Expanding Reasearch Partnerships Webinar Series
Presentation Date: May 16, 2018
Andrew Cecala, MBA – NIOSH
A Video Exposure Monitoring (VEM) technique, named “Helmet-CAM” by NIOSH, is a proven exposure assessment tool for determining workers’ exposure to respirable (silica) dust and other contaminants throughout the workday. Workers’ exposure to respirable dust, especially dust containing crystalline silica, has long been a serious concern for the health of our nation’s miners and other workers. In addition to the development of silicosis, the negative health effects of respirable silica dust include lung cancer, pulmonary tuberculosis, and airway diseases, and are related to the development of autoimmune disorders, chronic renal disease, and other health disorders. The VEM (Helmet-CAM) exposure assessment system provides insight into how, when, and where workers are being exposed to various contaminants. This is accomplished by integrating a person-wearable video recorder with a real-time data-logging aerosol monitor. Part of the Helmet-CAM system is a NIOSH-developed software (called “EVADE”) which provides an easy-to-use interface for synchronizing playback of recorded video and dust exposure data. This technology allows for accurate and efficient identification and assessment of key work areas and/or processes that significantly impact a worker’s respirable dust exposure. Helmet-CAM is particularly useful for mobile workers who are required to perform a range of tasks in multiple areas of an operation. Beyond respirable dust exposure, Helmet-CAM was recently expanded to assess exposure to other contaminant types, such as diesel, noise, and chemicals. The EVADE software was also updated to allow for multiple contaminant assessments and cameras to be used and viewed simultaneously. Another aspect of this research program is within the human factors discipline to help enhance worker-technology-management interactions—specifically, determining ways that management can use Helmet-CAM to communicate specific work practices that support proactive health and safety behavior, and to identify generalized optimal techniques to reduce exposures.
Robert Keefe, PhD– University of Idaho
Logging continues to rank among the most dangerous professions in the United States. Cable logging is particularly hazardous because ground workers interact with heavy equipment on difficult terrain and often work in poor visibility conditions. New positioning technologies that integrate the Global Navigation Satellite System (GNSS) with radio transmission (RF) make it possible to map the locations of equipment and ground workers in real-time on mobile devices to help improve situational awareness. Because GNSS-RF systems work independently of WIFI or cellular signals, this technology is well suited to safety applications in remote, forested areas. However, the GNSS and RF system components are affected in different ways by forest vegetation, topography and line-of-sight obstructions. The accuracy of GNSS positions also varies among technology providers and is affected by the transmission rate of worker positions. In this presentation, I summarize the state of the science based on results from several designed field experiments and sampling efforts in which we have deployed GNSS-RF in forested conditions, both to improve general situational awareness and to define fine-resolution hazardous work areas delineated by geofences around equipment and other site hazards. Among other findings, our results have led to a useful interpretation of Type I and Type II error when using location-based technology services for safety applications, which extends beyond our project and into other sectors. Based on synthesis of current field and survey results, draft safety recommendations have been developed and are being adapted based on the findings of a survey characterizing regional logging contractor perspectives on use of the technology. Looking ahead to additional field sampling on active logging operations and the final educational phase of the project, continued emphasis on development and use of platform-independent solutions and applications is critical for ensuring that our study results are translated most widely into practice.