Design and development of an IOT based wearable device for the safety and security of women and girl children

The aim of this work is to develop a wearable device for the safety and protection of women and girls. This objective is achieved by the analysis of physiological signals in conjunction with body position. The physiological signals that are analyzed are galvanic skin resistance and body temperature. Body position is determined by acquiring raw accelerometer data from a triple axis accelerometer. Acquisition of raw data is then followed by activity recognition which is a process of employing a specialized machine learning algorithm. Real-time monitoring of data is achieved by wirelessly sending sensor data to an open source Cloud Platform. Analysis of the data is done on MATLAB simultaneously. This device is programmed to continuously monitor the subject's parameters and take action when any dangerous situation presents itself. It does so by detecting the change in the monitored signals, following which appropriate action is taken by means of sending notifications/alerts to designated individuals.

[1]  Pedro M. Domingos.  A few useful things to know about machine learning , 2012, Commun. ACM.

[2]  Kenji Mase,et al.  Activity and Location Recognition Using Wearable Sensors , 2002, IEEE Pervasive Comput..

[3]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[4]  Bhanu Kapoor,et al.  Intelligent data analysis algorithms on biofeedback signals for estimating emotions , 2014, 2014 International Conference on Reliability Optimization and Information Technology (ICROIT).

[5]  Sotiris E. Nikoletseas,et al.  Hierarchical algorithm for daily activity recognition via smartphone sensors , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[6]  Gerhard Tröster,et al.  Discriminating Stress From Cognitive Load Using a Wearable EDA Device , 2010, IEEE Transactions on Information Technology in Biomedicine.

[7]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[8]  Emil Jovanov,et al.  Stress monitoring using a distributed wireless intelligent sensor system. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[9]  Mykola Pechenizkiy,et al.  What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[10]  Hao Liu,et al.  Towards mental stress detection using wearable physiological sensors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  A. A. Abdullah,et al.  Design and development of an emotional stress indicator (ESI) kit , 2012, 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT).