4.5 Article

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

期刊

APPLIED ERGONOMICS
卷 102, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apergo.2022.103732

关键词

Human performance modeling; Ergonomic risk assessment; End-to-end learning; Change point detection; Wavelets; Wearable sensors

资金

  1. Centers for Disease Control and Prevention [1 R21OH011749-01]
  2. Pilot Projects Research Training Program of the NY/NJ Education and Research Center
  3. National Institute for Occupational Safety and Health [T42 OH008422]
  4. GE Research

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This study investigates the effectiveness of an end-to-end framework that utilizes data from a single wearable sensor for ergonomic risk assessment. By identifying tasks and estimating task intensity, the framework eliminates the need for direct observation and achieves good accuracy rates and estimation errors.
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.

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