Exploratory narrative text analysis to characterize tasks associated with injuries among electric utility line workers: EPRI Occupational Health and Safety Database 1995-2013

Impact of neck posture and insulating stick use on neck disability in Korean line workers: a cross-sectional study

Background

Occupational neck disability is a prevalent issue, especially among line workers, who are often exposed to elevated levels of cervical ergonomic stress. The aim of this study was to investigate the impact of neck posture and insulating stick use on neck disability in a specific occupational group in Korea.

Methods

This cross-sectional study was conducted among 483 line workers in Gwangju and Jeonnam, Korea. Data were collected using the Neck Disability Index, Cervical Degenerative Index, and a structured questionnaire focusing on demographic and occupational factors. Logistic regression analysis was applied to determine the adjusted odds ratio (OR) and 95% confidence interval (CI) for neck posture and factors related to neck disability.

Results

Neck disability prevalence was 17.2% among the participants. Multivariate logistic regression analysis showed that factors related to neck disability included age over 60 years (adjusted OR: 3.08; 95% CI: 1.63-5.83), depression (adjusted OR: 8.33; 95% CI: 3.85-18.00), a history of cervical trauma (adjusted OR: 2.13; 95% CI: 1.04-4.40), and radiological degenerative changes in the cervical spine (adjusted OR: 2.33; 95% CI: 1.26-4.33). In particular, the adjusted OR of neck disability among live-line workers was 2.10 (95% CI: 1.12-3.92) when compared with support workers (model 1). Other analysis models showed that use of insulating sticks for more than 10 hours per week (adjusted OR: 2.46; 95% CI: 1.32-4.61) and higher neck extension (adjusted OR: 2.98; 95% CI: 1.14-3.46) were significant work-related risk factors (model 2,3).

Conclusions

Neck posture, age, depression, cervical trauma history, degenerative changes in the cervical spine, and use of insulating sticks are significant risk factors for neck disability among line workers in Korea. These findings highlight the need to improve the working environment and reduce the burden of cervical ergonomic stress among line workers.

Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach

Electrical line workers (ELWs) experience harsh environments, characterized by long shifts, remote operations, and potentially risky tasks. Wearables present an opportunity for unobtrusive monitoring of productivity and safety. A prerequisite to monitoring is the automated identification of the tasks being performed. Human activity recognition has been widely used for classification for activities of daily living. However, the literature is limited for electrical line maintenance/repair tasks due to task variety and complexity. We investigated how features can be engineered from a single wrist-worn accelerometer for the purpose of classifying ELW tasks. Specifically, three classifiers were investigated across three feature sets (time, frequency, and time-frequency) and two window lengths (4 and 10 seconds) to identify ten common ELW tasks. Based on data from 37 participants in a lab environment, two application scenarios were evaluated: (a) intra-subject, where individualized models were trained and deployed for each worker; and (b) inter-subject, where data was pooled to train a general model that can be deployed for new workers. Accuracies ≥ 93% were achieved for both scenarios, and increased to ≥96% with 10-second windows. Overall and class-specific feature importance were computed, and the impact of those features on the obtained predictions were explained. This work will contribute to the future risk mitigation of ELWs using wearables.

A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor

Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.