The Fugl-Meyer Assessment (FMA) is a stroke-specific, performance-based following stroke and integrates Brunnstrom’s stages of motor recovery (Gladstone et al. This method of assessment reduces the time required to perform the test. The Fugl-Meyer Assessment (FMA) is a stroke-specific, performance-based NOTE: *The authors have no direct financial interest in any tools, tests or. program were developed for the total Fugl-Meyer motor and sensory assessments; inter-rater reliability was . CRC; and (3) competency testing in which videotapes were submit- . Brunnstrom, a person recovering from hemiparetic stroke.
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Conceived and designed the experiments: Supervision of technical issues during the study: Review the manuscript and suggest some corrections: 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. 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 meyeer to jerky scores. Prediction accuracy for each of the 13 items and correlations between real FMA scores and scores using Kinect were analyzed. Log transformed jerky scores were significantly higher in the hemiplegic side 1. FMA using Kinect is a valid way to assess upper meyet function and can provide additional results for movement quality in stroke patients.
This may brunnstrm useful in the setting of unsupervised home-based rehabilitation. Stroke is a leading cause of disabilities worldwide[ 1 ] and hemiplegia is the most common impairment after stroke, [ 2 ] resulting in upper extremity UE dysfunction. UE impairment is associated with limitation of activities and worse health-related quality of life. This may be associated with barriers including costs, travel and limited use of public transportation due to disabilities.
A home-based virtual rehabilitation system could be a useful alternative for conventional rehabilitation to overcome barriers for outpatient rehabilitation in stroke patients, considering its low cost and greater accessibility.
Tele-based assessments by therapists using video are possible but may necessitate scheduling an appointment with the therapist and would involve additional cost. Some scores during virtual gaming can be used for assessment, but these are not intuitive, are typically not familiar to therapists. FMA is valid, reliable and responsive to change.
Most items in the UE motor domain are based on patient motion, although reflex or resistance has to be measured in a few items. In one previous study, scores brunnstrom from motion data captured by Kinect correlated well with the motor scores in chronic stroke patients.
The primary objectives of this study were to investigate whether Kinect motion data could be used to predict FMA score and whether predicted scores correlated with those measured by an experienced therapist in hemiplegic stroke patients.
Secondarily, the usefulness of Kinect movement quality analysis was investigated. Patients were recruited from December to February Patients were eligible for inclusion if they had unilateral hemiplegia caused by ischemic or hemorrhagic stroke. Patients brunmstrom excluded if they were younger than 18 years of age; had serious medical complications requiring intensive care, such as pneumonia, urinary tract infection, acute coronary syndrome, inability to provide written informed consent and any other conditions that might interfere with participation.
All subjects received detailed information about the study and provided written consent. The individual in Fig 1 provided written informed consent as outlined in the PLOS consent form to publish brunnstrmo picture. This research protocol was approved by the Seoul National University Bundang Hospital institutional review board and was conducted in accordance with the regulatory standards of Good Clinical Practice and the Declaration of Helsinki World Brjnnstrom Association Declaration of Helsinki: The recording program includes subjects’ tedt, recording arm side, assessment item number.
When pushing the record button and starting an item of Fugl-Meyer assessment, upper extremity skeleton of brunntsrom subject can be shown in the monitor. Among the 33 items for UE evaluation, 13 were selected for Kinect motion data recording: One occupational therapist with two-year experience in the FMA test did the evaluations. Subject motion fgul recorded simultaneously by Kinect for all 13 items.
Kinect motion data were saved as brunnstrpm separate file, which is upper-limb joint data including time. The saved data and FMA scores were transferred to an engineering department for analysis. The Kinect depth-sensing camera was operated with a frame-rate of 30Hz and was positioned in front of each subject to track the entire arm during FMA motions.
Before the motion was recorded, the therapist entered subject information including recording arm side and the recording assessment item number into the recoding program. Data were stored sequentially with time for the UE joint positions comprising 31 variables including time, and positions of the head, shoulder center, shoulder, elbow, wrist meyr hand.
Data were saved in text file format. The recorded joint movement data from each FMA assessment were extracted. For the left jeyer, as an example, left hand, left wrist, left elbow, left shoulder, shoulder center and head joint position data were extracted. To match the coordinates of both arms for machine learning, the right side data was mirrored to the left side based on the sagittal plane of the subject.
Then data from both fuugl could be put into the learning system together.
Data recorded at the start and end of each motion were clipped by thresholding of the joint distance between frames. The detailed clipping process is described in the S1 Appendix. To remove the differences of seating locations and to normalize body size, all joint data were transformed by minus of initial shoulder center and by dividing the summation of each body length i.
To predict a FMA score for each assessment item, an artificial neural network ANN among various pattern recognition algorithms was adopted. The prediction target of each item score 0, 1 or 2 was evaluated by one therapist.
In machine learning and cognitive science, ANNs are statistical learning models inspired by biological neural networks that have become popular in solving various problems in diverse fields. In particular, it has been adopted to solve motion recognition problems in computer vision.
To properly classify motion patterns, features must be extracted from the captured motion data, which contains the positional information of every upper limb joint. Angles and distances between two joints for example, hand-shoulder, hand-head and elbow-head are computed from the original position data. Normalized jerky data based on jerky motion analysis is also used as an additional feature.
In particular, bounding area and variance data for each feature are also used because the range of the motion increases as the FMA score increases. The extracted features from motion captured data and the corresponding FMA scores that were evaluated by one therapist were used to train the ANN model. Predicting a score for each assessment depends on different features.
Dimensionality reduction using principal component analysis PCA was performed to distinguish major features from all existing features. The original feature dimension was about with slight variation from item to item. Reduction to between four and 10 dimensions was done for highly associated principle components.
Therefore, different numbers of principal components were used to achieve the best accuracy for each assessment item. Dimensionality reduction is explained in more detail, in the S2 Appendix.
An identical ANN structure i. However, a different number of principal components were selected for each assessment after PCA dimensionality reduction. Thus, the dimensions of input data depended on the assessments.
Experimental data for each assessment were collected from 41 subjects. As both normal side and hemiplegic side data were collected for each subject, 82 motion data captures in total were used to train the ANN model.
Fugl-Meyer Assessment of Motor Recovery after Stroke – Physiopedia
However, the collected score data displayed a skewed distribution for some assessments. Thus, it was not reliable for the validation to merely divide the collected data into training and testing data. 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 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 brunstrom. The overall process of this cross validation is described in more detail in the S3 Appendix.
The evaluation of the movement impairment in this study is based on the integrated squared jerk. The movement of the joint center was used for the Jerky motion analysis. An integrated jerky motion varies greatly with the duration and length distance of the movement.
In order to solve this issue, we generated integrated jerky motion data that meyef dimensionless. The value includes movement times and distance.
Fugl-Meyer Assessment of sensorimotor function – Wikipedia
Therefore, in order to remove the effects, we made the value dimensionless by multiplying the overall duration with the length. The dimensionless value therefore made the original data comparable. The equation for normalized jerk is described in the following equation:. Jerk t is an 18 dimensional vector because subject motion data has 18 variables six joint x three dimension.
Jerk 2 t is two-norm of the jerk vector. Duration is the length of the clipped data. Length is the maximum distance of a position vector time t from the initial time T 1which is fugp greatest difference of motion distance from the start of motion.
Fugl-Meyer Assessment of Motor Recovery after Stroke
A higher jerky score derived from this method indicates more jerky movement Fig 3. Jerky scores during the motion for flexion synergy in FMA were used for analysis. A and B are the results from y-direction hand movements from UE numbers 10 and 61 during the motion for the flexion synergy item in FMA. C is an example of the results of jerky motion analysis. A smooth curve movement like A has a lower jerky score value, whereas a high trembling curve like B has a higher jerky motion score.
Categorical variables are presented as frequencies percentages. Total score of FMA for selected items ranged from 0 to Log jerky scores between hemiplegic and non-hemiplegic side were compared using paired t-test.