[BEIT NEWS] 12 Oct 2018 – eNightLog for elderly, digital construction and cyber security at healthcare
The non-contact and non-invasive eNightLog system is embedded with event sequence tracking and different kinds of remote sensing and imaging technologies, based on innovative algorithm developed by the BME team of PolyU. This innovative technique has already been patented.
Seventeen systems have been installed and tested in a nursing home for 2 months this year for nighttime monitoring. During this period, 380 incidences of elderly leaving bed alone were recorded, with all them being successfully detected (100%), and only 2 times of false alarm occurred (0.5%). In addition, the system recorded 525 events of caregivers visiting elderly and accompanying the elderly leaving, and accuracy rate is 100%.
Working on Imerso is simple as Users start by loading the software with all the data from the planning stage, which includes BIM, geometry, schedules as well as tolerance parameters and then these data are used as monitoring targets. When, site works continues, users upload 3D data on field. The next step perform by Imerso is to register that 3D data directly to the 3D building information model and displays the result, through this it is easy for the users to compare the current on-site conditions against the original design intent.
… by leveraging machine learning, organizations can largely diminish the security concerns that come with Internet of Things (IoT). Machine learning enables data exchanges to be monitored within the organization, and with external parties to detect anomalies that are not considered the norm. Machine learning can also aid in predicting threats, in its ability to analyze historical data from specific trends, which can be evaluated from the big data produced by the algorithms.
Keywords: Healthcare; Internet of Things (IoT); cyber security; machine learning; behavior analytics