Long Term Observation and Data Analysis of Active and Passive Capacitive Signals
During the last two decades, many different technologies have been developed to deploy capacitive sensing with various kinds of applications. This study aims to contribute to the growing area of capacitive sensing research by exploring new scopes of applications for capacitive sensing. Five case studies with different scenarios have been investigated by using two types of capacitive sensors either the passive or active sensor. Each use case has been studied under a specific environment for each scenario and the experiments have been carried out several times for a long term to get the most comprehensive behavior of the signal under these specific conditions. After observing, experimenting for a long term it was possible to determine the best scenario for each use case. Then we propose a technical implementation for analyzing the data and classifying the activities by implementing multiple algorithms using different techniques. The reported conclusion for each use case assesses how efficient the capacitive sensing would be for this application. Furthermore, we have indicated the challenges and limitations for every use case and proposed a future work either to overcome the limitations we have faced or to optimize the functionality and expand the scope of the usage. Some of these use cases are contributed to the field of smart living like smart driving, smart parking, and some of them are contributed to the field of smart health care like monitoring the patient unobtrusively in the bed as well as the contribution in the field of smart energy in the area of the power monitoring system. Finally, we have integrated all tested applications in one application of smart working.
Darmstadt, TU, Master Thesis, 2019