Monitoring of doors, door handles and windows using inertial sensors

University essay from KTH/Reglerteknik

Author: Oskar Eliasson; [2017]

Keywords: ;

Abstract: The main topics of this master's project are non-linear ltering techniques,system identication, and supervised classication. The main goal is to develop,implement, and test methods using inertial sensors to monitor doorandwindow movement. Secondly, it aims to investigate if it is possible tomonitor door movements by using event classication. The thesis beginswith an overview of the available sensors and a theoretical part, in which theltering algorithms and the modelling of door- and window movements aresummarized. Later a supervised learning algorithm is developed. Lastly, thehardware used to test the algorithm and the results from the tests are shown.The goal of the project is to develop ltering techniques using accelerometers,gyroscopes, and magnetometers. They measure various inertial units of amoving body. By combining measurements from dierent sensors and fusingthem, the movement of a body can be tracked. Door- and window movementare modelled and the Kalman lter is used to estimate the true parameters ofthe model. Due to the imperfections of the sensors, properties such as osetand drift are included in the models. The models are to be used in doorsand windows to track the angle of which they are open. Due to dicultiesof tracking the movement when they hit the door- or window frame, theK-Nearest Neighbours classication algorithm is used to predict whether thedoor- or window closes fully.A general method of implementing the ltering techniques is constructed.Due to limitations in computational power, limited sampling rate of thesensors, and the nonlinear nature of the movement, some generalizations arerequired. Hardware which can be placed on doors and windows are used toverify the models. As a rst verication, the models and ltering techniqueswere tested in Matlab. Secondly, the algorithms were tested live on doorsand windows. The result shows it is possible to track the movement of doorsand windows with an accuracy of over 10. The classication algorithmshows some success with tracking a closing door but requires more work tobecome more accurate. The result also shows that the sampling rate has tobe taken into consideration when designing the algorithms, a higher samplingrate results in more accurate tracking but increases power consumption andcomputing power.

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