Vedant's Journey

Abstract: As large-scale machine learning models become more prevalent in assistive and pervasive technologies, the research community has started examining limitations and challenges that arise from training data, e.g., fairness, bias, and interpretability issues. To this end, data-centric approaches are increasingly prevailing over time, showing that high-quality data is a critical component in many applications. Several studies explore methods to define and improve data quality, however, no uniform definition exists. In this work, we present an empirical analysis of the multifaceted problem of evaluating data quality. Our work aims at identifying data quality challenges that are most commonly observed by data users and practitioners. Inspired by the need for generally applicable methods, we select a representative set of quality indicators, that covers a broad spectrum of issues, and investigate the utility of these indicators on a broad range of datasets through inter-annotator agreement analysis. Our work provides insights and presents open challenges in designing improved data life cycles.

Abstract: A mechanical insect wing station has been constructed to understand the underlying fluid dynamic principles which guide the circulation of hemolymph in an insect wing. The frequency at which the wing flaps depends on the voltage fed to the motor and on the inertial properties of the wing. Using machine learning (ML) to perform regression, estimating the values of dependent variables based on independent variables, is a well-studied task both in academia and industry. While many generic regression models are available, we wanted to be able to calibrate the apparatus for new wings using only sparse data. A custom regression model was built using Tensorflow with the adaptive moment estimation algorithm (Adam) as the optimization function and Huber as the loss function to learn the relationship between the voltage and frequency of specific insect wing models fabricated with stereolithography resin printers. Datasets were prepared by manually tracking the flapping wing in video data in order to find the flapping frequency in Hertz and the voltage from the power source in Volts at that frequency. The mean absolute percentage error is used to measure the accuracy of the prediction. The model predicts voltage based on input frequency with a 94% success rate. Automating this manual task with an ML model improves the accuracy of values. This is essential to improve data accuracy in the insect wing hemodynamics experiments which has a broader application to downstream tasks such as developing systems for drug delivery and other applications in human health.

Abstract: Over 500 million people are affected by diabetes worldwide. For insulin-dependent patients, treatments include bulky and inconvenient battery-powered insulin pumps and painful syringe injections. The InsulPatch is an alternative, smaller-scale insulin delivery pump currently under development. It provides an inexpensive way to administer insulin easily, painlessly, and without a power source, making insulin delivery more convenient for Type 1 diabetics. Inspired by the insect respiratory system, the InsulPatch features a multilayer microfluidic pump system. It uses the wearer’s radial pulse to pump insulin transdermally via a microneedle array. Photolithography and stereolithography (SLA) 3D printing microfabrication techniques were used to create device design molds. The molds were then used to create the InsulPatch device layers by pouring liquid polydimethylsiloxane (PDMS) into the molds and baking to cure the PDMS. The devices contain three layers: a top layer consisting of actuation channels, a thin middle membrane, and a bottom layer containing the flow channels. Flow rate data was collected at different actuation pressures and frequencies for different flow and actuation channel geometric design parameters. The devices were actuated using pressurized air signals representing different blood pressures and heart rates. The flow rate data was collected and analyzed using Graphpad Prism. This data was then used to create a sequential regression machine learning model with 80% prediction accuracy, enabling the solution of the inverse problem of producing device designs for specific patients. After successful clinical trials, the InsulPatch will allow for accessible, painless, and convenient insulin delivery.

Abstract: 1 in 4 women experience intimate partner violence. Safety Tank, is a simple tank top with a detachable device created to detect unusual linear and rotational accelerations associated with interpersonal violence. By utilizing an accelerometer and gyroscope, the device can accurately monitor changes in motion, including forceful falls. When a concerning event occurs, Safety Tank generates alerts that are immediately sent to predetermined contacts, such as healthcare providers, intervention teams, and trusted friends.

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