This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $) and mean absolute error (MAE = $ {2.006}^{\circ } $), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ($ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ($ {r}^2>0.89 $). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.