An Innovative Sensor Fusion Algorithm for Motion Tracking With On-Line Bias Compensation: Application to Joint Angles Estimation in Yoga

Magnetic Inertial Measurement Units (MIMUs) represent an increasingly used technology in the field of human motion. However, their applications are strongly affected by magnetic disturbances and poor calibration, which could determine inaccurate attitude estimations. Thus, Inertial Measurement Units (IMUs), relying exclusively on accelerometers and gyroscopes, could be used as an alternative to MIMUs for motion tracking, avoiding the negative effect of magnetic disturbances. Unfortunately, gyroscope signals are characterized by several error sources, among which gyroscope bias, whose variations due to environmental factors and temporal instability strongly affect the attitude estimation accuracy. Aim of the present work is to propose a novel sensor fusion algorithm for IMU-based applications that embodies an adaptive on-line bias capture module. The accuracy of the proposed filter was tested on ten expert yoga practitioners during the execution of a sun salutation sequence. The right upper limb joint angles were estimated and their accuracy assessed in terms of Mean Absolute Error (MAE) and Pearson's correlation coefficient by comparison with an optoelectronic motion capture system. The achieved worst-case accuracy was 5.59, 6.75 and 3.49 degrees for the wrist, elbow and shoulder joints respectively. The accuracy of the algorithm is further confirmed by the high values of the Pearson's correlation coefficients between IMUs and OMC angles, which were greater than 0.91, 0.92 and 0.98 respectively for the three considered joints. The proposed algorithm can thus be considered as a promising tool for attitude estimation in those contexts when magnetic interferences cannot be monitored and, thus, adequately compensated.