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. (2011). Near Field Communication, 151-239. doi:10.1002/9781119965794.ch5Matsuoka, H., Wang, J., Jing, L., Zhou, Y., Wu, Y., & Cheng, Z. (2014). Development of a control system for home appliances based on BLE technique. 2014 IEEE International Symposium on Independent Computing (ISIC). doi:10.1109/indcomp.2014.7011751Standard ECMA-404. The JSON Data Interchange Format http://www.ecma-international.org/publications/files/ECMA-ST/ECMA-404.pdfThe EU General Data Protection Regulation http://www.eugdpr.org/Tews, E., & Beck, M. (2009). Practical attacks against WEP and WPA. Proceedings of the second ACM conference on Wireless network security - WiSec ’09. doi:10.1145/1514274.1514286Farooq, U., & Aslam, M. F. (2017). Comparative analysis of different AES implementation techniques for efficient resource usage and better performance of an FPGA. 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

INDOOR LOCATION TRACKING AND ORIENTATION ESTIMATION USING A PARTICLE FILTER, INS, AND RSSI

With the advent of wireless sensor technologies becoming more and more common-place in wearable devices and smartphones, indoor localization is becoming a heavily researched topic. One such application for this topic is in the medical field where wireless sensor devices that are capable of monitoring patient vitals and giving accurate location estimations allow for a less intrusive environment for nursing home patients. This project explores the usage of using received signal strength indication (RSSI) in conjunction with an inertial navigation system (INS) to provide location estimations without the use of GPS in a Particle Filter with a small development microcontroller and base station. The paper goes over the topics used in this thesis and the results

A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination

Recently with the advancement of solid state lighting and the application thereof to Visible Light Communications (VLC), the concept of Visible Light Positioning (VLP) has been targeted as a very attractive indoor positioning system (IPS) due to its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth. IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of VLP approaches have been proposed in the literature and primarily focus on a fixed combination of these elements and moreover evaluate the quality of the contribution often by accuracy or precision alone. In this dissertation, we provide a novel two-phase indoor positioning algorithmic framework that is able to increase robustness when subject to insufficient anchor luminaries and also incorporate any combination of the four major IPS components. The first phase provides robust and timely albeit less accurate positioning proximity estimates without requiring more than a single luminary anchor using time division access to On Off Keying (OOK) modulated signals while the second phase provides a more accurate, conventional, positioning estimate approach using a novel geometric constrained triangulation algorithm based on angle of arrival (AoA) measurements. However, this approach is still an application of a specific combination of IPS components. To achieve a broader impact, the framework is employed on a collection of IPS component combinations ranging from (1) pulsed modulations to multicarrier modulations, (2) time, frequency, and code division multiple access, (3) received signal strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and triangulation positioning algorithms. Results illustrate full room positioning coverage ranging with median accuracies ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework further allows for duty cycle variation to include dimming modulations and results range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a 78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a novel latency constrained optimization algorithm can be overlaid on the two phase framework to decide when to simply use the coarse estimate or when to expend more computational resources on a potentially more accurate fine estimate. The creation of the two phase framework enables robust, illumination, latency sensitive positioning with the ability to be applied within a vast array of system deployment constraints

LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph- Matching

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (ULPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.Universidad de AlcaláJunta de Comunidades de Castilla-La ManchaAgencia Estatal de Investigació

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

XRLoc: Accurate UWB Localization for XR Systems

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas which allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves $2.4$ cm median accuracy and $5.3$ cm $90^\mathrm{th}$ percentile accuracy in dynamic scenarios, performing at least $8\times$ better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over $10$ tags at update rates of $100$ Hz, with a localization latency of $\sim 1$ ms

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known