A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor. (July 2022)

• Record Type: Journal Article
• Authors: Lamooki, Saeb Ragani; Hajifar, Sahand; Kang, Jiyeon; Sun, Hongyue; Megahed, Fadel M.; Cavuoto, Lora A.
• Is Part Of: Applied ergonomics. Volume 102(2022)
• Publication Date: 2022-07
• Subjects: Human performance modeling -- Ergonomic risk assessment -- End-to-end learning -- Change point detection -- Wavelets -- Wearable sensors; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2022.103732
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: 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. Highlights: An end-to-end framework is presented to allow automated monitoring of task type, duration, and repetition count. Data from a single wrist-worn accelerometer was suitable for reliable calculation of ergonomic risk factors. >90% accuracy was achieved for activity recognition. Task duration and repetition counts were estimated with ∼7% error.