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“Diagnosis of Diseases Affecting Gait with Body Acceleration-Based Machine Learning Models”

Architectural Engineering Ph.D. Dissertation Defense Presentation by Mohammad Ali Takallou

1:00 pm
Peter Kiewit Institute Room: 261
1110 S. 67th Street
Omaha, NE 68182
Target Audiences:
The Durham School of Architectural Engineering and Construction,
Advised by Dr. Fadi Alsaleem

This dissertation demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. The study initially achieved high accuracy in distinguishing PAD patients from non-PAD controls using raw marker data from the sacrum. However, when validated with data from a wearable accelerometer at the waist, accuracy significantly decreased. To improve accuracy, the model was enhanced by extracting features from acceleration data, resulting in a more robust performance when validated with wearable accelerometer data.

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