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.

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.

The current push towards integrating new digital fabrication techniques into all parts of daily life has raised concerns about the changing role of the craftsperson in creative making. The goal of this dissertation is to gain insight into how new technologies can be incorporated into creative practices in a way that effectively supports the goals and workflows of practitioners. To do so, I explore three different cases in which 3D printing, a tool by which complex 3D objects are fabricated from digital designs, is used in tandem with traditional creative practices. Each project focuses on a different way to incorporate 3D printed objects, whether it be as a visualization for artists’ processes, a substitute medium for finished artworks, or as a step toward a larger fabrication workflow. Through this research, I discover how the integration of 3D printing affects creative processes, explore how these changes influence how and why practitioners engage in artistic practices, and gain insight into directions for future technological innovations.

Modern audio datasets and machine learning software tools have given researchers a deep understanding into Music Information Retrieval (MIR) applications. In this paper, we investigate the accuracy and viability of using a machine learning based approach to perform music genre recognition using the Free Music Archive (FMA) dataset. We compare the classification accuracy of popular machine learning models, implement various tuning techniques including principal components analysis (PCA), as well as provide an analysis of the effect of feature space noise on classification accuracy.

We propose the Bio-HCI framework, that focuses on three major components: biological materials, intermediate platforms, and interaction with the user. In this context, "biological materials" is meant to broadly cover biological matter (DNA, RNA, enzyme), biological information (gene, epigenetic), biological process (mutation, reproduction, self assembling), and biological form. These biological materials serve as the design elements for designers to use in the same way as digital materials. Intermediate Platform focuses on methods of connecting biological materials to a user, or a digital platform that connect to users. In most current use-cases, biological materials need an intermediate platform to transfer the information to the user and transfer the user's response back to biological materials. Examples include a DNA sequencer, microscope, or petri dish. User interaction emphasizes the interactivity between a user and the biological machine (biological materials + intermediate platform). The interaction ranges from a basic human-computer interaction such as using a biological machine as a file storage to a unique interaction such as having a biological machine that evolves to solve user's task. To examine this framework further, we present four experiments which focus on the different aspect of the Bio-HCI framework.

This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.