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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. [ABSTRACT FROM AUTHOR]

Purpose: accurate assessment of human joint parameters is a critical issue for the quantitative movement analysis, due to a direct influence on motion patterns. In this study three different known functional methods are experimentally compared to identify knee joint kinematics for further gait and motion analysis purposes., Methods: taking into account the human knee physiology complexity, within its roto-translation, the study is conducted on a lower limb mechanical analogue with a polycentric hinge-based kinematic model. The device mimics a joint with a mobile axis of rotation whose position is definable. Sets of reflective markers are placed on the dummy and flexion-extension movements are imposed to the shank segment. Marker positions are acquired using an optoelectronic motion capture system (Vicon 512)., Results: acquired markers' positions are used as input data to the three functional methods considered. These ones approximate the polycentric knee joint with a fixed single axis model. Different ranges of motion and number of markers are considered for each functional method. Results are presented through the evaluation of accuracy and precision concerning both misalignment and distance errors between the estimated axis of rotation and the instantaneous polycentric one, used as reference., Conclusion: the study shows the feasibility of the identification of joint parameters with functional approaches applied on a polycentric mechanism, differently from those usually conceived by the reviewed algorithms. Moreover, it quantifies and compares the approximation errors using different algorithms, by varying number and position of markers, as well ranges of motion.