This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Description
This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize

This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize a decent basketball shot pattern? - by introducing a supervised learning paradigm, where the ML method takes acceleration attributes to predict the basketball shot efficiency. The solution presented in this study considers motion capture devices configuration on the right upper limb with a sole motion sensor made by BNO080 and ESP32 attached on the right wrist, right forearm, and right shoulder, respectively, By observing the rate of speed changing in the shooting movement and comparing their performance, ML models that apply K-Nearest Neighbor, and Decision Tree algorithm, conclude the best range of acceleration that different spots on the arm should implement.
ContributorsLiang, Chengxu (Author) / Ingalls, Todd (Thesis advisor) / Turaga, Pavan (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Nearly one percent of the population over 65 years of age is living with Parkinson’s disease (PD) and this population worldwide is projected to be approximately nine million by 2030. PD is a progressive neurological disease characterized by both motor and cognitive impairments. One of the most serious challenges for

Nearly one percent of the population over 65 years of age is living with Parkinson’s disease (PD) and this population worldwide is projected to be approximately nine million by 2030. PD is a progressive neurological disease characterized by both motor and cognitive impairments. One of the most serious challenges for an individual as the disease progresses is the increasing severity of gait and posture impairments since they result in debilitating conditions such as freezing of gait, increased likelihood of falls, and poor quality of life. Although dopaminergic therapy and deep brain stimulation are generally effective, they often fail to improve gait and posture deficits. Several recent studies have employed real-time feedback (RTF) of gait parameters to improve walking patterns in PD. In earlier work, results from the investigation of the effects of RTF of step length and back angle during treadmill walking demonstrated that people with PD could follow the feedback and utilize it to modulate movements favorably in a manner that transferred, at least acutely, to overground walking. In this work, recent advances in wearable technologies were leveraged to develop a wearable real-time feedback (WRTF) system that can monitor and evaluate movements and provide feedback during daily activities that involve overground walking. Specifically, this work addressed the challenges of obtaining accurate gait and posture measures from wearable sensors in real-time and providing auditory feedback on the calculated real-time measures for rehabilitation. An algorithm was developed to calculate gait and posture variables from wearable sensor measurements, which were then validated against gold-standard measurements. The WRTF system calculates these measures and provides auditory feedback in real-time. The WRTF system was evaluated as a potential rehabilitation tool for use by people with mild to moderate PD. Results from the study indicated that the system can accurately measure step length and back angle, and that subjects could respond to real-time auditory feedback in a manner that improved their step length and uprightness. These improvements were exhibited while using the system that provided feedback and were sustained in subsequent trials immediately thereafter in which subjects walked without receiving feedback from the system.
ContributorsMuthukrishnan, Niveditha (Author) / Abbas, James (Thesis advisor) / Krishnamurthi, Narayanan (Thesis advisor) / Shill, Holly A (Committee member) / Honeycutt, Claire (Committee member) / Turaga, Pavan (Committee member) / Ingalls, Todd (Committee member) / Arizona State University (Publisher)
Created2022