Identification of knee gait waveform pattern alterations in individuals with patellofemoral pain using fast Fourier transform

Defining characteristics of independent walking persons after stroke presenting with different arm swing coordination patterns

BACKGROUND

Persons after stroke present with an altered arm swing during walking. Given the known influence of the arm swing on gait, it is important to identify the characteristics of persons with stroke with different arm-to-leg coordination patterns during walking.

METHODS

Twenty-five persons after stroke walked on a self-paced treadmill at comfortable walking speed. The frequency of shoulder movements per stride was detected by Fast Fourier transform analysis on the kinematic data for hemiplegic shoulder movements in the sagittal plane. An independent-sample t-test or Mann-Whitney U test was used to compare clinical and biomechanical parameters between identified subgroups.

RESULTS

Two earlier described subgroups based on the number of shoulder flexion-extension movements during one stride could be confirmed. Participants in the 1:1 ratio subgroup (one arm swing during one stride, N = 15) presented with a less upper limb impairment and less spasticity of the elbow extensors (p = 0.012) than the participants in the 2:1 ratio subgroup (two arm swings during one stride, N = 9). Although not significant, the participants in the 1:1 subgroup also seemed to have less spasticity of the shoulder internal rotators (p = 0.06) and a less walking variability based on the standard deviation of the step width. Further research on a greater sample should confirm these findings.

CONCLUSION

Fast Fourier transform analysis was used to identify subgroups based on sagittal shoulder kinematics during walking. The clinical and gait related differences between the identified subgroups can be taken into account in future research investigating post-stroke gait interventions aiming to improve the arm swing.

A guide to inter-joint coordination characterization for discrete movements: a comparative study

Characterizing human movement is essential for understanding movement disorders, evaluating progress in rehabilitation, or even analyzing how a person adapts to the use of assistive devices. Thanks to the improvement of motion capture technology, recording human movement has become increasingly accessible and easier to conduct. Over the last few years, multiple methods have been proposed for characterizing inter-joint coordination. Despite this, there is no real consensus regarding how these different inter-joint coordination metrics should be applied when analyzing the coordination of discrete movement from kinematic data. In this work, we consider 12 coordination metrics identified from the literature and apply them to a simulated dataset based on reaching movements using two degrees of freedom. Each metric is evaluated according to eight criteria based on current understanding of human motor control physiology, i.e, each metric is graded on how well it fulfills each of these criteria. This comparative analysis highlights that no single inter-joint coordination metric can be considered as ideal. Depending on the movement characteristics that one seeks to understand, one or several metrics among those reviewed here may be pertinent in data analysis. We propose four main factors when choosing a metric (or a group of metrics): the importance of temporal vs. spatial coordination, the need for result explainability, the size of the dataset, and the computational resources. As a result, this study shows that extracting the relevant characteristics of inter-joint coordination is a scientific challenge and requires a methodical choice. As this preliminary study is conducted on a limited dataset, a more comprehensive analysis, introducing more variability, could be complementary to these results.

Effects of Individualized Gait Rehabilitation Robotics for Gait Training on Hemiplegic Patients: Before-After Study in the Same Person

Background

Lower-limb exoskeleton robots are being widely used in gait rehabilitation training for patients with stroke. However, most of the current rehabilitation robots are guided by predestined gait trajectories, which are often different from the actual gait trajectories of specific patients. One solution is to train patients using individualized gait trajectories generated from the physical parameters of patients. Hence, we aimed to explore the effect of individual gaits on energy consumption situations during gait rehabilitation training for hemiplegic patients with lower-limb exoskeleton robots.

Methods

A total of 9 unilateral-hemiplegic patients were recruited for a 2-day experiment. On the first day of the experiment, the 9 patients were guided by a lower-limb exoskeleton robot, walking on flat ground for 15 min in general gait trajectory, which was gained by clinical gait analysis (CGA) method. On the other day, the same 9 patients wore the identical robot and walked on the same flat ground for 15 min in an individualized gait trajectory. The main physiological parameters including heart rate (HR) and peripheral capillary oxygen saturation (SpO2) were acquired via cardio tachometer and oximeter before and after the walking training. The energy consumption situation was indicated by the variation of the value of HR and SpO2 after walking training compared to before.

Results

Between-group comparison showed that the individualized gait trajectory training resulted in an increase in HR levels and a decrease in SpO2 levels compared to the general gait trajectory training. The resulting difference had a statistical significance of p < 0.05.

Conclusion

Using individualized gait guidance in rehabilitation walking training can significantly improve energy efficiency for hemiplegic patients with stroke.

Statistical kinematic modelling: concepts and model validity

Data reduction techniques are applied to reduce the volume of data while maintaining its integrity. For cyclic motion data, a reliable overview comparing these methods is lacking. Therefore, this study aims to evaluate the features of the different data reduction techniques by applying them to large public data sets. The periodicity of cyclic motion can be exploited by either analysing a single cycle or studying a series of cycles. Analysing single cycles requires a pre-processing step to isolate the amplitude variability. Three different alignment techniques were evaluated, namely Linear length normalisation (LLN), piecewise LLN (PLLN) and continuous registration (CR). CR showed to remove the most phase variation. For the data reduction, three techniques were assessed (i.e., principal component analysis (PCA), principal polynomial analysis (PPA) and multivariate functional PCA (MFPCA)) based on the in- and out-of-sample error, the compactness and the computation time. The differences were found to be minimal. From our results, PPA appeared to be most useful for data compression. Further, we recommend PCA and MFPCA for classification and feature extraction purposes. We suggest the use of PCA when computation time is key and we advise the use of MFPCA when the inclusion of different data sources is desired. In contrast, the analysis of a series of cycles requires a pre-processing step to decompose the series. Further, a regression model was used to compensate for the difference in fundamental frequency. PCA on FC and MFPCA with splines were applied on the frequency compensated curves. Both methods performed as good.

Using Time-Frequency Analysis to Characterize Altered Knee Dynamics in Post ACL Reconstruction Individuals

More than 250,000 individuals suffer an anterior cruciate ligament (ACL) injury in the United States each year requiring surgery and rehabilitation. However, despite exhaustive rehabilitation individuals are often plagued by neuromuscular deficits that lead to detrimental knee loading and knee osteoarthritis. Traditionally, time domain-based metrics like peak sagittal plane knee angle are used to quantify differences in knee mechanics; however, additional information can potentially be elucidated from time-frequency analyses. Here Smoothed Pseudo Wigner-Ville (SPWV), a time-frequency analysis technique, was used to investigate differences in knee loading dynamics between healthy controls and post ACL reconstruction individuals during running. The results indicated that post ACL reconstruction individuals adopt significantly different loading strategies in their injured limb than their non-injured limb. Individuals adopt a stiffer, more restrictive movement strategy delineated by a stronger low frequency to high frequency (LF/HF) ratio while the non-injured limb exhibit a more oscillatory motion (p<; 0.001). The time domain metrics were unable to identify differences between the ACL injured and non-injured limbs. The ability of SPWV to provide both quantitative and visual means to detect these differences supports its use as a clinical tool to track and monitor joint health.