Examensarbeten för masterexamen // Master Theses
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- PostDirect feedback application for training with Osteoarthritis affected to the lower extremities(2021) Carlberg, Emelie; Chalmers tekniska högskola / Institutionen för fysik; Karlsteen, Magnus; Karlsteen, MagnusThis master’s thesis covers the development of an application for patients and healthcare providers training to alleviate Osteoarthritis symptoms. The method used in this application to judge if the training is performed in a safe and correct manner is by using angles. These angles are measured using the smartphone accelerometers, magnetometer and gyroscope. These signals are used to provide direct feedback to the user, using Euler angles and a complementary filter. The results indicate that the smartphone application can measure the angles of interest with good precision. There is still need for further development due to the problems of gimbal locking using Euler angles. Conclusions from the work is that there is great potential using the application both from a patient perspective and as a long term method for healthcare providers to follow up without physical visits.
- PostInvestigation of Long Bow Vibrations(2021) Mosawi, Nasrin; Chalmers tekniska högskola / Institutionen för fysik; Karlsteen, MagnusThe work was about mapping which vibrations occur in bows and how they could affect accuracy. The work was carried out during the Corona pandemic, which meant limited resources for experimental measurement and the experiments were done in an office environment and not in the field. If possible, it was important to find applicable theory behind all experimental results. One goal was to measure with simple means and really understand what was to be measured and how. All experiments and measurements were done on a recurve bow. The measurements therefore began with recording sound using a microphone. Accelerometers were used for further measurements. To increase the understanding of these, a known source of vibration was needed. Thereby, a loudspeaker was modified with the possibility of attaching accelerometers in the middle of the diaphragm. The loudspeaker was powered by a signal generator via a power amplifier. A PC oscilloscope was used to record and interpret signals from the accelerometers. Three different oscillations were recorded, one elemental transverse oscillation of the string, the bending oscillation of the limbs and the third was transverse oscillation laterally with the string and arrow together. All three had a period that was much longer than the shooting. It was then considered important to be able to measure to describe the movement of the handle during the shooting process, which is specific to each combination archer and bow. Experiments on this were done with available equipment that was not sufficient due to too slow serial communication. After creating a theoretical model of the frame, ”cushioning pads” could be shown to have a positive but rather small effect.
- PostPredicting Environment Variables Using Accumulated Sensor Data(2021) Inbasekaran, Aravind; Chalmers tekniska högskola / Institutionen för fysik; Granath, Mats; Owais, MahmudiEnvironmental factors such as road roughness play a crucial role in operation of heavy vehicles. In order to recommend appropriate specifications to customers, manufacturers need to know which kind of road profile a vehicle will be operated on. By recommending correct specifications, manufacturers not only improve life of a vehicle but also trust of their customers. Over the years, many methods have been proposed to find the roughness profile of the roads. Our work focuses on how the road roughness can be classified using the data obtained from the on-board sensors fitted in the vehicles and with the help of HERE API which is a third party API that returns the roughness value information. The data that we have used are the accelerometer readings, Global Positioning System (GPS) and the roughness index values from HERE API for the classification. These data are collected over different periods for different vehicles and stored in a database. The main aim of this thesis is to build a Machine Learning model which will be able to classify a specific route/road between two GPS points into three main roughness categories - Good, Fair and Poor. This has been approached with the Support Vector Machine model and Multi-Layer Perceptron model.