The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.

Flywheel training is an increasingly popular training method that aids in the recovery process and promotes strength development while reducing the risk of re-injury. Additionally, automatic exercise classification offers athletes the convenience of effortlessly monitoring and tracking their training progress, enabling them to maintain consistency and achieve their fitness goals effectively. This thesis aims to investigate the feasibility and accuracy of developing a machine-learning model for classifying exercises performed on Exxentric kBox machines. The objective is to assess the model’s accuracy and determine whether the features provided by the Exxentric app are sufficient for constructing a robust classifier. To lay a strong foundation for the investigation, the research begins with a comprehensive literature review of exercise recognition studies. An exploratory data analysis is then conducted to gain valuable insights into the characteristics of the exercise data. The data preparation phase involves various techniques such as cleaning, feature engineering, scaling, sampling, and encoding to optimize the data for modeling. Moreover, signal processing techniques are employed to extract relevant features from the exercise data. A testing protocol is established, consisting of two sets of ten exercises. Each exercise is performed with a randomized number of repetitions, ranging from 5 to 12 repetitions. Data collection is carried out with the participation of ten individuals using the Exxentric App on their smartphones. Different types of classifiers are trained using data from the Exxentric database and tested on the collected data on-site, employing the generated features. Additionally, a CNN classifier is explored, utilizing only angular velocity as input. Comparative analysis is performed on the evaluation metrics of the models. In conclusion, while achieving accurate classification for all ten exercises was not fully realized, the CNN model relying on angular velocity as input exhibited promising results. Notably, squats were predicted correctly 95% of the time, which is the most prominent observation. The model also demonstrated significant accuracy in correctly identifying bent-over rows (72%), deadlifts (72.2%), standing calf raises (70.6%), and biceps curls (67%). Further research is warranted to improve the effectiveness and accuracy of exercise classification models. This includes exploring alternative input methods and refining feature engineering techniques to advance the field. / Svänghjulsträning är en alltmer populär träningsmetod som underlättar återhämtningsprocessen och främjar styrkeutveckling samtidigt som den minskar risken för nya skador. Dessutom erbjuder automatisk träningsklassificering idrottare bekvämligheten att enkelt övervaka och spåra sina träningsframsteg, vilket gör det möjligt för dem att upprätthålla konsekvens och effektivt uppnå sina träningsmål. Denna avhandling syftar till att undersöka genomförbarheten och noggrannheten hos att utveckla en maskininlärningsmodell för att klassificera övningar som utförs på Exxentric kBox-maskiner. Målet är att bedöma modellens noggrannhet och avgöra om funktionerna som tillhandahålls av Exxentric-appen är tillräckliga för att konstruera en robust klassificerare. För att lägga en stark grund för undersökningen inleds forskningen med en omfattande litteraturgenomgång av studier om igenkänning av övningar. Därefter genomförs en explorativ dataanalys för att få värdefulla insikter om egenskaperna hos övningsdatan. Dataförberedelsen innefattar olika tekniker såsom rengöring, funktionsteknik, skalning, provtagning och kodning för att optimera datan för modellering. Dessutom används signalbehandlingstekniker för att extrahera relevanta egenskaper från övningsdatan. En testprotokoll etableras, bestående av två uppsättningar med tio övningar. Varje övning utförs med ett slumpmässigt antal repetitioner, från 5 till 12 repetitioner. Insamlingen av data utförs med deltagande av tio individer som använder Exxentric-appen på sina smartphones. Olika typer av klassificerare tränas med hjälp av data från Exxentricdatabasen och testas på den insamlade datan på plats genom att använda de genererade egenskaperna. Dessutom undersöks en CNN-klassificerare som enbart använder vinkelhastighet som indata. En jämförande analys utförs på utvärderingsmåtten för modellerna. Slutsatsen är att även om det inte var möjligt att uppnå en korrekt klassificering för alla tio övningar, uppvisade CNN-modellen, med enbart vinkelhastighet som indata, lovande resultat. Noterbart är att knäböjningar korrekt förutsades 95% av tiden, vilket är den mest framträdande observationen. Modellen visade även betydande noggrannhet vid korrekt identifiering av stående rodd (72%), marklyft (72,2%), stående vadpress (70,6%) och bicepscurls (67%). Ytterligare forskning motiveras för att förbättra effektiviteten och noggrannheten hos modeller för klassificering av övningar. Detta inkluderar att utforska alternativa metoder för indata och att förbättra teknikerna för funktionsteknik för att vidareutveckla området.

Proteins and their intricate network of interactions are fundamental to many molecular processes that govern life. Mass spectrometry-based structural proteomics represents a powerful set of techniques for characterizing protein structures and interactions. The last decade has witnessed a large-scale adoption in the application of these techniques toward solving a variety of biological questions. Addressing these questions has often been coincident with the further development of these techniques. Insight into the structures of individual proteins and their interactions with other proteins in a proteome-wide context has been made possible by recent developments in the relatively new field of chemical crosslinking combined with mass spectrometry. In these experiments crosslinking reagents are used to capture protein-protein interactions by forming covalent linkages between proximal amino acid residues. The crosslinked proteins are then enzymatically digested into peptides, and the covalently-coupled crosslinked peptides are identified by mass spectrometry. These identified crosslinked peptides thus provide evidence of interacting regions within or between proteins. In this dissertation the development of tools and methods that facilitate this powerful technique are described. The primary arc of this work follows the development and application of mass spectrometry-based approaches for the identification of protein crosslinks ranging from those which exist endogenously to those which are introduced synthetically. Firstly, the development of a novel strategy for comprehensive determination of naturally occurring protein crosslinks in the form of disulfide bonds is described. Secondly, the application of crosslinking reagents to create synthetic crosslinks in proteins coupled with molecular dynamics simulations is explored in order to structurally characterize the intrinsically disordered tau protein. Thirdly, improvements to a crosslinking-mass spectrometry method for defining a protein-protein interactome in a complex sample is developed. Altogether, these described approaches represent a toolset to allow researchers to access information about protein structure and interactions. / Graduate