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Clinical Trial
. 2014 Aug 6;9(8):e104487.
doi: 10.1371/journal.pone.0104487. eCollection 2014.

Automated assessment of upper extremity movement impairment due to stroke

Affiliations
Clinical Trial

Automated assessment of upper extremity movement impairment due to stroke

Erienne V Olesh et al. PLoS One. .

Abstract

Current diagnosis and treatment of movement impairment post-stroke is based on the subjective assessment of select movements by a trained clinical specialist. However, modern low-cost motion capture technology allows for the development of automated quantitative assessment of motor impairment. Such outcome measures are crucial for advancing post-stroke treatment methods. We sought to develop an automated method of measuring the quality of movement in clinically-relevant terms from low-cost motion capture. Unconstrained movements of upper extremity were performed by people with chronic hemiparesis and recorded by standard and low-cost motion capture systems. Quantitative scores derived from motion capture were compared to qualitative clinical scores produced by trained human raters. A strong linear relationship was found between qualitative scores and quantitative scores derived from both standard and low-cost motion capture. Performance of the automated scoring algorithm was matched by averaged qualitative scores of three human raters. We conclude that low-cost motion capture combined with an automated scoring algorithm is a feasible method to assess objectively upper-arm impairment post stroke. The application of this technology may not only reduce the cost of assessment of post-stroke movement impairment, but also promote the acceptance of objective impairment measures into routine medical practice.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Study methodology.
A, illustration of recorded movements. Red lines indicate the direction of motion for joint angles included in the kinematic analysis. B, tracked points used to calculate joint angles. C, Average joint angles of the left arm calculated from the data captured by the two motion capture systems during 10 repetitions of the movement 8) shown in A.
Figure 2
Figure 2. Results of principal component analysis.
Cumulative explained variance and the number of principal components are shown for each movement type across participants (top plot) and for each participant across movement types (bottom plot). Grey dotted lines show results of decomposition of movement of the non-paretic arm, while black solid lines shows results of decomposition of movement of the paretic arm. The principal components were derived from mean data and used to reconstruct data from individual movements.
Figure 3
Figure 3. The comparison between quantitative scores from standard and low-cost motion capture and qualitative scores.
A, Dots show mean scores for each movement and each subject; thick line shows a regression fit. B, Symbols show mean scores for each subject; error bars show s.d. across 10 movements; thick line shows a regression fit.
Figure 4
Figure 4. Decoding performance.
A, Error in predicting each subject's qualitative score from regressions fitted to the rest of the participants. Mean errors are expressed as % of the correct score; error bars show s.d. across 10 movements. B, Symbols show the same data as in Fig. 3B; lines show regressions for datasets with one subject's data point removed. C, Histogram of intraclass correlation coefficients for relationships between individual human raters and the mean qualitative score. D, Colored lines show reducing errors as more raters score movements of the same participants per movement type, limb, and participant.
Figure 5
Figure 5. Relationships between qualitative and quantitative scores for each movement type.
Dots show mean scores across participants, thick lines show linear regressions with their equations and fit statistics above each plot.

References

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