Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding

Angle-of-Arrival Estimation with Practical Phone Antenna Configurations

With the advances of the Internet of Things and mobile connectivity, location-based services are becoming increasingly popular and continue to enhance our experience. Multiple antennas have been pivotal in providing reliable wireless communications and high-resolution localization. If the antennas of the array are isotropic, then the simplified array manifold determined by the array geometry can be used to estimate the angle-of-arrival (AOA). However, in the real world, mobile handsets tend to have very limited space, where the practical antennas are equipped on the same ground plane, and the array geometry hardly obeys the rule of half-wavelength spacing. Therefore, a practical antenna couples signals from other antennas, causing a mutual coupling effect. Complex array manifolds are produced on an antenna even if the received signal is propagated through a single path channel. In addition, the irregular radiation pattern of each antenna further impairs the AOA estimation capability. Given the above effects, the simplified array manifold determined by the array geometry can no longer provide precise localization. In this paper, we propose a generic array manifold model for both isotropic and practical antennas. We also present an efficient algorithm to enable AOA estimation on practical antennas on the basis of the proposed model and implement it on a 5G phone at a mid-band spectrum with a 100MHz channel bandwidth. Results reveal the promising performance of the proposed model, with the AOA estimation errors lower than 10∘ in over 90% of the scenarios

Enhanced the Weighted Centroid Localization Algorithm Based on Received Strength Signal in Indoor Wireless Sensor Network

A challenging problem that arises in the Wireless Sensor Network (WSN) is localization. It is essential for applications that need information about target positions, are inside an indoor environment. The Localization scheme presented in this experiment consists of four anchor nodes that change their position coordinates and one target node that is used to control the distance. The Localization algorithm designed in this paper makes use of the combination of two algorithms; the Received Strength Signal Indication (RSSI) and Weight Centroid Localization Algorithm (WCLA), called the RSSI-WCLA algorithm. The laboratory results show that the fusion between the RSSI-WCLA algorithm is outstanding than RSSI and WCLA algorithms itself in terms of localization accuracy. However, our proposed algorithm shows that the maximum error distance is less than 0.096m

Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes

Adaptive filtration of the UAV movement parameters based on the AOA-measurement sensor networks

Currently, the urgent task is to assess the small-sized maneuvering UAVs movement parameters. The location of an unknown UAV as a radio source can be determined using AoA measurements of the wireless sensor network. To describe the movement of a maneuvering UAV, a model is used in the form of a dynamic system with switching in discrete time. The values of switching variable determine type of UAV movement. To synthesize trajectory filtering algorithms, the Markov property of the extended process is used, which includes a vector of UAV movement parameters and a switching variable. The optimal trajectory filtering algorithm describes a recurrent procedure for calculating the a posteriori probability density function of an extended process. The optimal filtering device is multi-channel with feedback between the channels. To synthesize a quasi-optimal algorithm, linearized equations of UAV coordinates measurement in a Cartesian coordinate system based on AoA-measurements of a sensor network were obtained and an measurement errors analysis was performed. The quasi-optimal algorithm is obtained using the Gaussian approximation method of conditional a posteriori probability density functions and implements sequential processing of incoming measurements. It provides a joint solution to the problems of estimating UAV coordinates and recognizing of its movement type. Analysis of developed algorithm efficiency was carried out by Monte Carlo method. Shows the dependences of movement types recognition probabilities. A comparative analysis is performed with the Kalman filtering algorithm

Design criteria for Indoor Positioning Systems in hospitals using technological, organizational and individual perspectives

This dissertation considers three different studies that handle Indoor Positioning Systems (IPS) in hospitals. Study 1 uses the Reasoned Action Approach by questioning hospital visitors and employees about their intention to use IPS in hospitals. Study 2 reviews IPS in hospitals. Study 3 is based on the results of the first two studies. It handles expert interviews that were conducted with different hospitals and IPS developers to evaluate the determined propositions. Then, the insights were used to conduct and evaluate experiments by testing an ultrasound-based IPS for hospitals

Continuous monitoring of health and mobility indicators in patients with cardiovascular disease: a review of recent technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications