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 Energy. We propose to use a sampling method for nding position stops (point in a trajectory), because a deep learning method is not adapted to this case.
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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 [
Identifying visitor stops is the rst 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 rst software was
created to propose positions by badge. Given the weaknesses of the localization
signal intensity [
We conducted experiments to collect positions and initiate experimental work to determine a sampling value. The two gures below are from the same experiment with two di erent 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 remarkable. One-second sampling shows the e ect of noise on the signal: variations are important.
Despite signi cant 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: rstly number 14 (gray), then 11 (turquoise), 12 (red), 13 (light green), 16 (green), and 17 (orange).
After a ve-second sampling, the impact of the variations is much smaller. The stops are much better highlighted. 5
To conclude we propose an infrastructure for collecting human position in indoor 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 trying to use a graph of possible location in order to improve our algorithm and remove ambiguities.