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Pei, L., Guinness, R., Chen, R.Z., Liu, J.B., Kuusniemi, H., Chen, Y.W., Chen, L. and Kaistinen, J. (2013) Human Behavior Cognition Using Smartphone Sensors. Sensors, 13, 1402-1424.
http://dx.doi.org/10.3390/s130201402

has been cited by the following article:

• TITLE: Direction Detecting System of Indoor Smartphone Users Using BLE in IoT

  AUTHORS: D. Kothandaraman, C. Chellappan

  KEYWORDS: Orientation Sensor, BLE (Bluetooth Low Energy), IoT (Internet of Things), Direction Detection

  JOURNAL NAME: Circuits and Systems, Vol.7 No.8, June 15, 2016

  ABSTRACT: Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoorfixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user’s smartphones belonging to the Internet of Things (IoT). The orientation sensor senses theactual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based onsignal variation instantly in real time. Theuser activity’ssignals are captured using LabVIEWtoolkit then applied tovariousclassification algorithmssuch asRF—91.42%, Ibk—90.55%, j48—85.61%, K*—73.54% are the results obtained. An average of 85% was obtained in all the classifi- cation algorithims indicating the consistency and accuracy in detecting the directions of the users.RF was found to be the best among all the classification algorithms.IoT enabled devices have highdemand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.