Analyzing Lower Limb Motion Capture with Smartphone : Possible improvements using machine learning ; Analys av rörelsefångst för nedre extremiteterna med smartphone : Möjliga förbättringar med hjälp av maskininlärning

Human motion analysis (HMA) can play a crucial role in sports and healthcare by providing unique insights on movement mechanics in the form of objective measurements and quantitative data. Traditional, state of the art, marker-based techniques, despite their accuracy, come with financial and logistical barriers, and are restricted to laboratory settings. Markerless systems offer much improved affordability and portability, and can potentially be used outside of laboratories. However, these advantages come with a significant cost in accuracy. This thesis attempts to address the challenge of democratizing HMA by leveraging recent advances in smartphone technology and machine learning.\newline\newlineThis thesis evaluates two modalities of performing markerless HMA: Single smartphone using Apple Arkit, and multiple smartphone setup using OpenCap, and compares both to a state of the art multiple-camera marker-based system from Vicon. Additionally, this thesis presents and evaluates two approaches to improving the single smartphone modality: Employing a Gaussian Process Model (GPR), and a Long-short-term-memory (LSTM) neural network to refine the single smartphone data to align with the marker-based result. Specific movements were recorded simultaneously with all three modalities on 13 subjects to build a dataset. From this, GPR and LSTM models were trained and applied to refine the single camera modality data. Lower limb joint angles, and joint centers were evaluated across the different modalities, and analyzed for potential use in real-world applications. While the findings of this thesis are promising, as both the GPR and LSTM models improve the accuracy of Apple Arkit, and OpenCap providing accurate and consistent results. It is important to acknowledge limitations regarding demographic diversity and how real-world environmental factors may influence its application. This thesis contributes to the efforts in narrowing the gap between marker-based HMA methods, and more accessible solutions. ; Rörelseanalys av ...