Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Smart hospital emergency system via mobile-based requesting services

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study

Voice Enabled Indoor Localisation

The ability to track objects in real time offers a wide range of beneficial applications that include safety, security and the supply chain. The problem with location based systems is that they can be inconvenient and time-consuming to locate an object. A user has to access a computer and log onto a location system to locate an object. There are several problems with current location determination interfaces. Firstly a user has to log on to a computer; this can be inconvenient and time consuming as the user may have to locate a computer (which may be in another area of a building) and then log onto the system. The user has to look at a map that is displayed on screen to see where the object is located; the problem with this is that the user could make a mistake by looking at the wrong object or the wrong area of the building. Incorporating a voice control function into the system can solve interaction problems with some location based systems. This paper provides an overview of integrating voiceXML with an indoor location positioning system to locate objects through voice commands

Location Detection of Dementia Using GPS Coordinate Finder

Gerontology problems affect many old citizens at the age of 67 years old and above. Many people become forgetful as they become older and one of the problems is dementia. Memory impairments are the most common among people with dementia. Dementia can happen to anybody, but it is more common after the age of 65 years. It causes a loss of mental ability, and other symptoms. Dementia can be caused by various disorders which affect parts of the brain involved with thought processes. People with dementia do not remember how to get back home and there is no specialized device to assist family members to take care of their elders in Malaysia. Thus, this project aims to identify tracking system that is suitable for dementia and to develop an application to help relatives to track dementia’s location. This scope is to find the location of dementia and to help relatives to track dementia’s location using GPS tracker and coordinate finder application. This paper discussed about the location detection of the GPS in order to track for dementia’s location for the wide range of network with the accessibility of the Internet network. This system will help relatives to track the location of the people with dementia and to reduce the burden of family member to take care of dementia people. Additionally, the GPS will detect the location and send the message to receiver to inform the current coordinate location. MIT AppInventor is used in developing the Android Application and tested with a smart phone. This paper describes the system designed of GPS and application, and discussed the implementation of the system to the dementia people. Positive results are received from the conducted user perception study and shows that users (family members) are satisfied with the application in identifying dementia’s location effectivel

Wi-Fi Enabled Healthcare

Focusing on its recent proliferation in hospital systems, Wi-Fi Enabled Healthcare explains how Wi-Fi is transforming clinical work flows and infusing new life into the types of mobile devices being implemented in hospitals. Drawing on first-hand experiences from one of the largest healthcare systems in the United States, it covers the key areas associated with wireless network design, security, and support. Reporting on cutting-edge developments and emerging standards in Wi-Fi technologies, the book explores security implications for each device type. It covers real-time location services and emerging trends in cloud-based wireless architecture. It also outlines several options and design consideration for employee wireless coverage, voice over wireless (including smart phones), mobile medical devices, and wireless guest services. This book presents authoritative insight into the challenges that exist in adding Wi-Fi within a healthcare setting. It explores several solutions in each space along with design considerations and pros and cons. It also supplies an in-depth look at voice over wireless, mobile medical devices, and wireless guest services. The authors provide readers with the technical knowhow required to ensure their systems provide the reliable, end-to-end communications necessary to surmount today’s challenges and capitalize on new opportunities. The shared experience and lessons learned provide essential guidance for large and small healthcare organizations in the United States and around the world. This book is an ideal reference for network design engineers and high-level hospital executives that are thinking about adding or improving upon Wi-Fi in their hospitals or hospital systems

Real-time locating systems (RTLS) in healthcare: a condensed primer

Real-time locating systems (RTLS, also known as real-time location systems) have become an important component of many existing ubiquitous location aware systems. While GPS (global positioning system) has been quite successful as an outdoor real-time locating solution, it fails to repeat this success indoors. A number of RTLS technologies have been used to solve indoor tracking problems. The ability to accurately track the location of assets and individuals indoors has many applications in healthcare. This paper provides a condensed primer of RTLS in healthcare, briefly covering the many options and technologies that are involved, as well as the various possible applications of RTLS in healthcare facilities and their potential benefits, including capital expenditure reduction and workflow and patient throughput improvements. The key to a successful RTLS deployment lies in picking the right RTLS option(s) and solution(s) for the application(s) or problem(s) at hand. Where this application-technology match has not been carefully thought of, any technology will be doomed to failure or to achieving less than optimal results

Accurate acoustic ranging system using android smartphones

ACCURATE ACOUSTIC RANGING SYSTEM USING ANDROID SMARTPHONES By Mohammadbagher Fotouhi, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, we present the design, implementation, and evaluation of an android ranging system, a high-accuracy acoustic-based ranging system which allows two android mobile phones to learn their physical distance from each other. In this system we propose a practical solution for accurate ranging based on acoustic communication between speakers and microphones on two smartphones. Using the audible-band acoustic signal with the Wi-Fi assistance without the sound disturbance is promising for large deployment. Our method is a pure software-based solution and uses only the most basic set of commodity hardware: a speaker, a microphone, and Wi-Fi communication. So it is readily applicable to many commercial-off-the-shelf mobile devices like cell phones. Our system is the result of several design goals, including user privacy, decentralized administration, and low cost. Rather than relying on any centralized management which tracks the user’s location to help them find their distance, our system helps devices learn their distance from each other without advertising their location information with any centralized management. Compared to alternatives that require special-purpose hardware or pre-existence of precision location infrastructure , our system is applicable on most of off-the-shelf components so it is a commodity-based solution will obviously have wider applications and is cost effective. Currently, two smartphones are used to estimate the distance between them through Wi-Fi and audio communications. The basic idea is estimating the distance between two phones by estimating the traveling time of audio signal from one phone to the other as the speed of sound is known. The preliminary results of ranging demonstrate that our algorithm could achieve high accuracy, and stable and reliable results for real time smartphone-based indoor ranging

TechNews digests: Jan - Mar 2010

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

i-Light - Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living

[EN] We present i-Light, a cyber-physical platform that aims to help older adults to live safely within their own homes. The system is the result of an international research project funded by the European Union and is comprised of a custom developed wireless sensor network together with software services that provide continuous monitoring, reporting and real-time alerting capabilities. The principal innovation proposed within the project regards implementation of the hardware components in the form of intelligent luminaires with inbuilt sensing and communication capabilities. Custom luminaires provide indoor localisation and environment sensing, are cost-effective and are designed to replace the lighting infrastructure of the deployment location without prior mapping or fingerprinting. We evaluate the system within a home and show that it achieves localisation accuracy sufficient for room-level detection. We present the communication infrastructure, and detail how the software services can be configured and used for visualisation, reporting and real-time alerting.This work was funded by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number 46E/2015, i-Light-A pervasive home monitoring system based on intelligent luminaires.Marin, I.; Vasilateanu, A.; Molnar, A.; Bocicor, MI.; Cuesta Frau, D.; Molina Picó, A.; Goga, N. (2018). i-Light - Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living. Electronics. 7(10):1-24. https://doi.org/10.3390/electronics7100220S124710World Report on Ageing and Health http://apps.who.int/iris/bitstream/10665/186463/1/9789240694811_eng.pdf?ua=1ECP Makes Switching to eMAR Easy http://extendedcarepro.com/products/Carevium Assisted Living Software http://www.carevium.com/carevium-assisted-living-software/Yardi EHR http://www.yardi.com/products/ehr-senior-care/Yardi eMAR http://www.yardi.com/products/emar/Botia, J. A., Villa, A., & Palma, J. (2012). Ambient Assisted Living system for in-home monitoring of healthy independent elders. Expert Systems with Applications, 39(9), 8136-8148. doi:10.1016/j.eswa.2012.01.153Lopez-Guede, J. M., Moreno-Fernandez-de-Leceta, A., Martinez-Garcia, A., & Graña, M. (2015). Lynx: Automatic Elderly Behavior Prediction in Home Telecare. BioMed Research International, 2015, 1-18. doi:10.1155/2015/201939Luca, S., Karsmakers, P., Cuppens, K., Croonenborghs, T., Van de Vel, A., Ceulemans, B., … Vanrumste, B. (2014). Detecting rare events using extreme value statistics applied to epileptic convulsions in children. Artificial Intelligence in Medicine, 60(2), 89-96. doi:10.1016/j.artmed.2013.11.007Better Health Assessments Every Day, for Better Everyday Living http://healthsense.com/Home Telehealth https://www.usa.philips.com/healthcare/solutions/enterprise-telehealth/home-telehealthThe Carelink Network http://www.medtronic.com/us-en/healthcare-professionals/products/cardiac-rhythm/managing-patients/information-systems/carelink-network.htmlHaigh, P. A., Bausi, F., Ghassemlooy, Z., Papakonstantinou, I., Le Minh, H., Fléchon, C., & Cacialli, F. (2014). Visible light communications: real time 10 Mb/s link with a low bandwidth polymer light-emitting diode. Optics Express, 22(3), 2830. doi:10.1364/oe.22.002830Indoor Positioning System http://www.gelighting.com/LightingWeb/na/solutions/control-systems/indoor-positioning-system.jspIndoor and Outdoor Lighting Solutions http://www.acuitybrands.com/solutions/featured-spacesHuang, C.-N., & Chan, C.-T. (2011). ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI. Procedia Computer Science, 5, 58-65. doi:10.1016/j.procs.2011.07.010Charlon, Y., Fourty, N., & Campo, E. (2013). A Telemetry System Embedded in Clothes for Indoor Localization and Elderly Health Monitoring. Sensors, 13(9), 11728-11749. doi:10.3390/s130911728Patient/Elderly Activity Monitoring Using WiFi-Based Indoor Localization https://wiki.cc.gatech.edu/designcomp/images/3/3d/HHH_Report.pdfReal Time Location System http://zonith.com/products/rtls/Accurate Positioning https://www.pozyx.io/yooBee System Overview https://www.blooloc.com/over-yoobeeThe Top Indoor Location Engine for Smart Apps https://senion.com/Locating People, Way-Finding, and Attendance Tracking https://estimote.com/products/Indoor Navigation, Indoor Positioning, Indoor Analytics and Indoor Tracking https://www.infsoft.com/Lighting Reimagined https://www.lifx.com/Tabu. Lumen. Simply Brighter http://www.lumenbulb.net/Philips Hue http://www2.meethue.com/en-usElgato Avea https://www.elgato.com/en/aveaiLumi—The World’s Most Intelligent Light Bulbs hhttps://www.indiegogo.com/projects/ilumi-the-world-s-most-intelligent-light-bulbs--5#/Bluegiga BLE112 Bluetooth® Smart Module http://www.silabs.com/products/wireless/bluetooth/bluetooth-low-energy-modules/ble112-bluetooth-smart-moduleISO/IEEE 11073 https://www.iso.org/standard/67821.htmlDescription https://www.diodes.com/assets/Datasheets/ZXLD1366.pdfDigital Humidity Sensor SHT2x https://www.sensirion.com/en/environmental-sensors/humidity-sensors/humidity-temperature-sensor-sht2x-digital-i2c-accurate/Photo IC Type High Sensitive Light Sensor https://industrial.panasonic.com/cdbs/www-data/pdf/ADD8000/ADD8000CE2.pdfWSP2110 VOC Gas Sensor http://www.winsen-sensor.com/products/flat-surfaced-gas-sensor/wsp2110.htmlLow Power-Consumption CO2 Sensor http://www.winsen-sensor.com/d/files/PDF/Solid%20Electrolyte%20CO2%20Sensor/MG812%20CO2%20Manual%20V1.1.pdfGP2Y1010AU0F Compact Optical Dust Sensor http://www.sharp-world.com/products/device/lineup/data/pdf/datasheet/gp2y1010au_e.pdfEKMC (VZ) Series http://www3.panasonic.biz/ac/e/control/sensor/human/vz/index.jspSensors for Automotive & Industrial Applications: Grid-EYE Infrared Array Sensor https://na.industrial.panasonic.com/products/sensors/sensors-automotive-industrial-applications/grid-eye-infrared-array-sensorGeneric Attributes https://www.bluetooth.com/specifications/gattDeveloping NFC Applications. 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Journal of King Saud University - Computer and Information Sciences, 29(3), 295-302. doi:10.1016/j.jksuci.2016.01.004Luo, X.-L., Liao, L.-Z., & Wah Tam, H. (2007). Convergence analysis of the Levenberg–Marquardt method. Optimization Methods and Software, 22(4), 659-678. doi:10.1080/10556780601079233Wammu https://wammu.eu/gammu