Principal Investigator: Sang Hyeon Kang, Assistant Professor, Western Michigan University

Low back disorders remain a significant challenge in patient handling tasks that require high force and awkward trunk postures. While wearable passive back-support exoskeletons have shown potential in reducing low back stress during bed-to-chair patient handling tasks, the effectiveness of back-support exoskeletons in “ground-level patient handling”—a common activity in emergency medical services (EMS)—remains underexplored. Ground-level handling presents unique biomechanical challenges compared to bed or wheelchair transfers because passive lumbar tissues play a dominant role in resisting trunk extension moments. This proposal will evaluate the impact of two types of passive back-support exoskeletons (rigid and soft) during three ground-level patient handling scenarios. A lumbar biomechanical model will be employed to calculate lumbar tissue loads, spinal reaction forces, and internal extensor moments, while perceived exertion, mobility, comfort, and usability will be collected to complement objective findings. The successful outcomes of this proposed study will be quantitative/qualitative evidence of benefits (or potential limitations) of implementing passive back-support exoskeletons in EMS contexts. This proposal will enhance our understanding of individual lumbar tissue loads in ground-level patient handling and inform the development of wearable support systems that promote injury prevention and improve user perception.

Research Trainee: Subin Han, PhD Student, Purdue University

Principal Investigator: Jae Hong Park, Associate Professor, Purdue University

The overall goal of this research project is to develop a low-cost sampler for the rapid detection of airborne
viruses in the workplace. Exposure to airborne viruses poses a significant health risk to workers in various
settings. These health risks include infections, respiratory illnesses, and allergic reactions. To protect workers’
health, exposure assessment is essential and requires appropriate methods. Conventional methods for sampling
airborne viruses involve collecting samples for later laboratory analysis, such as polymerase chain reaction (PCR)
for identification. These methods are time-consuming and require specialized equipment, unsuitable for rapid
on-site virus detection in workplaces. This research proposes a low-cost, single-use sampler combined with an
immunochromatographic assay for rapid detection of airborne viruses in workplaces. The proposed method aims
to address the limitations of conventional methods by offering on-site testing, cost-effectiveness, and ease of
use. The central hypothesis is that a single-use sampler combined with an immunochromatographic assay can
rapidly detect target viruses in workplaces. To validate the hypothesis, this study has two Specific Aims: 1) to

determine the limit of detection of test kits used in the immunochromatographic assay and 2) to develop a low-
cost personal bioaerosol sampler. For Specific Aim 1, the detection limits of three different commercially available

lateral flow kits will be determined using the target virus, which will be inactivated influenza A. For Specific Aim
2, a bioaerosol sampler will be designed for collecting viruses. The sampler will utilize an inertial impactor to
collect particles onto a collection area, either a swab or a substrate. The collection efficiency of the developed
sampler will be evaluated, and the cut-off diameter will be determined. Furthermore, the performance of the
sampler will be evaluated through the sampling and detection of airborne influenza A. The proposed research
will address the NORA sector programs of “Healthcare and Social Assistance” and the cross-sector program of
“Immune, Infectious, and Dermal Disease Prevention.” By enhancing exposure assessment methodologies for
airborne viruses in workplaces, this study aims to safeguard workers within the HHS Region V from infectious
diseases. Furthermore, this framework empowers policymakers with on-site information, facilitating targeted and
efficacious interventions.

Research Trainee/Principal Investigator: Michael Yermakov, University of Cincinnati

Mentor: Jun Wang, Associate Professor, University of Cincinnati

Firefighters are routinely exposed to hazardous airborne contaminants, including particulate matter (PM), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs), generated from the combustion of organic and synthetic materials during structural fires. Despite the use of National Fire Protection Association (NFPA)-certified personal protective equipment (PPE), recent studies have demonstrated the presence of these toxicants inside firefighter turnout gear, indicating compromised protection due to gear design limitations, thermal degradation, and operational stress. Currently, no field-deployable technology exists to quantify the real-time protective performance of firefighter PPE during fire responses. This project aims to address this critical gap by developing a wearable, real-time monitoring system that continuously measures concentrations of PM and VOCs inside and outside the PPE ensemble to calculate the Workplace Protection Factor (WPF) in real-time. This proof-of-concept system will consist of two magnetically coupled sensor pockets—one mounted externally on the PPE to monitor ambient contaminants, and one inside to assess in-gear exposure levels. Each unit will contain a miniaturized low-cost PM sensor and a photoionization detector (PID)-based VOC sensor. The system will generate real-time WPF values and can be integrated with a visual color-coded alert to notify the wearer when protection is compromised.

The proposed project includes two specific aims. Aim 1 will identify, test, and calibrate commercially available low-cost PM and VOC sensors under controlled laboratory conditions mimicking the high heat, humidity, and particulate/gaseous environments typical of fireground exposure. Sensors will be benchmarked against reference-grade instruments (e.g., optical particle counters and calibrated PIDs) using a smoke generation chamber and combustion of typical structural fire materials such as treated
wood, plastics, and paper. Calibration algorithms will be developed to account for environmental interferences and improve measurement accuracy. Aim 2 will evaluate the performance of the real-time monitoring system in a manikin-based simulation. A full-body manikin wearing turnout gear and connected to a Breathing Recording and Simulation System (BRSS) will be exposed to controlled combustion byproducts inside a test chamber. Simulated workloads (mean inspiratory flows of 30, 85, and 135 L/min) will allow testing under moderate to strenuous breathing conditions. Simultaneous measurement of pollutant concentrations inside and outside the PPE will enable dynamic WPF calculation across varying environmental and physiological conditions.

The significance of this project lies in its novel integration of real-time sensors into PPE performance evaluation, which aligns with NIOSH and NORA Public Safety Council objectives to enhance surveillance of occupational exposures and develop improved protective technologies for first responders. This pilot study will provide foundational data and a validated prototype for future field deployment and scale-up. The proposed work will contribute to the development of responsive, evidence-based strategies to improve firefighter health and safety, particularly in underserved and high-risk regions such as HHS Region V.

Research Trainee: Jing Kun Wang, PhD Student, Purdue University

Principal Investigator: Denny Yu, Associate Professor, Purdue University 

Burnout, depression, and suicide among healthcare professionals are urgent occupational health challenges that align closely with the Total Worker Health (TWH) framework, which promotes integrated strategies to protect and advance worker well-being. Surgical teams are particularly vulnerable due to the intense cognitive, emotional, and interpersonal demands of their clinical environment. For example, 40% to 60% of orthopedic surgeons, 31% of nurses, and 59% of anesthesiologists report experiencing or being at high risk of burnout. These psychological stressors can compromise both healthcare worker well-being and patient safety. Yet early detection remains difficult, as current tools for assessing mental health rely on retrospective self-report questionnaires that are limited in capturing moment-to-moment psychological strain.

This proposed research addresses two critical gaps: (1) the lack of communication-based indicators of burnout and psychological distress in clinical teams, and (2) the absence of predictive models that can detect emerging mental health risk from naturalistic, in-situ team interactions. We propose a novel system that continuously
analyzes team dialogue in real time to assess and predict burnout risk.
Two specific aims are proposed:
Specific Aim 1: Identify objective communication features and develop predictive models of clinically measured burnout
Specific Aim 2: Prototype a real-time prediction system for burnout using intraoperative communication

The key deliverables include: 1) a set of communication features that reflect burnout-related behaviors, and 2) a functional, real-time prediction prototype that processes audio data to generate burnout risk scores without requiring wearables or manual labeling. This work lays the groundwork for a scalable, privacy-conscious tool to support mental health monitoring in high-stress healthcare settings.

Research Trainee: Nathan Chen, Research fellow, Department of Environmental Health Sciences, University of Michigan

Principal Investigator: Rick Neitzel, Professor, Department of Environmental Health Sciences, University of Michigan

Occupational exposure to whole-body vibration (WBV) has been associated with adverse musculoskeletal health effects, especially for lower back pain, which is one of the occupational diseases increasing disability-adjusted life years. Bus drivers are exposed to WBV during their regular work. There are over 500,000 bus drivers in the
Midwest region, with an expected growth rate higher than the national average. Recently, a WBV mobile application utilizing accelerometers built into off-the-shelf smartphones to has been validated to measure vibration in mining vehicles and light vehicles. However, the validation of using the WBV mobile app to measure WBV exposure on the metropolitan buses for exposure assessment purpose remains limited. The goal of the proposed study is to evaluate occupational exposure to whole-body vibration from metropolitan bus seats in Michigan, using an innovative modern personal vibration exposure assessment tool. The WBV mobile app we propose to use to measure the WBV levels from the metropolitan bus seats is expected to reduce the time and cost of making measurements. In Aim 1, we will evaluate performance of using WBV mobile app to measure WBV levels from the bus driver seats. By comparing app results with those from WBV dosimeters, the correlation between WBV level measured from the WBV mobile app and that from the WBV dosimeters will allow us to evaluate the app’s performance. In Aim 2, we will evaluate occupational exposure to WBV from the bus driver seats using the WBV exposure level and duration measured from the mobile app to estimate daily 8-hour WBV exposure doses. The proposed study will fill the large WBV exposure-related knowledge gap using low-cost sensors for heavy duty vehicles in the Midwest region of the U.S. The WBV mobile app validated in a heavy-duty bus scenario will provide insights about the utility of the WBV mobile app as a regular monitoring system to protect occupation drivers from the WBV hazard.

Research Trainee: Abas Shkembi, PhD Student, Department of Environmental Health Sciences, University of Michigan

Principal Investigator: Rick Neitzel, Professor, Department of Environmental Health Sciences, University of Michigan

Exposures in the workplace, such as carcinogens and noise, cause hundreds of thousands of cases of disease, illness, and thousands of deaths every year. The most highly exposed workers in the US may be the same individuals also highly exposed to pollutants at their homes and communities. This leads to a situation where some individuals may be cumulatively burdened by excessive exposures both in and out of work. However, a critical research gap remains in understanding the full extent of cumulative occupational and environmental exposures. Without this knowledge, interventions to improve occupational health in the US may be ineffective. The primary objective of this pilot proposal is to determine the feasibility of identifying communities whose residents work at jobs with the highest workplace exposures and gather preliminary evidence on whether these are also communities with the highest environmental exposures. This will be done by developing ten novel measures of workplace exposure for all US neighborhoods from 2010-2019 (Aim 1). Drawing on several publicly available data sources on employment, job-exposure matrices, and geospatial environmental data, we will estimate the annual percent of the workforce exposed to various chemical, physical, biological, safety, and psychosocial hazards for every census tract. This will be first time that comprehensive, nationwide, census tract-level estimates of hazardous work exposure will be constructed, guided by the Total Worker Health framework. We will then identify whether there are communities with cumulatively high occupational and environmental exposures using an unsupervised machine learning algorithm (Aim 2). The proposed project closely aligns with the National Occupational Research Agenda across multiple sectors regarding improved occupational surveillance, particularly in Construction, Agriculture, Forestry, and Fishing, and Public Safety. In the long-term, successful completion of this project is expected to guide US occupational and environmental regulations and policies towards an approach that improves worker health both in out of the workplace.