Can human movement analysis contribute to usability understanding?

A new methodology based on functional principal component analysis to study postural stability post-stroke

BACKGROUND

A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke.

METHODS

A randomized controlled pilot trial was developed. Thirty inpatients post-stroke were included. Control and target groups were treated with the same conventional physical therapy protocol based on functional criteria, but specific techniques were added to the target group depending on the subjects' functional level. Postural stability during standing was quantified by posturography. The assessments were performed once a month from the moment the participants were able to stand up to six months post-stroke.

FINDINGS

The target group showed a significant improvement in postural control recovery trend six months after stroke that was not present in the control group. Some of the assessed parameters revealed significant differences between treatment groups (P < 0.05).

INTERPRETATION

The proposed methodology allows Functional Principal Component Analysis to be performed when data is scarce. Moreover, it allowed the dynamics of recovery of two different treatment groups to be determined, showing that the techniques added in the target group increased postural stability compared to the base protocol.

A new methodology for Functional Principal Component Analysis from scarce data. Application to stroke rehabilitation

Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subjects with missing values. In this paper, we present an alternative methodology extracting the FPCs directly from the sampled data, avoiding the need to have functional data before extracting them. We demonstrate the feasibility of our approach from real data obtained from the analysis of balance recovery after stroke. Finally, we demonstrate that FPCA can obtain differences between groups when these differences are more related to the dynamics of the process than data values at given points.

Functional principal component analysis as a new methodology for the analysis of the impact of two rehabilitation protocols in functional recovery after stroke

BACKGROUND

This study addressed the problem of evaluating the effectiveness of two protocols of physiotherapy for functional recovery after stroke. In particular, the study explored the use of Functional Principal Component Analysis (FPCA), a multivariate data analysis in order to assess and clarify the process of regaining independence after stroke.

METHODS

A randomized double-blind controlled trial was performed. Thirteen subjects with residual hemiparesis after a single stroke episode were measured in both in- and outpatient settings at a district hospital. All subjects were able to walk before suffering the stroke and were hemodynamically stable within the first week after stroke. Control and target groups were treated with conventional physiotherapy for stroke, but specific techniques were added for treatment of the target group depending on patients' functional levels.Independence level was assessed with the Barthel Index (BI) throughout 7 evolution stages (hemodynamic stability, beginning of standing, beginning of physical therapy sessions in the physiotherapy ward and monthly assessment for 6 months after stroke).

RESULTS

FPCA was applied for data analysis. Statistically significant differences were found in the dynamics of the recovery process between the two physiotherapy protocols. The target group showed a trend of improvement six months after stroke that was not present in the control group.

CONCLUSIONS

FPCA is a method which may be used to provide greater insight into the analysis of the rehabilitation process than that provided by conventional parametric methods. So, by using the whole curves as basic data parameters, subtle differences in the rehabilitation process can be found.FPCA represents a future aid for the fine analysis of similar physiotherapy techniques, when applied in subjects with a huge variability of functional recovery, as in the case of post-stroke patients.