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Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients.

Kim WS, Cho S, Baek D, Bang H, Paik NJ - PLoS ONE (2016)

Bottom Line: Prediction accuracies ranged from 65% to 87% in each item and exceeded 70% for 9 items.Correlations were high for the summed score for the 13 items between real FMA scores and scores obtained using Kinect (Pearson's correlation coefficient = 0.873, P<0.0001) and those between total upper extremity scores (66 in full score) and scores using Kinect (26 in full score) (Pearson's correlation coefficient = 0.799, P<0.0001).Log transformed jerky scores were significantly higher in the hemiplegic side (1.81 ± 0.76) compared to non-hemiplegic side (1.21 ± 0.43) and showed significant negative correlations with Brunnstrom stage (3 to 6; Spearman correlation coefficient = -0.387, P = 0.046).

View Article: PubMed Central - PubMed

Affiliation: Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea.

ABSTRACT
Virtual home-based rehabilitation is an emerging area in stroke rehabilitation. Functional assessment tools are essential to monitor recovery and provide current function-based rehabilitation. We developed the Fugl-Meyer Assessment (FMA) tool using Kinect (Microsoft, USA) and validated it for hemiplegic stroke patients. Forty-one patients with hemiplegic stroke were enrolled. Thirteen of 33 items were selected for upper extremity motor FMA. One occupational therapist assessed the motor FMA while recording upper extremity motion with Kinect. FMA score was calculated using principal component analysis and artificial neural network learning from the saved motion data. The degree of jerky motion was also transformed to jerky scores. Prediction accuracy for each of the 13 items and correlations between real FMA scores and scores using Kinect were analyzed. Prediction accuracies ranged from 65% to 87% in each item and exceeded 70% for 9 items. Correlations were high for the summed score for the 13 items between real FMA scores and scores obtained using Kinect (Pearson's correlation coefficient = 0.873, P<0.0001) and those between total upper extremity scores (66 in full score) and scores using Kinect (26 in full score) (Pearson's correlation coefficient = 0.799, P<0.0001). Log transformed jerky scores were significantly higher in the hemiplegic side (1.81 ± 0.76) compared to non-hemiplegic side (1.21 ± 0.43) and showed significant negative correlations with Brunnstrom stage (3 to 6; Spearman correlation coefficient = -0.387, P = 0.046). FMA using Kinect is a valid way to assess upper extremity function and can provide additional results for movement quality in stroke patients. This may be useful in the setting of unsupervised home-based rehabilitation.

No MeSH data available.


Related in: MedlinePlus

Prediction accuracies(%) of Fugl-Meyer assessment (FMA) scores using Kinect for real FMA scores in each item.
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pone.0158640.g002: Prediction accuracies(%) of Fugl-Meyer assessment (FMA) scores using Kinect for real FMA scores in each item.

Mentions: Using conventional validation, such as fixed partitioning the data set, the error of the training set is not a useful estimator of model performance and the error of the test data is not reliable in various testing data sets. Therefore, to reduce variability, multiple rounds of cross-validation were performed using different partitions. The validation results are averaged over the rounds and derive a more accurate estimate of model prediction performance. In this manner, 8- to 10-fold cross validations for each FMA item were performed. The k-fold means that the sample is randomly partitioned into k subsamples. One of the subsamples constitutes testing data and others are training data. Our cross-validation average error is shown in the prediction accuracy result (Fig 2). The overall process of this cross validation is described in more detail in the S3 Appendix.


Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients.

Kim WS, Cho S, Baek D, Bang H, Paik NJ - PLoS ONE (2016)

Prediction accuracies(%) of Fugl-Meyer assessment (FMA) scores using Kinect for real FMA scores in each item.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4930182&req=5

pone.0158640.g002: Prediction accuracies(%) of Fugl-Meyer assessment (FMA) scores using Kinect for real FMA scores in each item.
Mentions: Using conventional validation, such as fixed partitioning the data set, the error of the training set is not a useful estimator of model performance and the error of the test data is not reliable in various testing data sets. Therefore, to reduce variability, multiple rounds of cross-validation were performed using different partitions. The validation results are averaged over the rounds and derive a more accurate estimate of model prediction performance. In this manner, 8- to 10-fold cross validations for each FMA item were performed. The k-fold means that the sample is randomly partitioned into k subsamples. One of the subsamples constitutes testing data and others are training data. Our cross-validation average error is shown in the prediction accuracy result (Fig 2). The overall process of this cross validation is described in more detail in the S3 Appendix.

Bottom Line: Prediction accuracies ranged from 65% to 87% in each item and exceeded 70% for 9 items.Correlations were high for the summed score for the 13 items between real FMA scores and scores obtained using Kinect (Pearson's correlation coefficient = 0.873, P<0.0001) and those between total upper extremity scores (66 in full score) and scores using Kinect (26 in full score) (Pearson's correlation coefficient = 0.799, P<0.0001).Log transformed jerky scores were significantly higher in the hemiplegic side (1.81 ± 0.76) compared to non-hemiplegic side (1.21 ± 0.43) and showed significant negative correlations with Brunnstrom stage (3 to 6; Spearman correlation coefficient = -0.387, P = 0.046).

View Article: PubMed Central - PubMed

Affiliation: Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea.

ABSTRACT
Virtual home-based rehabilitation is an emerging area in stroke rehabilitation. Functional assessment tools are essential to monitor recovery and provide current function-based rehabilitation. We developed the Fugl-Meyer Assessment (FMA) tool using Kinect (Microsoft, USA) and validated it for hemiplegic stroke patients. Forty-one patients with hemiplegic stroke were enrolled. Thirteen of 33 items were selected for upper extremity motor FMA. One occupational therapist assessed the motor FMA while recording upper extremity motion with Kinect. FMA score was calculated using principal component analysis and artificial neural network learning from the saved motion data. The degree of jerky motion was also transformed to jerky scores. Prediction accuracy for each of the 13 items and correlations between real FMA scores and scores using Kinect were analyzed. Prediction accuracies ranged from 65% to 87% in each item and exceeded 70% for 9 items. Correlations were high for the summed score for the 13 items between real FMA scores and scores obtained using Kinect (Pearson's correlation coefficient = 0.873, P<0.0001) and those between total upper extremity scores (66 in full score) and scores using Kinect (26 in full score) (Pearson's correlation coefficient = 0.799, P<0.0001). Log transformed jerky scores were significantly higher in the hemiplegic side (1.81 ± 0.76) compared to non-hemiplegic side (1.21 ± 0.43) and showed significant negative correlations with Brunnstrom stage (3 to 6; Spearman correlation coefficient = -0.387, P = 0.046). FMA using Kinect is a valid way to assess upper extremity function and can provide additional results for movement quality in stroke patients. This may be useful in the setting of unsupervised home-based rehabilitation.

No MeSH data available.


Related in: MedlinePlus