories.
Fig. 1. Technical architecture for collecting Bluetooth signal.
3 Identification of visitor stops
Identifying visitor stops is the first step in rebuilding the trajectory of people.
So we set out to set up an algorithm for identifying stops based on a temporal
sampling (1s, 2s and 5s). The collector returning the highest average of the RSSI
collected on a sample is considered as a stopping position. A sub-sampling, with
the values of RSSI allows to extract a position in most cases. A first software was
created to propose positions by badge. Given the weaknesses of the localization
signal intensity [1], the solution generates intermediate positions, when there
is an ambiguity. For two signals picked up during a time undergoing the same
variations, with an almost identical power average, it is not possible to determine
whether the badge is closer to either of the collectors. Preliminary results suggest
that the value of the RSSI depends on many environmental parameters: humidity
levels in the air, movements of the human body, positioning relative to the
collection device and presence of aqueous medium between the beacon and the
Towards reconstruction of human trajectories in indoor environments 3
badges. In fact, the badge wearer can be one meter from the collector A, and
fifteen meters from the collector B. The identical intensities can be related to
the environment: the badge wearer is with someone who is positioned between
his badge and the collector, while nothing blocks the signal of the badge to the
collector B. The classification of the human position could have been based on
in-depth learning techniques and on-the-ground truth. These signal propagation
problems prevent us from using these AI methods.
4 Experimentation
We conducted experiments to collect positions and initiate experimental work
to determine a sampling value. The two figures below are from the same experi-
ment with two different sampling values. The sampling is done in soon after the
collection using an algorithm written in Python. Fig. 2 shows the result with a
sampling at 1 second and Fig. 3 at 5 seconds. All that has been done so far has
only one goal: to highlight the movements of visitors by locating their stops in
front of collection devices. A lot of visualization is achievable with Kibana. Only
the value of captured power (RSSI) by badge and by collection device interests
us: we simply want to know if in a temporal graph, these stops are remark-
able. One-second sampling shows the effect of noise on the signal: variations are
important.
Fig. 2. One-second sampling.
Despite significant variations in intensity received, stops in front of collection
devices are obvious. The intensity picked up near these sensors is higher than
elsewhere. It forms peaks over several seconds, where the intensity is higher.
These intensities apply for a single badge. By quickly analyzing this graph, we
can identify the path of the badge in front of the devices: firstly number 14 (gray),
then 11 (turquoise), 12 (red), 13 (light green), 16 (green), and 17 (orange).
4 G. Bernard et al.
After a five-second sampling, the impact of the variations is much smaller.
The stops are much better highlighted.
Fig. 3. Five-second sampling.
5 Conclusion and future works
To conclude we propose an infrastructure for collecting human position in in-
door environments. A technology based on Bluetooth Low Energy (iBeacon) was
chosen, this one is coupled with an ElasticSearch infrastructure. To determine
human position stops, we use a discretization method focusing mainly on the
average values collected for a temporal sampling. In future works, we would try-
ing to use a graph of possible location in order to improve our algorithm and
remove ambiguities.
References
1. A. Thaljaoui, T. Val, N. Nasri and D. Brulin, ”BLE localization using
RSSI measurements and iRingLA,” 2015 IEEE International Conference on In-
dustrial Technology (ICIT), Seville, 2015, pp. 2178-2183. https://doi.org/doi:
10.1109/ICIT.2015.7125418
2. X. Zhu and Y. Feng, ”RSSI-based Algorithm for Indoor Localization”, Com-
munications and Network, Vol. 5 No. 2B, 2013, pp. 37-42. https://doi.org/doi:
10.4236/cn.2013.52B007
3. Du, Jinze: Indoor localization techniques for wireless sensor networks, 2018, PhD,
University of Nantes
4. N. Juniarta, M. Couceiro, A. Napoli and C. Raı̈ssi, ”Sequential Pattern Mining
using FCA and Pattern Structures for Analyzing Visitor Trajectories in a Museum”,
Proceedings of the Fourteenth International Conference on Concept Lattices and
Their Applications, Olomouc, Czech Republic, June 12-14, 2018, pp. 231-242.