This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.

The aim of this paper was to present a review on the role that movement variability (MV) plays in the analysis of sports movement and in the monitoring of the athlete's skills. MV has been traditionally considered an unwanted noise to be reduced, but recent studies have re-evaluated its role and have tried to understand whether it may contain important information about the neuro-musculo-skeletal organisation. Issues concerning both views of MV, different approaches for analysing it and future perspectives are discussed. Information regarding the nature of the MV is vital in the analysis of sports movements/motor skills, and the way in which these movements are analysed and the MV subsequently quantified is dependent on the movement in question and the issues the researcher is trying to address. In dealing with a number of issues regarding MV, this paper has also raised a number of questions which are still to be addressed.

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In remote sensing, principal components analysis is usually performed using unstandardized variables. However, the use of standardized variables yields significantly different results. In this paper principal components of two LANDSAT MSS subscenes were separately calculated using both methods. The result indicate substantial improvement in signal-to-noise ratio and image enhancement by using standardized variables in the principal components analysis.

Future research is needed with respect to the effects of long-term resistance-training interventions on both technical parameters of swimming and overall swimming performance. The results of such work will be highly informative for the scientific community, coaches and athletes.