Browsing by Author "Callihan, Michael"

Now showing 1 - 8 of 8
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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.
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    Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor
    (MDPI, 2023) Callihan, Michael; Cole, Heather; Stokley, Holly; Gunter, Joshua; Clamp, Kaitlyn; Martin, Alexis; Doherty, Hannah; University of Alabama Tuscaloosa
    Background: With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system. Methods: Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy. Results: High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance. Conclusions: The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.
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    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.
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    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.
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    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.
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    A Pathway to Developing the Simcomp Tool for Competency-Based Evaluation of Simulation
    (University of Alabama Libraries, 2023) Cole, Heather S; Guerra, Donna
    To meet the pressing needs of the workforce and the evolving student population, institutions of higher education must be open to curriculum adaptation. With the release of the new AACN Essentials, accredited nursing institutions are now tasked with transitioning to competency-based education for entry-level nursing students. This change to nursing curricula requires nurse educators to review, and possibly revise, educational approaches and evaluation methods for the learner with one common goal of producing competent nursing graduates. The purpose of this dissertation is to generate new knowledge regarding competency-based evaluations in the simulation setting to serve as a foundation for the development of a competency-based tool for use in the simulation setting - the SimComp Tool. The first manuscript outlines the current approach to simulation evaluation. These methods are based on scaffolding theory-based models, and student perceptions rather than objective faculty outcomes, with only half of the instruments showing validity and reliability. The second manuscript presents nursing faculty perceptions of competency-based education to 1) promote standardization across nursing programs, 2) improve patient safety, and 3) require guidance and support for faculty. Manuscript three explores expert faculty panelists' agreement on the presented domains, observable behaviors, and critical behaviors that could be assessed in a simulated-learning environment and used as a foundation for achieving entry-level nursing competence. The three manuscripts included in this dissertation were guided by Kurt Lewin's Change Theory by unfreezing current societal norms within nursing curricula by identifying the restraining forces (barriers) and driving forces (benefits) of the transition to competency-based education. The integration of the results from each article provides supporting evidence for the future development of the SimComp Tool.
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    Proof of Concept Testing of Safe Patient Handling Intervention Using Wearable Sensor Technology
    (MDPI, 2023) Callihan, Michael; Somers, Brylan; Dinesh, Dhruv; Aldred, Lauren; Clamp, Kaitlyn; Treglown, Alyssa; Custred, Cole; Porteous, Kathryn; Szukala, Emily; University of Alabama Tuscaloosa
    Background: Healthcare workers make up one of the occupations in the United States that experience the most musculoskeletal injuries. These injuries are often related to the movement and repositioning of patients. Despite previous injury prevention attempts, injury rates remain at an unsustainable level. The purpose of this proof-of-concept study is to provide preliminary testing of the impact of a lifting intervention on common biomechanical risk factors for injury during high-risk patient movements.; Methods: A before-and-after (quasi-experimental) design was utilized to compare biomechanical risk factors before and after a lifting intervention. Kinematic data were collected using the Xsens motion capture system, while muscle activations were collected with the Delsys Trigno EMG system. Results: Improvements were noted in the lever arm distance, trunk velocity, and muscle activations during the movements following the intervention; Conclusions: The contextual lifting intervention shows a positive impact on the biomechanical risk factors for musculoskeletal injury among healthcare workers without increasing the biomechanical risk. A larger, prospective study is needed to determine the intervention's ability to reduce injuries among healthcare workers.
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    Skill Acquisition in Baccalaureate Nursing Programs: Are Students Competent Upon Graduation?
    (University of Alabama Libraries, 2024) Smith, Kathy Lynn; McKnight, Douglas
    Unintentional errors and poor competency in clinical nursing skills can increase the risk of infections and delay treatment for hospitalized patients. Nursing programs often teach beginning nursing skills in the first/second semester of nursing school and yet, some students have no or very limited opportunities to perform these skills on patients in a hospital setting prior to graduation. Are these students still competent in basic nursing skills at the time of graduation? The purpose of this study was to identify the number of opportunities, nursing students had to perform three basic clinical skills and to determine if there was a correlation between the frequency of opportunities and the student's level of competence in performing these same skills, at the time of graduation. A questionnaire was utilized, and three basic clinical skills were video-taped and graded for skill competency at the end of the nursing program. Frequency and correlational methods were used to examine the data. Although the data in this study did not show a relationship between the frequency of opportunities to attempt skills during nursing school with the competence of students to perform those same skills at the time of graduation, important data was obtained from this study. The number of times nursing students had the opportunity to practice basic nursing skills in the hospital setting was identified by the data and in which clinical courses this occurred. Clinical competence in three basic nursing skills was determined at the time of graduation and although the results were discouraging, in gathering this type of data, modification of nursing course activities and clinical sites can occur to maximize critical clinical experiences for nursing students. As a result of this process, nursing programs can hopefully improve student competency, safety, and outcomes in performing basic clinical skills at the time of graduation.Keywords: Competency in nursing clinical skills, opportunity to practice clinical nursing skills, nursing skills, clinical nursing, nursing education