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

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