Occupational and environmental risk factors for falls among workers in the healthcare sector

Falls are a leading cause of occupational injury for workers in healthcare, yet the risk factors of falls in this sector are understudied. Falls resulting in workers' compensation for time-loss from work from 2004–2007 for healthcare workers in British Columbia (BC) were extracted from a standardised incident-reporting database. Productive hours were derived from payroll data for the denominator to produce injury rates; relative risks were derived through Poisson regression modelling. A total of 411 falls were accepted for time-loss compensation. Compared to registered nurses, facility support workers (risk ratio (95% CI) = 6.29 (4.56–8.69)) and community health workers (6.58 (3.76–11.50)) were at high risk for falls. Falls predominantly occurred outdoors, in patients' rooms and kitchens depending on occupation and sub-sector. Slippery surfaces due to icy conditions or liquid contaminants were a leading contributing factor. Falls were more frequent in the colder months (January–March). The risk of falls varies by nature of work, location and worker demographics. The findings of this research will be useful for developing evidence-based interventions.

Statement of Relevance: Falls are a major cause of occupational injury for healthcare workers. This study examined risk factors including occupation type, workplace design, work setting, work organisation and environmental conditions in a large healthcare worker population in BC, Canada. The findings of this research should contribute towards developing evidence-based interventions.     

基于agent的建筑工人流动行为仿真及其对行业的影响

建筑工人的频繁流动行为影响到建筑业产业结构升级及可持续发展.将社会网络理论与博弈论相结合,建立建筑工人的社会关系网络和博弈关系网络双层平行联动网络,构建既能反映建筑劳务市场中个体的行为选择过程又能涵盖劳务组织中的关系结构网络的多智能体仿真模型,运用仿真软件进行仿真实验,从社会关系和博弈关系视角揭示建筑工人流动行为发生过程及其对行业技能水平、劳动力成本、劳动关系和社会关系的影响.结果表明,建筑工人频繁的流动不利于行业技术水平的提高,并会导致企业用工成本增加、雇佣关系恶化、工人间关系弱化等问题.建立有效的培训制度、企业实施技术奖励措施和完善市场保险保障体制,可缓解建筑工人频繁的流动,从而降低其不利影响.     

Fall risk assessment of construction workers based on biomechanical gait stability parameters using wearable insole pressure system

Falls on the same level are a leading cause of non-fatal injuries in the construction industry, and loss of balance events are the primarily contributory risk factors associated with workers’ fall injuries. Previous studies have indicated that changes in biomechanical gait stability parameters provide substantial safety gait metrics for assessing workers’ fall risks. However, scant research has been conducted on changes in biomechanical gait stability parameters based on foot plantar pressure patterns to assess workers’ fall risks. This research examined the changes in spatial foot regions and loss of balance events associated with biomechanical gait stability parameters based on foot plantar pressure patterns measured by wearable insole pressure system. To test the hypotheses of this study, ten asymptomatic participants conducted laboratory simulated loss of balance events which are often initiated by extrinsic fall risk factors. Our results found: (1) statistically significant differences in biomechanical gait stability parameters between spatial foot regions, especially with the peak pressure parameter; and (2) statistically significant differences in biomechanical gait stability parameters between loss of balance events when compared to normal gait (baseline), especially with the pressure-time integral parameter. Overall, the findings of this study not only provide useful safety gait metrics for early detection of specific spatial foot regions but also allow safety managers to understand the mechanism of loss of balance events in order to implement proactive fall-prevention strategies.     

Towards a model for assessing workers' risks resulting from the implementation of information and communication systems and technologies

The authors investigate the effects of information and communication systems and technologies (ICST) on workers' health and safety. A multidisciplinary and convergent perspective is used to define a model that evaluates the risks associated with the implementation of any information system. The model incorporates the organizational, individual, and social elements that affect workers' risks. Special attention has been paid to the incorporation of ergonomic and organizational factors including culture, technological change, and informatics ethics. © 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 39–59, 2006.     

Risk perception of construction equipment operators on construction sites of Turkey

Turkey has been an attractive country for construction industry in the last decade. Many large-scale construction projects, which have been realized by both international and local construction firms, helped the economy and provided employment opportunities for many. At the same time, many construction workers have been losing their lives on construction sites, which involve the usage of heavy equipment on a daily basis.Past research studies suggest that employee participation and their perception of safety risks could be valuable for determining and eliminating hazards on construction site. Therefore, this study aimed to determine and evaluate the risk perception of construction equipment operators in Turkey. The study is mainly based on a questionnaire survey performed in 51 construction projects that involved 198 heavy equipment operators. A statistical analysis was first performed on the results of the survey to observe the frequency distribution of parameters, such as safety and health training, using flagger, experience, type of equipment, working conditions and other project related data. Then, statistical methods such as, t-test, ANOVA analysis, Kruskall–Wallis one way analysis of variance, and Mann–Whitney U test were performed to seek statistically meaningful differences in risk perception of operator groups with different attributes.Results revealed the importance of safety and health training as well as working with an assistant, such as a flagger. It was observed that operators who took safety and health training and operators who worked with flaggers perceived risk differently than others. It was also found that the project type influences the risk perception of equipment operators due to diversity of construction equipment activities performed, as well as number of incidents occurred in those projects.Relevance to industryThe authors expect this research to lead to discussion and further research on risk assessment for construction industry. The risk assessment findings of this study, in particular, could help the safety professionals detect possible unforeseen risks and design safety and health plans for construction sites that require usage of heavy equipment on a daily basis. Heavy equipment manufacturers could also devise a similar research that involves operators’ risk perception to design more ergonomic and safe equipment.     

The benefits of an additional worker are task-dependent: assessing low-back injury risks during prefabricated (panelized) wall construction

Team manual material handling is a common practice in residential construction where prefabricated building components (e.g., wall panels) are increasingly used. As part of a larger effort to enable proactive control of ergonomic exposures among workers handling panels, this study explored the effects of additional workers on injury risks during team-based panel erection tasks, specifically by quantifying how injury risks are affected by increasing the number of workers (by one, above the nominal or most common number). Twenty-four participants completed panel erection tasks with and without an additional worker under different panel mass and size conditions. Four risk assessment methods were employed that emphasized the low back. Though including an additional worker generally reduced injury risk across several panel masses and sizes, the magnitude of these benefits varied depending on the specific task and exhibited somewhat high variability within a given task. These results suggest that a simple, generalizable recommendation regarding team-based panel erection tasks is not warranted. Rather, a more systems-level approach accounting for both injury risk and productivity (a strength of panelized wall systems) should be undertaken.     

Low back injury risks during construction with prefabricated (panelised) walls: effects of task and design factors

New technology designed to increase productivity in residential construction may exacerbate the risk of work-related musculoskeletal disorders (WMSDs) among residential construction workers. Of interest here are panelised (prefabricated) wall systems (or panels) and facilitating an ongoing effort to provide proactive control of ergonomic exposures and risks among workers using panels. This study, which included 24 participants, estimated WMSD risks using five methods during common panel erection tasks and the influences of panel mass (sheathed vs. unsheathed) and size (wall length). WMSD risks were fairly high overall; e.g. 34% and 77% of trials exceeded the 'action limits' for spinal compressive and shear forces, respectively. Heavier (sheathed) panels significantly increased risks, although the magnitude of this effect differed with panel size and between tasks. Higher levels of risk were found in tasks originating from ground vs. knuckle height. Several practical recommendations based on the results are discussed. STATEMENT OF RELEVANCE: Panelised wall systems have the potential to increase productivity in residential construction, but may result in increased worker injury risks. Results from this study can be used to generate future panel design and construction processes that can proactively address WMSD risks.     

Determination of workers' compliance to safety regulations using a spatio-temporal graph convolution network

The safety of workers in construction remains a critical issue despite the automation of several tasks with fewer workers on site. As fatal accidents of workers account for a significant number of construction accidents, considerable effort has been made to monitor workers’ safety behaviors with additional personnel for supervising workers. With the advancement of data analytics, recent research has reported various human activity recognition methods based on image data to perform automated worker monitoring without additional labor. Nevertheless, unlike existing approaches based on a single image, a method that can capture a series of actions from sequential images is required to monitor workers’ compliance with safety behavior. To this end, an approach based on OpenPose and a spatio-temporal graph convolutional network is proposed in this study to evaluate workers’ compliance with safety regulations using sequential videos. The two primary functions of the developed method include 1) classifying each safety behavior among five representative behaviors stipulated in construction, and 2) determining the compliance of workers with each safety regulation. The results indicate that the developed approach can capture momentary safety behaviors and workers’ compliance with feasible accuracy of an average F1 score greater than 0.8. Furthermore, the proposed method can be extended to safety intervention policies with behavior-based feedback to inform workers of their non-compliance with safety behaviors. Therefore, this study contributes to proactive safety management by focusing on workers’ behavioral levels rather than on accident rate-based management.     

Low back injury risks during construction with prefabricated (panelised) walls: effects of task and design factors

New technology designed to increase productivity in residential construction may exacerbate the risk of work-related musculoskeletal disorders (WMSDs) among residential construction workers. Of interest here are panelised (prefabricated) wall systems (or panels) and facilitating an ongoing effort to provide proactive control of ergonomic exposures and risks among workers using panels. This study, which included 24 participants, estimated WMSD risks using five methods during common panel erection tasks and the influences of panel mass (sheathed vs. unsheathed) and size (wall length). WMSD risks were fairly high overall; e.g. 34% and 77% of trials exceeded the ‘action limits’ for spinal compressive and shear forces, respectively. Heavier (sheathed) panels significantly increased risks, although the magnitude of this effect differed with panel size and between tasks. Higher levels of risk were found in tasks originating from ground vs. knuckle height. Several practical recommendations based on the results are discussed.

Statement of Relevance:Panelised wall systems have the potential to increase productivity in residential construction, but may result in increased worker injury risks. Results from this study can be used to generate future panel design and construction processes that can proactively address WMSD risks.     

Neural mechanisms behind semantic congruity of construction safety signs: An EEG investigation on construction workers

Accurate comprehension of safety signs plays a critical part in warning construction workers of potential work hazards. However, existing studies have rarely investigated construction workers' comprehension of safety signs at construction sites. Moreover, existing evaluation methods are generally based on subjective behavior tests, questionnaires, and interviews. Therefore, this study examined the effects of semantic congruity on the comprehension of safety signs, including two sign types (prohibition vs. warning signs) and two conditions (semantic congruence vs. incongruence), combining event-related potentials and time-frequency analysis measurements. Adopting the S1-S2 paradigm, electroencephalogram data were recorded when participants decided whether S1 and S2 were semantically congruent or not. Results showed that the semantically incongruent safety sign-word pairs elicited larger N400 amplitudes and increased theta (3–8 Hz) power in 300–420 ms. The amplitude of N400 in the semantically incongruent condition of the warning sign-word pairs was more negative than that for the prohibition sign-word pairs, while there were no significant differences between the prohibition and warning sign-word pairs in the semantically congruent condition. A greater late positive potential (LPP) (550–750 ms) was also elicited in the semantically incongruent safety sign-word pairs, which was different from previous studies that observed larger LPP in congruent conditions. These results suggest complicated cognitive mechanisms of safety sign comprehension in construction workers. This study extends safety sign comprehension research by using electrophysiological approaches and provides useful indicators for researchers or safety engineers to measure the semantic congruity of proposed sign designs.     

Inferring workplace safety hazards from the spatial patterns of workers’ wearable data

Hazard identification in construction typically requires safety managers to manually inspect an area. However, current approach is very limited due to the dynamic nature of construction sites and the subjective nature of human perception. Using wearable inertial measurement units (WIMU), previous literatures revealed the relationship between a worker’s abnormal gait patterns and the existence of slip, trip and fall (STF) hazards. Though the prior work demonstrated the strong correlation between STF hazards and abnormal gait patterns, automated hazard identification is a challenging issue due to the lack of knowledge on decision threshold on identifying hazards under different construction environments. To fill the research gap, this study developed an approach that can automatically identify the STF hazards without knowledge about thresholds by investigating the spatial associations of workers’ abnormal gait occurrences. An experiment simulating a brick installation was performed with different types of STF hazards (e.g., poor housekeeping), and results demonstrate the feasibility of STF hazards identification with the developed approach. The results highlight the opportunities of revealing potential accident hotspots via an efficient and semi-automated methodology, which overcomes many of the limitations in current practice.     

Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared to many existing classification methods (e.g., hot-spot detection, sequential alignment method), the network method substantiates the social meanings underlying the interconnectedness and similarities of people's activity trajectories and better integrates spatial interaction (colocation or distance-decay) and temporal connections (concurrence or sequence) of daily lives in the measure of similarity. This approach enables us to detect variant community structures, with individuals in the same community interacting relatively more than individuals belonging to different communities, by decomposing the complex trajectories into different meaningful events (e.g., activities, trips, tours, and subsequences). We also demonstrate the practicality and scientific merit of the network analysis approach in a case study of household travel behavior in Charlotte, North Carolina. Results derived from disaggregated survey data establish the effectiveness and flexibility of the network methods to detect cohesive communities of individuals and ATPs by different narratives of everyday-life events. This study also suggests that the network analysis approach has great potential to classify large datasets of other space-time trajectories and to discover policy-sensitive activity, trip, and tour patterns that help us develop policy and planning alternatives for sustainable communities and mobility.     

The effects of safety handrails and the heights of scaffolds on the subjective and objective evaluation of postural stability and cardiovascular stress in novice and expert construction workers

Work performed on scaffolds carries the risk of falling that disproportionately threatens the safety and health of novice construction workers. Hence, objective measures of the postural stability, cardiovascular stress, and subjective difficulty in maintaining postural balance were evaluated for four expert and four novice construction workers performing a manual task in a standing posture on a scaffold with and without safety handrails at two different elevation heights. Based on a multivariate analysis of variance, the experience, scaffold height, and presence of a handrail were found to significantly affect measures of the postural stability and cardiovascular stress. At a lower level of worker experience, a higher scaffold height, and in the absence of a handrail (which may correspond to higher risk of a fall), postural stability was significantly reduced, while cardiovascular stress and subjective difficulties in maintaining postural balance increased. We emphasize the importance of training and handrails for fall prevention at construction sites.     

Deep learning-based classification of work-related physical load levels in construction

Work-related musculoskeletal disorders (WMSDs) are the leading cause of the nonfatal injuries for construction workers, and a worker’s overexertion is a major source of such WMSDs. Pushing, pulling, and carrying movements—which are all activities largely associated with physical loads—account for 35% of WMSDs. However, most previous studies have focused on the identification of non-ergonomic postures, and there has been limited effort expended on measuring a worker’s exposures to the physical loads caused by materials or tools during construction tasks. With the advantage of using a wearable inertial measurement sensor to monitor a worker’s bodily movements, this study investigates the feasibility of identifying various physical loading conditions by analyzing a worker’s lower body movements. In the experiment with laboratory settings, workers performed a load carrying task by moving concrete bricks. A bidirectional long short-term memory algorithm is employed to classify physical load levels; this approach achieved 74.6 to 98.6% accuracy and 0.59 to 0.99 F-score in classification. The results demonstrate the feasibility of the proposed approach in identifying the states of physical loads. The findings of this study contribute to the literature on classifying ergonomically at-risk workers and on preventing WMSDs in high physical demand occupations, thereby helping enhance the health and safety of the construction workplace.     

数字化医院信息安全因素的分析与研究

随着计算机的高速发展,各大医院都加快了信息数字化的建设。在数字化信息的建设过程中,医院网络系统大大的提高了医院的工作进展效率,使得信息得到了更好的管理,但是在建设过程中,系统的保密性和安全性是首要考虑的要素。为了确保数字化医院系统的安全稳定运行,减少安全隐患,需要考虑有关安全方面的各个因素。本文针对医院数字化网络系统,对影响其安全的各个主要因素进行分析与研究。     

Real-time personal protective equipment monitoring system

The use of personal protective equipment (PPEs) in the construction industry is necessary to guarantee the safety of the workers. However, this equipment is not usually worn properly. Nowadays, the only control performed over the use of PPEs consists of a visual inspection. This paper introduces a novel cyber-physical system (CPS) to check in real time how the PPEs are worn by the workers. In order to perform such a control, an architecture composed of a wireless local area network and a body area network is considered. A system prototype was developed by using Zigbee and RFID technologies that support the deployment of both kinds of networks. The worker carries a microcontroller-based device that detects the presence of the PPEs and sends a report to a central unit where alerts and historical data are generated. This paper is basically aimed at introducing the monitoring system, describing its hardware and software components, and analyzing the coverage and consumption of the worker’s device.     

FOUR APPROACHES TO THE STUDY OF AGEING IN INDUSTRY

Accurate reporting of work-related conditions is necessary to monitor workplace health and safety, and to identify the interventions that are most needed. Reporting systems may be designed primarily for external agencies (OSHA or workers' compensation) or for the employer's own use. Under-reporting of workplace injuries and illnesses is common due to a variety of causes and influences. Based on previous reports, the authors were especially interested in the role of safety incentive programmes on under-reporting. Safety incentive programmes typically reward supervisors and employees for reducing workplace injury rates, and thus may unintentionally inhibit proper reporting. The authors describe a case study of several industrial facilities in order to illustrate the extent of under-reporting and the reasons for its occurrence. A questionnaire and interview survey was administered to 110 workers performing similar tasks and several managers, health, and safety personnel at each of three industrial facilities. Although less than 5% of workers had officially reported a work-related injury or illness during the past year, over 85% experienced work-related symptoms, 50% had persistent work-related problems, and 30% reported either lost time from work or work restrictions because of their ailment. Workers described several reasons for not reporting their injuries, including fear of reprisal, a belief that pain was an ordinary consequence of work activity or ageing, lack of management responsiveness after prior reports, and a desire not to lose their usual job. Interviews with management representatives revealed administrative and other barriers to reporting, stemming from their desire to attain a goal of no reported injuries, and misconceptions about requirements for recordability. The corporate and facility safety incentives appeared to have an indirect, but significant negative influence on the proper reporting of workplace injuries by workers. A variety of influences may contribute to under-reporting; because of under-reporting, worker surveys and symptom reports may provide more valuable and timely information on risks than recordable injury logs. Safety incentive programmes should be carefully designed to ensure that they provide a stimulus for safety-related changes, and to discourage under-reporting. A case-control study of similar establishments, or data before and after instituting safety incentives, would be required to more clearly establish the role of these programmes in under-reporting.     

A context-augmented deep learning approach for worker trajectory prediction on unstructured and dynamic construction sites

Predicting workers’ trajectories on unstructured and dynamic construction sites is critical to workplace safety yet remains challenging. Existing prediction methods mainly rely on entity movement information but have not fully exploited the contextual information. This study proposes a context-augmented Long Short-Term Memory (LSTM) method, which integrates both individual movement and workplace contextual information (i.e., movements of neighboring entities, working group information, and potential destination information) into an LSTM network with an encoder-decoder architecture, to predict a sequence of target positions from a sequence of observations. The proposed context-augmented method is validated using construction videos and the prediction accuracy achieved is 8.51 pixels in terms of final displacement error (FDE), with an observation time of 3 s and prediction time of 5 s—5.4% smaller than using the position-based method. Compared to conventional one-step-ahead predictions, the proposed sequence-to-sequence method predicts trajectories over multiple steps to avoid error accumulation and effectively reduces the FDE by 70%. In addition, qualitative analysis is conducted to provide insights to select appropriate prediction methods given different construction scenarios. It was found that the context-aware model leads to better performance comparing to the position-based method when workers are conducting collaborative activities.     

Online decentralized information gathering with spatial–temporal constraints

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial–temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents’ trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial–temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.