Human Factors Affecting Logging Injury Incidents in Idaho and the Potential for Real-Time Location-Sharing Technology to Improve Safety

Digital Innovations for Occupational Safety: Empowering Workers in Hazardous Environments

BACKGROUND

The quest to increase safety awareness, make job sites safer, and promote decent work for all has led to the utilization of digital technologies in hazardous occupations. This study investigated the use of digital innovations for safety and health management in hazardous industries. The key challenges and recommendations associated with such use were also explored.

METHOD

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a total of 48 studies were reviewed to provide a framework for future pathways for the effective implementation of these innovations.

FINDINGS

The results revealed four main categories of digital safety systems: wearable-based systems, augmented/virtual reality-based systems, artificial intelligence-based systems, and navigation-based systems. A wide range of technological, behavioral, and organizational challenges were identified in relation to the key themes.

CONCLUSION

Outcomes from this review can inform policymakers and industrial decision-makers about the application of digital innovations for best safety practices in various hazardous work conditions.

The effect of sleepiness in situation awareness: A scoping review

BACKGROUND

Situational awareness is the acquisition of information from elements present in the work environment, the perception of the meaning of this information, and the prediction of future working conditions. Sleepiness and fatigue can influence an individual's ability to reach situation awareness, decision-making, and performance on a task.

OBJECTIVE

This scoping review examines methods used to assess situational awareness, fatigue, sleepiness, and their interrelationships.

METHODS

A systematic search of online databases was conducted to identify experimental, peer-reviewed articles published in English between 2017 and 2022. A total of 29 publications were selected for analysis.

RESULTS

The selected studies originated from various countries, primarily in the northern hemisphere. Health and automotive engineering were the academic categories with the highest publications. The studies employed objective and subjective methods to assess situational awareness, fatigue, and sleepiness.

CONCLUSIONS

Most studies reported a decline in situational awareness during fatigue and sleepiness conditions, although one study did not find this association. Future research should focus on employing objective methods to analyze cognitive factors, increasing sample sizes, and conducting testing in real-world situations.

Sensor Integration in a Forestry Machine

This paper presents the integration of multimodal sensor systems for an autonomous forestry machine. The utilized technology is housed in a single enclosure which consolidates a set of components responsible for executing machine control actions and comprehending its behavior in various scenarios. This sensor box, named Sentry, will subsequently be connected to a forestry machine from MDB, model LV600 PRO. The article outlines previous work in this field and then details the integration and operation of the equipment, integrated into the forest machine, providing descriptions of the adopted architecture at both the hardware and software levels. The gathered data enables the assessment of the forestry machine's orientation and position based on the information collected by the sensors. Finally, practical experiments are presented to demonstrate the system's behavior and to analyze the methods to be employed for autonomous navigation, thereby assessing the performance of the established architecture. The novel aspects of this work include the physical and digital integration of a multimodal sensor system on a forestry machine, its use in a real case scenario, namely, forest vegetation removal, and the strategies adopted to improve the machine localization and navigation performance on unstructured environments.

Factors associated with injury among Maine logging workers

INTRODUCTION

Despite dramatic improvements in safety, logging remains one of the most dangerous industries in the United States. The purpose of this study was to explore longitudinal injury trends among Maine logging workers.

METHODS

Loggers participated in seven quarterly surveys, over the course of 18 months. Categorical and free text data related to traumatic and acute injury, musculoskeletal disorders (MSD), and chronic pain were exported from REDCap into SAS 9.4, Excel, and NVivo, for quantitative and qualitative analysis, respectively. Time to injury was modeled using two different approaches: (1) time to the occurrence of first injury modeled by proportional hazard regression and (2) an intensity model for injury frequency. Two research team members also analyzed qualitative data using a content analysis approach.

RESULTS

During the study, 204 injuries were reported. Of the 154 participants, 93 (60.4%) reported musculoskeletal pain on at least one survey. The majority of injuries were traumatic, including fractures, sprains, and strains. Lack of health insurance was found to be related to increased risk of first injury [HR = 1.41, 95% CI = 0.97-2.04, p = 0.069]. Variables found to be related to injury intensity at the univariate level were: (1) a lack of health insurance [HR = 1.51, 95% CI = 1.04-2.20, p = 0.030], (2) age [HR for 10-year age increase;= 1.12, 95% CI = 0.99-1.27, p = 0.082], and (3) years employed in logging industry [HR for 10-year increase = 1.12, 95% CI = 0.99-1.26, p = 0.052]. Seeking medical attention for injury was not a priority for this cohort, and narratives revealed a trend for self-assessment. A variety of barriers, including finances, prevented loggers from seeking medical attention.

DISCUSSION

We found that loggers still experience serious, and sometimes disabling, injuries associated with their work. It was unsurprising that many injuries were due to slips, trips, and falls, along with contact with logging equipment and trees/logs. The narratives revealed various obstacles preventing loggers from achieving optimal health. Examples included geographic distance from healthcare, lack of time to access care, and entrenched values that prioritized independence and traditional masculinity. Financial considerations were also consistently cited as a primary barrier to adequate care.

CONCLUSION

There is a continued need to emphasize occupational health and safety in the logging industry. Implementation of relevant safety programs is key, but it is likely that the benefits of these will not be fully realized until a cultural shift takes place within this industry.

Working hours, sleep, and fatigue in the agriculture, forestry, and fishing sector: A scoping review

INTRODUCTION

Agriculture, forestry, and fishing industry (AgFF) workers often work extremely long hours during peak production seasons, resulting in sleep deprivation and fatigue. The National Occupational Research Agenda has classified fatigue as a "significant safety issue" and area of concern for many industry sectors, including AgFF. This review explores current research and practice in AgFF and proposes next steps.

METHODS

We conducted a scoping literature review to examine the extent and nature of research in this area. Article inclusion criteria included peer-reviewed journal articles written in English; published after 1989; covering AgFF workers in high-income countries; with data on working hours/schedules and sleep related to safety and health.

RESULTS

Limited research has addressed long hours and sleep deprivation among AgFF workers. We identified 8350 articles for title and abstract review. Among those, 407 underwent full-text review and 96 met all inclusion criteria (67% agriculture, 25% fishing/seafood processing, 8% forestry). The literature provided some evidence fatigue contributes to fatalities, injuries, and illnesses in AgFF. Older, new, young, foreign-born, and female workers, as well as those who work in small organizations or longer hours (40+) may be at higher risk for fatigue-related injury and illness. Few studies have developed or evaluated interventions to control risks.

DISCUSSION

Given that fatigue is a factor in injury and illness for this sector, future AgFF surveillance and research should increase efforts to capture fatigue and sleep data, directly investigate the role of long hours and nonstandard work schedules in the sector, and most importantly, create practical interventions to manage fatigue.

A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations

Activity recognition modelling using smartphone Inertial Measurement Units (IMUs) is an underutilized resource defining and assessing work efficiency for a wide range of natural resource management tasks. This study focused on the initial development and validation of a smartphone-based activity recognition system for excavator-based mastication equipment working in Ponderosa pine (Pinus ponderosa) plantations in North Idaho, USA. During mastication treatments, sensor data from smartphone gyroscopes, accelerometers, and sound pressure meters (decibel meters) were collected at three sampling frequencies (10, 20, and 50 hertz (Hz)). These data were then separated into 9 time domain features using 4 sliding window widths (1, 5, 7.5 and 10 seconds) and two levels of window overlap (50% and 90%). Random forest machine learning algorithms were trained and evaluated for 40 combinations of model parameters to determine the best combination of parameters. 5 work elements (masticate, clear, move, travel, and delay) were classified with the performance metrics for individual elements of the best model (50 Hz, 10 second window, 90% window overlap) falling within the following ranges: area under the curve (AUC) (95.0% - 99.9%); sensitivity (74.9% - 95.6%); specificity (90.8% - 99.9%); precision (81.1% - 98.3%); F1-score (81.9% - 96.9%); balanced accuracy (87.4% - 97.7%). Smartphone sensors effectively characterized individual work elements of mechanical fuel treatments. This study is the first example of developing a smartphone-based activity recognition model for ground-based forest equipment. The continued development and dissemination of smartphone-based activity recognition models may assist land managers and operators with ubiquitous, manufacturer-independent systems for continuous and automated time study and production analysis for mechanized forest operations.

Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety

Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.

Applications of GIS-Based Software to Improve the Sustainability of a Forwarding Operation in Central Italy

Reducing potential soil damage due to the passing of forest machinery is a key issue in sustainable forest management. Limiting soil compaction has a significant positive impact on forest soil. With this in mind, the aim of this work was the application of precision forestry tools, namely the Global Navigation Satellite System (GNSS) and Geographic Information System (GIS), to improve forwarding operations in hilly areas, thereby reducing the soil surface impacted. Three different forest study areas located on the slopes of Mount Amiata (Tuscany, Italy) were analyzed. Extraction operations were carried out using a John Deere 1410D forwarder. The study was conducted in chestnut (Castanea sativa Mill.) coppice, and two coniferous stands: black pine (Pinus nigra Arn.) and Monterey pine (Pinus radiata D. Don). The first stage of this work consisted of field surveys collecting data concerning new strip roads prepared by the forwarder operator to extract all the wood material from the forest areas. These new strip roads were detected using a GNSS system: specifically, a Trimble Juno Sb handheld data collector. The accumulated field data were recorded in GIS Software Quantum GIS 2.18, allowing the creation of strip road shapefiles followed by a calculation of the soil surface impacted during the extraction operation. In the second phase, various GIS tools were used to define a preliminary strip road network, developed to minimize impact on the surface, and, therefore, environmental disturbance. The results obtained showed the efficiency of precision forestry tools to improve forwarding operations. This electronic component, integrated with the on-board GNSS and GIS systems of the forwarder, could assure that the machine only followed the previously-planned strip roads, leading to a considerable reduction of the soil compaction and topsoil disturbances. The use of such tool can also minimize the risks of accidents in hilly areas operations, thus allowing more sustainable forest operations under all the three pillars of sustainability (economy, environment and society).

Decomposing Differences in Poor Self-rated Health between Those in Agriculture and Natural Resource Occupations and the Rest of the Labor Force

Objective: Occupations in agriculture and natural resources persistently have some of the highest rates of injury and illness. Additionally, these fields are dominated by segments of the population known to demonstrate poorer health, such as those with less education, lower family income, and more irregular labor force participation. Thus, it is unclear if health disparities between those in these sectors and the rest of the labor force are unique to these occupations, or a reflection of their demographic composition. The objective of this study was to determine how much of the difference in self-rated health between those who work agriculture and natural resource occupations - meaning farming, forestry, fishing, hunting, and resource extraction - and the rest of the labor force was due to demographic characteristics versus unexplained factors unique to the occupations.Methods: Using the National Health Interview Survey from 2008 to 2017, a two-way Oaxaca-Blinder decomposition of linear probability models predicting poor self-rated health between those reporting agriculture and natural resource occupations and other working adults with sociodemographic characteristics was performed.Results: Results show more than the total difference in the probability of poor self-rated health between the two groups (0.0173) can be explained by demographic composition (0.0303). If the agriculture and natural resource workforce had the average demographic composition between them and the rest of the labor force, they would have lower rates of poor self-rated health than the broader labor force.Conclusion: While agriculture and natural resource occupations are hazardous, the prevalence of poor self-rated health in the labor force is not unique to these occupations, but appears common among all occupations dominated by those with low income and education.

What Are the Occupational Risks in Forestry? Results of a Long-Term Study in Slovakia

Temporal patterns in occupational safety and health can shed light on the efficiency of safety measures companies adopt and identify when workers are prone to occupational accidents. We analyzed these patterns to identify the effects of factors such as the share of salvage logging, experience, age, daytime, weekday, and more on the number of occupational accidents at Forests of the Slovak Republic (FSR). We analyzed a database of 2963 occupational accidents and 443 occupational illnesses suffered by FSR employees and contractors. We then analyzed a subset of said database, containing 401 accident records coded according to European Statistics at Work manual. We used regression and correlation analyses and generalized linear models to test the relationship between the accident frequency and volume of harvested timber and volume of salvage logging. We used logistic regression, chi2 tests, and Cramér’s V statistic to test when accidents occur within shifts, weeks, and months. We found the volume of harvested timber significantly affects the frequency of severe and fatal accidents of contractors (R 0.81; p < 0.05), whereas, for employees, the relationship was insignificant. Over time, the number of accidents and incidence rate decreased, and inexperienced or older workers were the most prone to accidents.

Recent Contributions of Some Fields of the Electronics in Development of Forest Operations Technologies

In the last years, there has been a growing need to improve forest-wood chain concerning all three pillars of sustainability (economic, environmental, and social). Using electronic systems, in particular GIS, GNSS, and various kinds of sensors related to forest harvesting, is clearly one of the most powerful instruments to reach this aim. The contribution of these tools to forest operation is wide and various. One of the most important application was integrating ICT and GPS/GNSS on-board systems on modern forest machines. This allowed one to ensure multiple benefits to forest operation field. On the one hand, electronic systems, and particularly GIS, could be used to improve forest harvesting with a previous planning of the skid trails network, in order to minimize utilization impacts and risks for operators, ensuring at the same time high work productivity. Moreover, GIS developed files could also be implemented in modern forest machine GPS/GNSS systems, helping forest machines operators to move only along a designed skid trails network or making it possible to avoid restricted access areas. On the other hand, modern forest machines could be equipped with complex and accurate sensors that are able to determine, register, and share information about wood biomass quantity and quality and even undertake economic evaluation of stumpage value. Finally, the input and output of these systems and sensors could be implemented in a decision support system (DSS) ensuring the best silvicultural and operative alternative from a sustainable forest management point of view. A detailed review of the contribution of electronics in the development of forest operations is provided here.

Intent to Adopt Location Sharing for Logging Safety Applications

Logging entails work in remote areas with multiple hazards and consistently ranks among the most fatal occupations in the United States. Location-sharing (LS) devices that enable users to communicate geographic positions to others have been suggested as a technological approach to improving workplace safety on logging operations. This study investigated logger intent to adopt LS-based safety practices. Employing concepts from the Theory of Planned Behavior, including intent, attitude, norms, and perceived behavioral control, we surveyed Idaho loggers at three logger training programs. We evaluated their likelihood of using LS devices on logging operations and examined factors associated with LS adoption. The results showed that Idaho loggers are likely to use (a) automatic position updates for hand fallers, (b) LS devices on all ground workers and heavy equipment, and (c) LS technology for general situational awareness. Participants also recognized specific safety benefits to LS, particularly for emergency situations, such as communicating the need for help or expediting the discovery of injured coworkers. Our findings support further development of LS technology for logging safety, particularly devices and applications that facilitate injury response for isolated workers, such as hand fallers.

Positioning Methods and the Use of Location and Activity Data in Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.