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Automated manufacturing environments often benefit greatly from the ability to detect patterns that deviate from expected behavior. Anomaly Detection (AD) is vital in automated manufacturing to mitigate risks such as production delays, defects, and safety hazards, ensuring smooth operations and optimal productivity. AD tasks are commonly tackled using Machine Learning (ML). However, large feature sets are computationally expensive, potentially noisy and may make it challenging to understand the important factors driving the manufacturing process. To address these problems, feature selection methods are utilized. Feature selection is a technique which becomes increasingly important as high-dimensional data becomes more prevalent. In this study, our objective is to investigate how the performance of ML models trained on the Modular Ice cream Dataset on Anomalies in Sensors dataset (MIDAS) is influenced by the application of feature selection techniques. We evaluated the feature selection methods Variance Threshold (VT), F-test, χ2-test, Mutual In-formation (MI), Genetic Algorithm (GA) and Forward Selection (FS). The results showed that MI outperforms the other methods with respect to model accuracy, feature selection time and training time in Anomaly Classification (AC), but is slightly outperformed on accuracy in AD by FS. These results provide insights about feature selection methods for AD in automated manufacturing environments.

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Right-heart catheterization is the most accurate method for estimating pulmonary artery pressure (PAP). Because it is an invasive procedure it is expensive, exposes patients to the risk of infection, and is not suited for long-term monitoring situations. Medical researchers have shown that PAP influences the characteristics of heart sounds. This suggests that heart sound analysis is a potential noninvasive solution to the PAP estimation problem. This thesis describes the development of a prototype system, called PAPEr, which estimates PAP noninvasively using heart sound analysis. PAPEr uses patient data with machine learning algorithms to build models of how PAP affects heart sounds. Data from 20 patients was used to build the models and data from another 31 patients was used as a validation set. PAPEr diagnosed these 31 patients for pulmonary hypertension with an accuracy of 77 percent.

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Background: Movement screens are frequently used to identify abnormal movement patterns that may increase risk of injury and/or hinder performance. However, abnormal patterns are often detected visually based on the observations of a coach or clinician leading to poor inter- and intrarater reliability. In addition, they have been criticized for having poor validity and sensitivity. Quantitative, or data-driven methods can increase objectivity, remove issues related to inter-rater reliability and offer the potential to detect new and important features that may not be observable by the human eye. The combination of motion capture data, pattern recognition and machine learning could provide a quantitative method to better assess movement competency. Purpose: The purpose of this doctoral thesis was to create the foundation for the development of an objective movement screening tool that combines motion capture data, pattern recognition and machine learning. This doctoral thesis is part of a larger project to bring an objective movement screening tool for use in the field to market. Methods: This thesis is comprised of four studies based on a single data collection and a common series of pre-processing steps. Data from 542 athletes were collected by Motus Global, a for-profit biomechanics company, with athletes ranging in competition level from youth to professional and competing in a wide-range of sports. For the first study of this thesis, an online software program was developed to examine the inter- and intra-reliability of a movement screen, with intrareliability being further examined to compare reliability when body-shape was and was not modified. The second study developed the objective movement screen framework that utilized motion capture, pattern recognition and machine learning. Study 3 and 4 assessed different types of input data, classification goals (e.g., skill level and sport played), feature reduction and selection methods, and increasingly complex machine learning algorithms. Results: For Study 1, when looking at inter- and intra-rater reliability of expert assessors during subjective scoring of movements, intra-rater reliability was better than inter-rater reliability. When assessing the effects of body-shape, on average, reliability worsened when body-shape was manipulated. Study 2 provided proof-of-principle that athletes were able to be classified based on skill level using marker-based optical motion capture data, principal component analysis (PCA) and linear discriminant analysis. For Study 3, PCA in combination with linear classifiers outperformed non-linear classifiers when classifying athletes based on skill level; feature selection increased classification rates, and classification rates when using simulated inertial measurement unit data as the input data were on average better than when using marker-based optical motion capture data. In Study 4, athletes were able to be differentiated based on sport played and recurrent neural nets (RNNs) and PCA in combination with traditional linear classifiers were the optimal machine learning algorithms when classifying athletes based on skill level and sport played. Conclusion: This thesis demonstrates that objective methods can differentiate athletes based on desired demographics using motion capture, pattern recognition and machine learning. This thesis is part of a larger project to bring an objective movement screening tool for field-use to market and provides a solid foundation to use in the continued development of an objective movement screening tool.

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