Optomyography is the term chosen to name a device containing several reflective photosensors placed in an armband. The device is constructed so that it can measure muscle movements in the arm through the detection of skin structure variation. The aims of this research were to build such a device that is capable of expressing the structure changes in form of intensity changes in light emitting diodes and determine whether it is possible to distinguish different muscle movements. This was accomplished by designing and testing two different circuit schematics for Optomyography that differ depending on whether the outgoing signal is processed or not. The schematics were then used to build two electrical circuits of Optomyography that were tested with several subjects to see if they were functioning. The results show that only the version of Optomyography, which processes the outgoing signal with a high-pass filter and amplifies it, is able to detect muscle movements through skin structure variations. However, further investigation of the device is needed to see if different movements can be distinguished.

Railway Catenary systems play a crucial role in the safe and reliable transportation of goods and people throughout the world. Monitoring the catenary infrastructure is crucial for safety purposes and therefore requires inspections. However, the current inspection methods are not sufficient for detecting all possible failure modes. The use of lidar has been proposed to augment the current inspection methods. This research proposes two methods for the classification of various overhead catenary components, resulting from lidar data, both solely relying on the coordinates of the captured datapoints. The methods resulted from a literature analysis and the parameters were obtained trough experimentation with a small dataset. The methods were validated using a larger dataset of 22.5 km between Boden and Gällivare and achieved promising outcomes. The first method resulted in an F1 score of 93,37% was obtained with 87,39% accuracy, whereas the second method, using a simple morphological region filtered obtain an F1 score of 95,48% and an accuracy of 91,27%. The novel contributions of the processing of lidar data in railway infrastructure is the use of a simple morphological region filter and the use of surface variation, a geometric feature for the extraction of masts and bridges. Further research is advised into the computational efficiency and further classification of components in the overhead catenary system.