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

Work-related upper extremity musculoskeletal disorders (WRUED) are a major problem in modern societies as they affect the quality of life of workers and lead to absenteeism and productivity loss. According to studies performed in North America and Western Europe, their prevalence has increased in the last few decades. This challenge calls for improvements in prevention methods. One avenue is through the development of wearable sensor systems to analyze worker's movements and provide feedback to workers and/or clinicians. Such systems could decrease the physical work demands and ultimately prevent musculoskeletal disorders. This paper presents the development and validation of a data fusion algorithm for inertial measurement units to analyze worker's arm elevation. The algorithm was implemented on two commercial sensor systems (Actigraph GT9X and LSM9DS1) and results were compared with the data fusion results from a validated commercial sensor (XSens MVN system). Cross-correlation analyses [r], root-mean-square error (RMSE) and average absolute error of estimate were used to establish the construct validity of the algorithm. Five subjects each performed ten different arm elevation tasks. The results show that the algorithm is valid to evaluate shoulder movements with high correlations between the results of the two different sensors and the commercial 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 to estimate arm elevation.