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Work-related upper extremity musculoskeletal disorders (WRUED) are a majorproblem in modern societies as they affect the quality of life of workers andlead to absenteeism and productivity loss. According to studies performed inNorth America and Western Europe, their prevalence has increased in the lastfew decades. This challenge calls for improvements in prevention methods. Oneavenue is through the development of wearable sensor systems to analyzeworker's movements and provide feedback to workers and/or clinicians. Suchsystems could decrease the physical work demands and ultimately preventmusculoskeletal disorders. This paper presents the development and validationof a data fusion algorithm for inertial measurement units to analyze worker'sarm elevation. The algorithm was implemented on two commercial sensor systems(Actigraph GT9X and LSM9DS1) and results were compared with the data fusionresults from a validated commercial sensor (XSens MVN system).Cross-correlation analyses [r], root-mean-square error (RMSE) and averageabsolute error of estimate were used to establish the construct validity of thealgorithm. Five subjects each performed ten different arm elevation tasks. Theresults show that the algorithm is valid to evaluate shoulder movements withhigh correlations between the results of the two different sensors and thecommercial sensor (0.900-0.998) and relatively low RMSE value for the ten tasks(1.66-11.24{\deg}). The proposed data fusion algorithm could thus be used toestimate arm elevation.

Development and Validation of a Data Fusion Algorithm with Low-Cost Inertial Measurement Units to Analyze Shoulder Movements in Manual Workers