Location of Persons Using Binary Sensors and BLE Beacons for Ambient Assitive Living

In ambient assistive living (AAL) it is of a paramount importance to be able to detect, localize and estimate the activities of persons living at their homes. Specially, estimating an accurate person's localization and detailed movement patterns in everyday activities, are very valuable for health monitoring and safety assessment. The indoor positioning and navigation (IPIN) research community mainly concentrates on the use of radio beaconing for trilateration with WiFi, Bluetooth or UWB, acoustic beaconing, inertial pedestrian dead-reckoning, and the fusion of several approaches, using fingerprinting or Bayesian estimators. In the area of activity recognition (AR) authors use different kind of sensors such as smart floors, binary sensors attached to common objects, or infer the proximity to objects using Bluetooth beacons. In this paper we want to join these two different field approaches (IPIN and AR) by proposing an indoor localization method that make use of smart floor information, binary sensors, and the signal strength received at a smartwatch coming from BLE beacons deployed in a smarlab. We use the smart floor as a ground truth in order to estimate location accuracy of persons in a totally unobtrusive way. The localization results, for a person moving in the smart livinglab during 10 days, showing accuracies below 1.5 meter in 80% of the cases. The proposed approach can help the tracking of multiple persons living together and also serve as a complement to improve the performance of location-aware activity recognition algorithms,

[1]  Fernando Seco Granja,et al.  Event-Driven Real-Time Location-Aware Activity Recognition in AAL Scenarios , 2018, UCAmI.

[2]  Rainer Mautz,et al.  The challenges of indoor environments and specification on some alternative positioning systems , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[3]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[4]  R. Mautz.  Indoor Positioning Technologies , 2012 .

[5]  Ignas Niemegeers,et al.  A survey of indoor positioning systems for wireless personal networks , 2009, IEEE Communications Surveys & Tutorials.

[6]  Fernando Seco Granja,et al.  Light-matching: A new signal of opportunity for pedestrian indoor navigation , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[7]  Chris D. Nugent,et al.  A Web System for Managing and Monitoring Smart Environments , 2016, IWBBIO.

[8]  Fernando Seco Granja,et al.  Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings , 2012, J. Locat. Based Serv..

[9]  Chris D. Nugent,et al.  UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living , 2018, UCAmI.

[10]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[11]  Neal Patwari,et al.  Radio Tomographic Imaging with Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[12]  C. Nugent,et al.  The Experience of Developing the UJAmI Smart Lab , 2018, IEEE Access.

[13]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[14]  F. Seco,et al.  A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[15]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.