Theses and dissertations

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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The design and evaluation of a kinect-based postural symmetry assessment and training system

Description
The increased risk of falling and the worse ability to perform other daily physical activities in the elderly cause concern about monitoring and correcting basic everyday movement. In this thesis, a Kinect-based system was designed to assess one of the most important factors in balance control of human body when…

The increased risk of falling and the worse ability to perform other daily physical activities in the elderly cause concern about monitoring and correcting basic everyday movement. In this thesis, a Kinect-based system was designed to assess one of the most important factors in balance control of human body when doing Sit-to-Stand (STS) movement: the postural symmetry in mediolateral direction. A symmetry score, calculated by the data obtained from a Kinect RGB-D camera, was proposed to reflect the mediolateral postural symmetry degree and was used to drive a real-time audio feedback designed in MAX/MSP to help users adjust themselves to perform their movement in a more symmetrical way during STS. The symmetry score was verified by calculating the Spearman correlation coefficient with the data obtained from Inertial Measurement Unit (IMU) sensor and got an average value at 0.732. Five healthy adults, four males and one female, with normal balance abilities and with no musculoskeletal disorders, were selected to participate in the experiment and the results showed that the low-cost Kinect-based system has the potential to train users to perform a more symmetrical movement in mediolateral direction during STS movement.
ContributorsZhou, Henghao (Author) / Turaga, Pavan (Thesis advisor) / Ingalls, Todd (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2016
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Basketball Shooting Prediction Using Machine Learning Models and Motion Capture System

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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Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants

Description
Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health…

Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant.The implementation of a non-parametric method to predict the onset of brady- cardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribu- tion of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
ContributorsMitra, Sinjini (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020