McCreath Frangakis, Alexis2024-09-042024-09-042024-09-04http://hdl.handle.net/10393/46530https://doi.org/10.20381/ruor-30533Data gathered from inertial measurement units can be used to design subtask segmentation algorithms that further analyse functional mobility tests. This method has been shown to be effective for the 6-minute walk test, and the timed up-and-go test (TUG), and have produced gait analysis and fall risk models, especially in an elderly population. While further research has recently been published to assess individuals with a more asymmetrical gait, such as lower limb amputees, subtask segmentation of functional mobility tests such as the L Test of Functional Mobility (L Test), which are recommended for lower limb amputees, has not been investigated. With a decreased ceiling effect in comparison to the TUG, and increased turn requirements, the L Test would be able to provide a more in-depth assessment of a patient's mobility. In this thesis, a rule-based subtask segmentation algorithm was designed and tested with data from both able-bodied individuals and lower limb amputees. The algorithm produced acceptable results for able-bodied individuals (97% accuracy, > 98% specificity, > 74% sensitivity), but had low sensitivity for data from lower limb amputees (93-97% accuracy, 97-99% specificity, 33-60% sensitivity). A machine learning algorithm was then trained on data from both able-bodied and lower limb amputee participants and tested on data from a lower limb amputee population. This algorithm produced improved results for lower limb amputee participants (> 85% accuracy, > 75% sensitivity, > 95% specificity). This research designed and assessed both a rule-based and a machine learning algorithm for subtask segmentation of the L Test using data collected from both able-bodied and lower limb amputee participants. Overall, this thesis contributes to the progression of movement analysis for lower limb amputees, and to the understanding of motion during an L Test.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/L TestSubtask SegmentationFunctional MobilityThesis