Biomedical subjects

A Forner-Cordero

Publications and source records attributed to A Forner-Cordero.

3 recordsLinked to original sources

PubMed2006

Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles.

The number of consecutive strides that can be recorded in measurements of gait have been limited due to the number of force plates and dimensions of the measurement field. In addition, the feet are constrained to land on the force plates. A method to calculate the inverse dynamics from the motion and incomplete information from the ground reaction forces (GRF), vertical component and its application point, is presented and compared to the calculations based on force plate measurements. This method is based on the estimation of the three-dimensional GRF during walking with pressure insoles. RMS errors were lower than 20 W for knee joint power compared to those derived from force plate measurements. The errors were larger during double stance phase due to errors in the application point measured with the insoles. This method, with some technical improvement, could be implemented in new gait analysis protocols measuring several consecutive steps either on a treadmill or over ground, depending on the motion-measurement system, without constraining foot placement.

Adult↗

PubMed2006

Describing gait as a sequence of states.

Traditionally, gait analysis has been based on normalizing the stride time to a percentage and then averaging several strides measured under the same conditions. This procedure relies on the questionable assumptions that gait is a cyclic movement with superimposed noise and that there is no variability in the timing of activation or in the angles within the stride so no rescaling occurs during the percentage conversion. However, there is a fluctuation in the timings at which the peak values occur. A typical hallmark of this time-rescaling is the increase of the joint angle standard deviation when the angular velocity increases. The goal of this paper is to present a description of gait to avoid averaging without distorting the original curves. In addition, it allows the analysis of the fluctuation between consecutive strides. In this method, it is assumed that gait is quasi-periodic. The key point is the representation of gait by a state vector that evolves in time. This state vector can be used to calculate the instantaneous period and provides a measure of the time fluctuations between strides. The sequence of states method describes a quasi-periodic movement like gait with a continuous estimate of cycle time and provides measure of the deviations between cycles.

Ankle Joint↗

PubMed2005

Principal component analysis of complex multijoint coordinative movements.

Principal components analysis (PCA) has not been very much in vogue within the field of movement coordination even though it is useful to reduce data dimensionality and to reveal underlying data structures. Traditionally, studies of coordination between two joints have predominantly made use of relative phase analyses. This has resulted in the identification of principal constraints that govern the Central Nervous System's organization and the control of coordination patterns. However, relative phase analyses on pairwise joints have some drawbacks because they are not optimal for revealing convergent patterns among multijoint coordination modes and for unraveling generic control strategies. In this paper, we present a method to analyze multijoint coordination based on the properties of PC, more specifically the eigenvalues and eigenvectors of the covariance matrix. The comparison between relative phase analysis and PCA shows that both provide similar and consistent results, underscoring the latter technique's sensitivity to the study of coordination performance. In addition, it provides a method for automatic pattern detection as well as an index of performance for each joint within the context of the global coordination pattern. Finally, the merit of the PCA technique within the context of central pattern generators (CPG) will be discussed.

Adult↗