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Exerted force estimation using a wearable sensor during manual material handling
Author(s) -
Chihara Takanori,
Sakamoto Jiro
Publication year - 2021
Publication title -
human factors and ergonomics in manufacturing and service industries
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.408
H-Index - 39
eISSN - 1520-6564
pISSN - 1090-8471
DOI - 10.1002/hfm.20881
Subject(s) - workload , forearm , wearable computer , set (abstract data type) , computer science , wrist , regression analysis , data set , simulation , mathematics , statistics , medicine , surgery , embedded system , programming language , operating system
While evaluating the physical workload on workers, the force exerted needs to be estimated in a convenient way because biomechanical analysis for physical workload evaluation requires data on both posture and exerted force. The aim of this study was to investigate the effectiveness of exerted force estimation using a wearable device that can be easily utilized even by ordinary workers. We measured eight electromyograms (EMGs) on the forearm and the forearm posture with an armband type wearable sensor during manual material handling with varying holding weights and heights. The measurement results showed that the EMGs monotonically increased with an increase in weight. In addition, the EMGs varied with the holding height even when the same weight was held. We constructed an estimation function of the weight using multiple regression analysis. Two sets of explanatory variables were used to investigate the effectiveness of the forearm posture data: the eight EMGs (i.e., SET 1) and the eight EMGs and the two forearm angles (i.e., SET 2). Multiple regression analysis showed that the accuracy of SET 2 was better than that of SET 1. In addition, the average absolute error of the estimation function with SET 2 was 1.49 kg; thus, we concluded that the accuracy of this estimation function has sufficient accuracy for the evaluation of physical workload.