Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. Firstly, an infinite impulse response (IIR) digital 4th order Butterworth filter was implemented to remove the noise from the raw gyro sensor data. Secondly, the mode value of the static state gyro sensor data was subtracted from the measured data to remove offset values. Thirdly, a robust double derivative and integration method was introduced to remove any remaining drift error from the data. Lastly, sensor attachment errors were minimized by establishing the gravitational acceleration vector from the acceleration data at standing upright and sitting posture. These improvements proposed allowed for removing the drift effect, and showed an average of 2.1°, 33.3°, 15.6° difference for the hip knee and ankle joint flexion/extension angle, when compared to without implementation. Kinematic and spatio-temporal gait parameters were also calculated from the heel-contact and toe-off timing of the foot. The data provided in this work showed potential of using wearable sensors in clinical evaluation of patients with gait-related diseases.

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The human joint kinematics is an interesting topic in biomechanics and turns to be useful for the analysis of human movement in several fields. A crucial issue regards the assessment of joint parameters, like axes and centers of rotation, due to the direct influence on human motion patterns. A proper accuracy in the estimation of these parameters is hence required. On the whole, stereophotogrammetry-based predictive methods and, as an alternative, functional ones can be used to this end. This article presents a new functional algorithm for the assessment of knee joint parameters, based on a polycentric hinge model for the knee flexion-extension. The proposed algorithm is discussed, identifying its fields of application and its limits. The techniques for estimating the joint parameters from the metrological point of view are analyzed, so as to lay the groundwork for enhancing and eventually replacing predictive methods, currently used in the laboratories of human movement analysis. This article also presents an assessment of the accuracy associated with the whole process of measurement and joint parameters estimation. To this end, the presented functional method is tested through both computer simulations and a series of experimental laboratory tests in which swing motions were imposed to a polycentric mechanical analogue and a stereophotogrammetric system was used to record them.

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The integration of proper algorithms and computer graphics-based systems seems promising for the design of biomechanical models and the relative motion analysis. Thus, consequences on research fields as gait analysis are gathered, focusing on joints kinematics. Human motion patterns are indeed directly influenced from human model and associated joints parameters, such as centers and axes of rotation. These, as a matter of fact, determine the body segments coordinates systems. Joints parameters are estimated with several methods. The aim of this research is to evaluate the consistency of a functional approach versus a the predictive one. A validation of the algorithm used to estimate the lower limbs joints centers in gait analysis is provided with a proper subject-specific multibody model implemented in OpenSim space. Joints angles are estimated using a global optimization method and a comparison with the gold standard technique is also discussed. Overall the obtained results are consistent for the two different methodologies. The correlation of the curves is excellent in the sagittal plane, and very good in the coronal and transversal plane.

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