Walking efficiently is fundamental to maintaining an independent style of life. In this context, instrumented gait analysis is a powerful tool to assess motor capacity and performance as well as to diagnose and plan for intervention. The intent of this talk is to present an overview of conceptual, analytical and experimental elements to quantitatively describe human movement, with specific focus on gait using magneto-inertial sensors. It includes a review and a taxonomy scheme of the techniques for the estimation of the spatio-temporal gait parameters and joint kinematics.
The half-squat is the most widely used exercise in the resistance training, which must be considered optimal only if is specific and safe. Safeness relies, with other factors, in using the correct technique and being provided with adequate monitoring and feedback. In this perspective, this study a) provided a thoroughly characterization of the less dangerous squat technique, b) showed how wearable inertial measurement units (IMU) can be used to quantify key variables useful to reduce errors. The IMU estimate presented a good concurrent validity (r=0.91) for trunk maximal forward inclination, although with significant mean systematic bias of 7±5 deg, and fair concurrent validity for pelvis and barbell rotations in the frontal plane with lower systematic biases, thus encouraging to use IMUs to provide practitioners a quantitative feedback of the execution.
The proper execution of the sprint start is crucial in determining the performance during a sprint race. In this respect, when moving from the crouch to the upright position, trunk kinematics is a key element. The purpose of this study was to validate the use of a trunk mounted Inertial Measurement Unit (IMU) in estimating the trunk inclination and angular velocity in the sagittal plane during the sprint start. In-lab sprint starts were performed by five sprinters. The local acceleration and angular velocity components provided by the IMU were processed using an adaptive Kalman filter. The accuracy of the IMU inclination estimate and its consistency with trunk inclination were assessed using reference stereophotogrammetric measurements. A Bland–Altman analysis, carried out using parameters (minimum, maximum, and mean values) extracted from the time histories of the estimated variables, and curve similarity analysis (correlation coefficient > 0.99, Root Mean Square Difference < 7 deg) indicated the agreement between reference and IMU estimates, opening a promising scenario for an accurate in-field use of IMUs for sprint start performance assessment.