A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer

• Published in: IEEE transactions on biomedical engineering 2015-08, Vol.62 (8), p.2033-2043
• Main Authors: Ligorio, Gabriele, Sabatini, Angelo M.
• Format: Article
• Language: English
• Subjects: Acceleration; accelerometer; Accelerometers; Accelerometry; Algorithms; Attitude estimation; Biomechanical Phenomena; Biomedical measurement; Gravity; Gyroscopes; Human Activities; Humans; Kalman filtering; Mathematical model; Quaternions; Signal Processing, Computer-Assisted; Walking - physiology
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2015.2411431

Goal: Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. Methods: The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. Results: The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. Conclusion: A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Significance: Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.