Technology as a tool to study visitor behaviour in museums: positioning and neuropsychological detection to identify physical & cognitive barriers

Inclusive communication projects in museums and cultural sites often start from generically applicable assumptions referring to the principles of accessible and inclusive design, without considering the peculiarities of a cultural experience. It therefore seems important to study the audiences’ behaviour in museums, with particular attention to the different types of visitors: regular audiences with appropriate backgrounds; occasional audiences with very different backgrounds; and disaffected audiences who do not consider cultural experiences important or rewarding. It is precisely the latter that an inclusive design must carefully target, with the aim of understanding the reason for this exclusion and thus overcoming it, hence it is important that such studies do not observe only the first two types of audience, whereas this is often the case. In this context, precise positioning is mandatory: in case of museums, it is necessary to determine users’ location at every epoch, with high sampling rate, to monitor movements, times and stops of the public within the museum, in relation to the exhibits, the spatial features of the rooms, and the communication and display solutions, relating them to information resulting from ad hoc surveys. From the positioning point of view, one of the main problems is represented in tracking people in indoor environments, where the GNSS is not available, and there are often cramped spaces. Besides, if the number of people to be tracked is high, the level of difficulties increases dramatically. The problem of positioning even large numbers of people within closed and delimited spaces presents some difficulties and technical criticalities. On the other hand, the restitution of such data requires accentuated reliability: the behaviour and reactions recorded in the public during the experiment must be related to precise spatial positions, since the emotional responses of the public can vary in a very short time. At present, the research group is studying and implementing new technologies available in mobile devices, such as Ultra Wide Band (UWB) technology, to study individual visiting experiences. The technological challenge in these contexts goes beyond mere technical effectiveness. Indeed, the instrumentation required to track individual visitors, in certain solutions, risks influencing people’s behaviour because it is moderately ostrusive: conversely, the challenge at present is to integrate the various sensing devices into compact and unobtrusive soluti- ons. The Authors have implemented a Python code on a portable Raspberry device that guarantees the users’ location by exploiting signals coming from beacon devices. Communication systems between the device detecting neurophysiological reactions and monitoring physical movements can be implemented and optimised, fusing this technology with another one related to positioning purposes, exploiting electromagnetic signals such as ultra-wide-band technologies or Bluetooth, which guarantees the possibility of reaching positioning solutions even in indoor environments without afflicting the signals for neurophysiological parameter estimations

Influence of measured radio map interpolation on indoor positioning algorithms

Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.- (undefined

Modeling WLAN Received Signal Strengths Using Gaussian Process Regression on the Sodindoorloc Dataset

While any wireless technology can be used for indoor localization purposes, WLANhas the advantage of having a huge existing infrastructure. A radio map that matches specific locations to received signal strength is needed, to enable most of these indoor localization methods. To create these radio maps, with enough detail to achieve sufficient localization accuracy, is expensive and time consuming. Therefore, methods to interpolate and extrapolate more detailed maps from sparse radio maps are being developed. One recent approach is to use Gaussian process regression. Even though some papers already studied Gaussian process regression, most studied only the basic model with zero mean and squared exponential kernel. In addition, when the model fit was evaluated in more detail, the experimental area was of limited complexity. Hence, this thesis evaluates the fit of Gaussian process regression, in a more complex indoor environment, based on adequate model metrics and analysis of the plots of the predicted mean and standard deviation functions. As a conclusion, the most suitable model is presented, as well as the reasoning why it was chosen

A WiFi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm

The advances in WiFi technology have encouraged the development of numerous indoor positioning systems. However, their performance varies significantly across different indoor environments, making it challenging in identifying the most suitable system for all scenarios. To address this challenge, we propose an algorithm that dynamically selects the most optimal WiFi positioning model for each location. Our algorithm employs a Machine Learning weighted model selection algorithm, trained on raw WiFi RSS, raw WiFi RTT data, statistical RSS & RTT measures, and Access Point line-of-sight information. We tested our algorithm in four complex indoor environments, and compared its performance to traditional WiFi indoor positioning models and state-of-the-art stacking models, demonstrating an improvement of up to 1.8 meters on average

Real-time Wi-Fi network performance evaluation

The most critical parameters that indicate the Wi-Fi network are throughput, delay, latency, and packet loss since they provide significant benefits, especially to the end-user. This research aims to investigate Wi-Fi performance in an indoor environment for light-of-sight (LOS) and nonlight-of-sight (NLOS) conditions. The effect of the surrounding obstacles and distance has also been reported in the paper. The parameters measured are packet loss, the packet sent, the packet received, throughput, and latency. Site measurement is done to obtain real-time and optimum results. The measured parameters are then validated using the EMCO ping monitor 8 software. The comparison results between the measurement and the simulation are well presented in this paper. Additionally, the measurement distance is done up to 30 meters and the results are reported in the paper as well. The results indicate that the throughput value decreases with an increasing distance, where the lowest throughput value is 24.64 Mbps and the highest throughput value is 70.83 Mbps. Next, the maximum latency value from the measurement is 79 ms, while the lowest latency value is 56.09 ms. Finally, this research verified that obstacles and distances are among the contributing factors affecting the throughput and latency performance of the Wi-Fi network

Improving fingerprint-based positioning by using IEEE 802.11mc FTM/RTT observables

Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.This research was partially supported by MCIN/AEI/10.13039/ 501100011033 and ERDF “A way of making Europe” under grant PGC2018-099945-BI00, and by the European GNSS Agency (GSA) under grant GSA/GRANT/04/2019/BANSHEEPeer ReviewedPostprint (published version

Indoor Positioning for BIM

Building Informational Modeling (BIM) is very popular in the construction industry in Norway today, and Omega 365 has created a suite of tools for BIM, including a 3D visualising tool for 3D models of buildings, called a BIMViewer. This tool exists in multiple forms, and one of them is an app for mobile phones, which construction workers carry with them on construction sites. When determining one's own position in the BIMViewer, it may take time to find and select the correct position. This study aims to create a feature for the BIMViewer using new technology, IEEE802.11mc and comparing it with an old method, Wi-Fi received signal strength (RSS) with the Log Distance Path Loss model. In addition, GPS was tried in order to prove it was not usable for this use case and in order to compare it with the other two methods. The main goal is to find a method that is cheap for clients to implement in regards to equipment and installation, but is precise enough to provide a good user experience. Three experiments were conducted for this study, one using only GPS and two for the other two methods. One experiment used only a single floor and the other used two floors. Both of these experiments used only 6 access points and were conducted at NyeSUS, the new hospital in Stavanger which was an active construction zone during the experiments. The experiments showed that GPS was a bad choice for the use case and that both the other methods were usable. The round trip time (RTT) method, which used the IEEE802.11mc measurements was more precise than the RSS method, however suffered from the need for more access points than the RSS method. This study concludes that both the RTT and the RSS methods may be usable, however some improvements would be needed for a truly good user experience. The study also suggests that a mix of the two methods may be beneficial

WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research

A New Set of Wi-Fi Dynamic Line-Based Localization Algorithms for Indoor Environments

Localization is of great importance for several fields such as healthcare and security. To achieve localization, GPS technologies are common for outdoor localization but are insufficient for indoor localization. This is because the accuracy and precision of the users’ indoor locations are influenced by many factors (e.g., multipath signal propagations). As a result, the methodologies and technologies for indoor localization services need to remain continuously under development. A related challenge is the time complexity of the methodologies which impacts the performance of the mobile phones’ limited resources. To address these challenges, a new set of fingerprinting algorithms called Fingerprinting Line-Based Nearest Neighbor (FLBNN) is proposed. Furthermore, the new set is compared to other existing Nearest Neighbor-based algorithms. When the deployment of four access points is considered, the FLBNN algorithms outperform several algorithms in terms of accuracy such as Nearest Neighbor version 2, Nearest Neighbor version 4, and Soft-Range-Limited KNN by approximately 17.1%, 7.8%, and 24.1%; respectively. With regards to precision, the new set of algorithms outperforms Path-Loss-Based Fingerprint Localization (PFL) and Dual-Scanned Fingerprint Localization (DFL) by approximately 7.0% and 60.9%; respectively. Moreover, the FLBNN algorithms have a time complexity of O(t * p) where the term t is the number of deployed centroids and the term p is the number of Path Loss exponents. In addition, the new set of algorithms achieves faster run time compared to those for PFL and DFL. As a result, this Thesis improves the cost and reliability of the indoor location services

Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons

Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.Los sensores infrarrojos piroeléctricos (PIR) se utilizan ampliamente como detectores de infrarrojos (IR) debido a su implementación básica, bajo costo, baja potencia y rendimiento. Combinados con una lente Fresnel, se pueden utilizar como detector binario en aplicaciones de control de presencia y movimiento. Además, por sus características, pueden utilizarse en dispositivos inteligentes autónomos o incluirse en aplicaciones de robótica o redes de sensores. En este trabajo, se presentan dos arquitecturas de procesamiento neuronal: (1) un enfoque de procesamiento analógico para lograr el comportamiento de una neurona presináptica a partir de un sensor PIR. Se implementa un circuito analógico similar al modelo de integración y disparo con fugas para poder generar tasas de picos proporcionales a los estímulos IR recibidos en un sensor PIR. (2) Una neurona postsináptica integrada donde se introduce una matriz de red neuronal con picos junto con un algoritmo basado en técnicas de procesamiento digital. Esta estructura permite conectar un conjunto de sensores al circuito postsináptico emulando un nervio óptico. Como estudio de caso, todo el enfoque de procesamiento neuronal presentado en este artículo se aplica a la detección de flujo óptico considerando una matriz de cuatro PIR como entrada. Los resultados validan tanto el enfoque de picos para un sensor analógico presentado como la capacidad de recuperar la información analógica enviada como trenes de picos en un nervio óptico simulado.This research was partially supported by the Spanish grant MINDROB (PID2019-105556GB-C33) and by the CHIST-ERA H2020 grant SMALL (CHIST-ERA-18-ACAI-004)