DSpace Repository :: Browsing by Author "Jeong, Nathan"

Browsing by Author "Jeong, Nathan"

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Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique
(MDPI, 2021) Choffin, Zachary; Jeong, Nathan; Callihan, Michael; Olmstead, Savannah; Sazonov, Edward; Thakral, Sarah; Getchell, Camilee; Lombardi, Vito; University of Alabama Tuscaloosa
Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.
A Compact Ultra-Wideband Monocone Antenna with Folded Shorting Wires for Vehicle-to-Everything (V2X) Applications
(MDPI, 2023) Lee, Martin Wooseop; Abushakra, Feras; Choffin, Zachary; Kim, Sangkil; Lee, Hee-Jo; Jeong, Nathan; University of Alabama Tuscaloosa; Pusan National University; Daegu University
In this paper, a capacitively-fed, ultra-wideband (UWB), and low-profile monocone antenna is proposed for vehicle-to-everything (V2X) applications. The proposed antenna consists of a monocone design with an inner set of vias. Additionally, an outer ring is added with a small gap from the monocone and shorted with six folded wires of different lengths to extend the operating band. The proposed antenna covers the frequency range from 0.75 GHz to 7.6 GHz and has a 164% fractional bandwidth, with a gain value varying between 2 and 10 dBi. The dimensions of the antenna are 0.37?(L) x 0.37?(L) x 0.067?(L). The antenna was fabricated using a 3D printer with low-cost polylactic acid plastic (PLA) material and then sprayed with aerosol copper nanoparticles. The efficiency was approximately 90% throughout the frequency bands of interest. Finally, the proposed antenna was installed on a vehicle and tested with an OBU (onboard unit) and a RSU (roadside unit) in the field. The results show a longer wireless communication range for V2X applications.
Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning
(MDPI, 2022) Anderson, Wolfe; Choffin, Zachary; Jeong, Nathan; Callihan, Michael; Jeong, Seongcheol; Sazonov, Edward; University of Alabama Tuscaloosa; Pohang University of Science & Technology (POSTECH)
This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.
Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics
(MDPI, 2023) Choffin, Zachary; Jeong, Nathan; Callihan, Michael; Sazonov, Edward; Jeong, Seongcheol; University of Alabama Tuscaloosa; Pohang University of Science & Technology (POSTECH)
Extreme angles in lower body joints may adversely increase the risk of injury to joints. These injuries are common in the workplace and cause persistent pain and significant financial losses to people and companies. The purpose of this study was to predict lower body joint angles from the ankle to the lumbosacral joint (L5S1) by measuring plantar pressures in shoes. Joint angle prediction was aided by a designed footwear sensor consisting of six force-sensing resistors (FSR) and a microcontroller fitted with Bluetooth LE sensors. An Xsens motion capture system was utilized as a ground truth validation measuring 3D joint angles. Thirty-seven human subjects were tested squatting in an IRB-approved study. The Gaussian Process Regression (GPR) linear regression algorithm was used to create a progressive model that predicted the angles of ankle, knee, hip, and L5S1. The footwear sensor showed a promising root mean square error (RMSE) for each joint. The L5S1 angle was predicted to be RMSE of 0.21 degrees for the X-axis and 0.22 degrees for the Y-axis, respectively. This result confirmed that the proposed plantar sensor system had the capability to predict and monitor lower body joint angles for potential injury prevention and training of occupational workers.
Machine Learning-Driven Intelligent Shoe-Based Wearable System for Human Health Enhancement
(University of Alabama Libraries, 2024) Choffin, Zachary Michael; Jeong, Nathan
In the last few decades, biomechanical research has made significant advancements in understanding human movement and developing techniques to improve individuals' everyday life. However, risks such as falls, musculoskeletal injuries, and improper biomechanics are still prevalent across all age groups and populations. While research has expanded the understanding of these risks, there is an ever-growing need for prevention strategies that can be seamlessly integrated into daily life. The development of intelligent wearable shoe sensor systems can open the door to transform the way we approach biomedical health and movement analysis. These discreet, unobtrusive devices can continuously monitor biomechanical data like gait patterns, joint angles, center of pressure, and balance, providing a way for individuals to mitigate risks and for doctors to have continuous data on how patients progress with treatment. This dissertation examines the development and use cases of the intelligent shoe-based wearable in biomechanical applications. It proposes an all-in-one insole capable of being discreet and low-cost to measure pressure across a user's foot and digitize this data for use in machine learning applications. A lower body joint angle detection model utilizing inverse dynamics is proposed. A method of classifying human motion and uniquely identifying individuals utilizing intelligent shoe-based wearable data is also proposed. Finally, a framework for utilizing center of pressure across the foot to correlate to a 2-dimensional center of balance is presented as an alternative to traditional IMU-based systems.
Magnetic Materials for Magnetic Storage Devices and Electromagnetic Interference Absorbers
(University of Alabama Libraries, 2022) Mshar, Alecsander; Hong, Yang-Ki; Gupta, Subhadra
This thesis consists of two parts: perpendicularly magnetic anisotropy thin films for magnetic storage devices, and electromagnetic interference (EMI) materials to protect electronic devices (components) from electromagnetic interference. First, the impacts of the patterning procedure and magnetic domain size on the magnetic characteristics of Co/Pd multilayer thin films are investigated. Multilayered Co and Pd thin films show a large perpendicular anisotropy. In order to increase the magnetic storage density and enhance the coercivity of Co/Pd films, a nanopatterning process was developed to fabricate multilayered Co/Pd nanopillars. The differences in magnetic properties between the patterned and unpatterned films were investigated using a vibrating sample magnetometer (VSM). A scanning electron microscope (SEM) was used to observe the patterned features. In addition, a study was done to minimize the nanopillar dimensions with respect to the plasma etching conditions. It was found that the coercivity of the patterned nanopillars increased by as much as 130% compared to unpatterned film, and the nanopillar diameter ranged from 15 to 30 nm. Secondly, the EMI absorption efficiency of nickel ferrite (NiFe2O4) was investigated. The nickel ferrites were synthesized by two different methods, and their magnetic and physical properties were measured by VSM, SEM, x-ray diffraction (XRD), and vector network analyzer (VNA). It was found that both the permittivity and permeability of nickel ferrite depend on the synthesis method. The synthesized nickel ferrite and polyaniline nanocomposites were prepared to measure EMI shielding effectiveness. This nanocomposite shows improved EMI shielding compared to pure nickel ferrite and opens an avenue toward flexible EMI shielding materials.
Microwave imaging for watermelon maturity determination
(Elsevier, 2023) Garvin, Joe; Abushakra, Feras; Choffin, Zachary; Shiver, Bayley; Gan, Yu; Kong, Lingyan; Jeong, Nathan; University of Alabama Tuscaloosa; Stevens Institute of Technology
Microwave imaging technology is a useful method often applied in medical diagnosis and can be used by the food industry to ensure food safety and quality. For fruit, ripeness is the primary characteristic which determines quality for the consumer. This paper proposes a novel microwave imaging system to determine the ripeness of watermelon as a proof of concept. The design employs a circular array with 10 Coplanar Vivaldi antennas offering wide bandwidth, high gain, and high efficiency. S-parameters between antennas are collected quickly via automated channel switching for fast image generation. Eight different watermelon samples of varying ripeness, type, dimensions, and origin are scanned and imaged. Comparisons with sample cross-sections show distinct differences in image characteristics based on watermelon maturity. Sugar concentration of unripe and ripe watermelon is also measured and plotted for further validation of the imaging technique.
Microwave Properties of Coplanar Waveguide-Based PEDOT:PSS Conducting Polymer Line in Ethanol Gas Atmosphere
(MDPI, 2020) Lee, Hee-Jo; Jeong, Nathan; Choi, Hyang Hee; Daegu University; University of Alabama Tuscaloosa; Yonsei University
This study aims to investigate the microwave properties of coplanar waveguide (CPW)-based poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) conducting polymer line in an ethanol gas atmosphere, with the frequency range of 0.5-2 GHz. For an ethanol-exposed PEDOT:PSS line (test sample), the transmission coefficient (S-21) decreased immediately; moreover, the microwave effective conductivity (sigma(m/w)) decreased simultaneously, compared with the ethanol-free PEDOT:PSS line (reference sample). The immediate variations in Delta S-21 ( = S-21,S-ethanol - S-21,S-free) and Delta sigma(m/w) ( = sigma(m/w,ethanol) - sigma(m/w,free)) were approximately 10.2 dB and 2.7 x 10(4) S/m, respectively. Furthermore, in the analysis of the circuit model of the PEDOT:PSS line, the characteristic impedance and distributed elements, i.e., resistance (R) and inductance (L) per length, of the test sample increased, compared with the reference sample. However, upon stopping the exposure to ethanol gas, the microwave properties of the test sample instantaneously recovered to those of the reference sample. According to these critical observations, we could confirm that the coplanar waveguide with a PEDOT:PSS line shows a significant difference in the diverse microwave properties, through rapid response to the ethanol gas at room temperature.
Miniaturized Ultra-Wideband UAV Radars and Antennas for Remote Sensing Applications
(University of Alabama Libraries, 2023) Abushakra, Feras; Jeong, Nathan
In the last few years, ultra-wideband (UWB) radars have shown great potential for remote sensing of the earth, such as measuring snow thickness, soil moisture, and the bathymetry of rivers. Unmanned Aerial Vehicles (UAVs) are an attractive alternative to manned aircraft due to their low cost and flexibility in installation and operation of ultra-wideband radars. The development of drone-based radar systems for remote sensing has been a topic of significant research over the last few years. Ultra-wideband Frequency Modulated Continuous Wave (FMCW) radars are developed in this dissertation for fine-resolution snow and soil moisture measurements. The mm-wave transmitted signal is down-converted from 77-81 GHz into 2-6 GHz and the received signal is up-converted back to the mm-wave range for digitization and processing. This approach enabled us to develop a very lightweight (<1.5 kg) and compact UWB microwave radar. The radar can be operated up to 100 m altitude for estimated flight time of more than 25 minutes in Tuscaloosa, AL to scan wide areas. The proposed radar has two different versions. The first version used a radar transmitter and receiver chains with waveguide components. This design weighs 5.5 lb (2.5 kg) while the second version is developed with printed circuit board (PCB) up-down converters to reduce the weight and size by approximately 40%. In addition, different antenna arrays are designed to support the radar. The UWB radars including the antenna arrays are tested in the lab and anechoic chamber. Also, the whole system was operated in the field at different altitudes, wind speeds, and weather conditions to measure soil moisture and snow depth in 2021, 2022, and 2023. The antenna arrays were steered with certain angles and mounted to collect data at nadir and off-nadir-incidence angles. Both focused and unfocused Synthetic Radar Aperture (SAR) processors are used to process the collected raw data from the radar system. A snow thickness map is generated for the flight area in Colorado using an automated snow tracker. The radar results and in-situ measurements are compared and they are in good agreement.
Ultra-Wideband Monocone Antenna and V2X Testing
(University of Alabama Libraries, 2022) Lee, Wooseop; Jeong, Nathan; University of Alabama Tuscaloosa
In this thesis, a capacitively-fed, ultrawide bandwidth, low profile, Omni-directional monocone antenna is proposed for V2X wireless communications. The proposed antenna consists of five main components – circular monocone, capacitive feed, grounded ring, ground post near capacitive feed, the short and long meander grounding vias. The proposed antenna is modeled with an electromagnetic simulator and validated with measurement. The results show that the proposed antenna supports ultrawide bandwidth from 0.75 GHz to 7.47 GHz mounted on a ground plane, allowing GSM, CDMA, UMTS,LTE, GPS, WiFi, BT, DSRC, and C-V2X bands. The prototype of the antenna is 3D printed with low-cost plastic material and sprayed with copper particles for rapid and cost-effective fabrication. The diameter and height of the antenna are 148 mm and 26.695 mm, respectively. The efficiency is measured to be over 87.97 % throughout the frequency bands of interest. Proceedings from antenna design and measurement, different cases of V2X testing were conducted. The Line of Sight (LOS), Non-Line of Sight (NLOS), Intersection, and three different shadowing tests were examined and evaluated with Cohda Wireless’s DSRC and C-V2X supportive units.
Velocity Profile Optimization of a Connected Vehicle Traveling Through a Series of Signalized Intersections
(University of Alabama Libraries, 2024) Douglas, Brenin; Bittle, Joshua A.; Agrawal, Ajay K.
Human drivers are limited in optimizing driving due to a lack of information about their surroundings, such as other vehicles' trajectories and future intersection signals and timings. With connected and automated vehicles (CAVs), this information becomes available, enabling driving cycle optimization, which has the potential to save time, money, and energy. A significant application of CAVs is navigating through signalized intersections and corridors by prescribing an optimal speed trajectory that avoids stopping at 'red' signals and thereby maximizes efficiency by avoiding unnecessary idling and re-acceleration. While numerical solvers on multi-objective cost functions are commonly used to handle the complexities of velocity optimization, this paper presents an entirely analytical solution that is built on simple models for vehicle dynamics, engine control,the transmission, and an engine efficiency map for estimating fuel consumption. Though not as comprehensive as some numerical methods, the analytical approach is fully interpretable, adaptable to any vehicle powertrain (electric, hybrid, or internal combustion engine (ICE)),and can be optimized for computational efficiency, which are all critical considerations foronboard CAV applications. Additionally, velocity trajectories in this algorithm areconstructed using engine torque request as the control variable, instead of the commonlyused acceleration control variable, to ensure the optimal trajectory remains within dynamic engine operating limits.The proposed algorithm is tested on a sample network under various EGO vehicle initial conditions and signal phase scenarios, demonstrating that following the optimal trajectory can reduce fuel consumption compared to other valid trajectories. As a simple analytical algorithm focused on feasibility for the given powertrain (ICE in this case) and as a real-time application, the proposed solution can be expanded to handle additional constraints like interfering traffic or used in conjunction with a numerical solver for increasingly complex scenarios.

Browsing by Author "Jeong, Nathan"

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