Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model's performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

In Chile, 18% of the population is over 60 years old and is projected to reach 31% in three decades. An aging population demands the development of strategies to improve quality of life (QoL). In this randomized trial, we present the implementation and evaluation of the Quida platform, which consists of a network of unintrusive sensors installed in the houses of elderly participants to monitor their activities and provide assistance. Sixty-nine elderly participants were included. A significant increase in overall QoL was observed amongst participants allocated to the interventional arm (p < 0.02). While some studies point out difficulties monitoring users at home, Quida demonstrates that it is possible to detect presence and movement to identify patterns of behavior in the sample studied, allowing us to visualize the behavior of older adults at different time intervals to support their medical evaluation.

The Problem of Monitoring Activities of Older People in Multi-Resident Scenarios: An Innovative and Non-Invasive Measurement System Based on Wearables and PIR Sensors

This paper presents an innovative multi-resident activity detection sensor network that uses the Bluetooth Low Energy (BLE) signal emitted by tags worn by residents and passive infrared (PIR) motion sensors deployed in the house to locate residents and monitor their activities. This measurement system solves the problem of monitoring older people and measuring their activities in multi-resident scenarios. Metrics are defined to analyze and interpret the collected data to understand daily habits and measure the activity level (AL) of older people. The accuracy of the system in detecting movements and discriminating residents is measured. As the sensor-to-person distance increases, the system decreases its ability to detect small movements, while still being able to detect large ones. The accuracy in discriminating the identity of residents can be improved by up to 96% using the Decision Tree (DT) classifier. The effectiveness of the measurement system is demonstrated in a real multi-resident scenario where two older people are monitored during their daily life. The collected data are processed, obtaining the AL and habits of the older people to assess their behavior.

Simulation Tool and Online Demonstrator for CDMA-Based Ultrasonic Indoor Localization Systems

This work presents the CODEUS platform, which includes a simulation tool together with an online experimental demonstrator to offer analysis and testing flexibility for researchers and developers in Ultrasonic Indoor Positioning Systems (UIPSs). The simulation platform allows most common encoding techniques and sequences to be tested in a configurable UIPS. It models the signal modulation and processing, the ultrasonic transducers’ response, the beacon distribution, the channel propagation effects, the synchronism, and the application of different positioning algorithms. CODEUS provides results and performance analysis for different metrics and at different stages of the signal processing. The UIPS simulation tool is specified by means of the MATLAB© App-Designer environment, which enables the definition of a user-friendly interface. It has also been linked to an online demonstrator that can be managed remotely by means of a website, thus avoiding any hardware requirement or equipment on behalf of researchers. This demonstrator allows the selected transmission schemes, modulation or encoding techniques to be validated in a real UIPS, therefore enabling a fast and easy way of carrying out experimental tests in a laboratory environment, while avoiding the time-consuming tasks related to electronic design and prototyping in the UIPS field. Both simulator and online demonstrator are freely available for researchers and students through the corresponding website.

The Estimation of the Potential for Using Smart-Trackers as a Part of a Medical Indoor-Positioning System

This research aims to estimate the feasibility of using smart-bracelets as a part of a medicine indoor-positioning system, to monitor the health status and location of patients in a hospital. The smart-bracelet takes on the role of a token of the system and can measure pulse, blood pressure and saturation and provide data transmission over the BLE. The distance between token and anchor was calculated by the RSSI. The position of a token and anchor relative to each other was determined by the trilateration method. The results of the research showed that the accuracy of the developed system in a static position is 1.46 m and exceeds 3 m in a dynamic position. Results of experiments showed that measurements from the smart bracelets are transmitted to the server of the system without distortion. The study results indicated that smart-bracelets could be used to locate patients inside a hospital or estimate their current health state. Given the low accuracy of systolic pressure measurement, it is recommended to develop an algorithm that will allow smooth measuring error for higher-precision estimation of the patient’s general health state. In addition, it is planned to improve the positioning algorithm.

UJAmI Location: A Fuzzy Indoor Location System for the Elderly

Due to the large number of elderly people with physical and cognitive issues, there is a strong need to provide indoor location systems that help caregivers monitor as many people as possible and with the best quality possible. In this paper, a fuzzy indoor location methodology is proposed in a smart environment based on mobile devices and Bluetooth Low Energy (BLE) beacons where a set of Received Signal Strength Indicators (RSSI) is received by mobile devices worn by the inhabitants. The use of fuzzy logic and a fuzzy linguistic approach is proposed to deal with the imprecise nature of the RSSI values, which are influenced by external factors such as radio waves, causing significant fluctuations. A case study carried out at the Smart Lab of the University of Jaén (UJAmI Smart Lab) is presented to demonstrate the effectiveness of the proposed methodology, where our proposal is compared with a non-fuzzy logic approach, obtaining an accuracy of 91.63%, approximately 10 points higher than the methodology without using fuzzy logic. Finally, our theoretical proposal is accompanied by a description of the UJAmI Location system, which applies the theory to the functionality of locating elderly people in indoor environments.

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman-Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, PFA, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain PFA<10-5 for a frame length of 300 ns, it is required that the threshold should be greater than 2.2. It is also shown that for a fix PFA, the probability of detection PD increases with an increase in interference-to-noise ratio (INR). Furthermore, PD approaches 1 for INR >-2 dB even for a very low PFA i.e., PFA=1×10-7. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed PFA=0.1 and PD=0.5. Finally, the derived performance expressions are corroborated through simulation.

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards-Case Study: Workplace Reopening

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning

Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity and stability of RSS fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated RSS Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse RSS acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of RSS measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey.

Assessment of Anchors Constellation Features in RSSI-Based Indoor Positioning Systems for Smart Environments

In this paper, we assess the features of a rectangular constellation of four anchors on the position estimation accuracy of a mobile tag, operating under the IEEE 802.15.4 specifications. Each anchor implements a smart antenna with eight switched beams, which is capable to collect Received Signal Strength Indicator (RSSI) data, exploited to estimate the mobile tag position within a room. We also aim at suggesting a deployment criterion, providing the discussion of the best trade-off between system complexity and positioning accuracy. The assessment validation was conducted experimentally by implementing anchor constellations with different mesh sizes in the same room. Mean accuracies spanning from 0.32 m to 0.7 m on a whole 7.5 m × 6 m room were found by varying the mesh area from 1.19 m2 to 17 m2, respectively.