  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.
