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Jawad Ahmad new doctor at STC
Wed 01 Dec 2021 16:17
On Tuesday November 30, Jawad Ahmad successfully presented his doctoral thesis "Development and Characterization of Large Area Pressure Sensors and Sitting Posture Monitoring Systems" to the grading committee and and he can now entitle himself as doctor.
Opponent to the thesis was Professor Leena Ukkonen from Tampere University in Finland. In the grading committee was Associate Prof. Zhibin Zhang from Uppsala Universitet, Associate Prof. Sari Merilampi from Satakunta University of Applied Sciences, Finland and Professor Tingting Zhang from Mid Sweden University.
Jawad presented his work with developing a large area pressure sensors for monitoring sitting postures. The pressure sensing system measures distributed pressure when an individual sits on a chair equipped with a pressure sensor array. This technology could provide grounding for the advancement of health-related monitoring systems for both able-bodied and disabled individuals and inform them of their sitting time and sitting posture, and this could be used to establish a sitting pattern. To accomplish this, Jawad designed the pressure sensors using non-conventional flexible electronics. A blend of non-conductive and low-resistance ink were used as pressure-sensitive material to enable the realization of screen-printed sensors. To characterise the performance of the suggested pressure sensor, several tests, such as repeatability, drift and flexibility, was conducted. He also exposed the sensor to different humidity and temperature conditions in a climate chamber to examine its functionalities.
A graphical user interface was developed for real-time demonstration of data from distributed pressure points in the form of a pressure map to display the pressure values. Four sitting postures was identified: forward, backward, left, and right leaning. Furthermore, a stretchable pressure sensor is proposed that could follow slight stretching with regard to changes in the shape of the human skin. Machine learning algorithms have been employed to further enhance the sitting posture identification, and accuracy of 99.03% is attained. A standalone embedded system capable of illustrating real-time pressure data has been developed with the potential to be used in portable health monitoring systems. In summary, this work provides a promising framework for measuring pressure distribution and identifying irregular sitting postures that may help to reduce the potential risks of developing health-related issues associated with prolonged sitting time.
