Article ID: #73568054. Received: July 29, 2022. Revised: August 29, 2022, Accepted: December 10, 2022
Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022
e-ISSN: 2432-5422, p-ISSN: 2188-8116
Applied Science and Computer Science Publications

EMG-Based Recognition Method of Finger Movement Impairment Level in
Post-Stroke Patients Based on Fugl-Meyer Assessment
Adhe Rahmatullah Sugiharto, Shuhei Tsuchida, I Putu Alit Pawana,
Tsutomu Terada, Masahiko Tsukamoto
Abstract: The restoration of finger motor functions is considered difficult during rehabilitation due tothe complexity of the underlying muscles. The Fugl-Meyer Assessment (FMA) method is used bydoctors to manually assess the level of finger movement impairment. However, there is a risk ofevaluation errors due to inherent subjectivity. Therefore, a new, more accurate method must be
developed to predict the level of impairment. This study aimed to evaluate the impairment level of finger
movement based on the FMA. EMG signals were recorded from four patients while performing seven
movements, and feature extraction was performed. SVM and Random Forest were used to classify thelevel of impairment for each movement. The SVM model obtained good results in the fourth movement,with an accuracy of 91.7% and an F1 score of 0.78.
Keywords: impairment level; finger movement; post-stroke patients; recognition.
I.

INTRODUCTION

M

uscle dysfunction is the most common form ofimpairment after a stroke. Muscle dysfunction
increases the risk of paralysis of the arm after ahemiplegic stroke, which is often associated with greater
impairment, work deficiencies, and a lower quality oflife. The proliferation of unused muscle fibers due todysfunction results in abnormal patterns of muscle
activation, such as spastic joint contractions, whichfurther contribute to the disorder [1], [2]. Because fingers
are crucial in many everyday activities, the handicap is
an important factor in the muscular motor function.
People who have suffered a stroke in their arm may
become incapable of performing daily tasks [3].
To restore muscle fiber function after a stroke, it isnecessary to perform rehabilitation as a routine muscle
recovery process [2], [4]. Rehabilitation refers to the
combined and coordinated use of medical, social,educational, and occupational measures to retrain aperson to the highest level of functional skills [4]. The
Fugl-Meyer upper extremity assessment (FMA-UE) is a
widespread method for measuring motor recovery after
a stroke, especially during upper extremity rehabilitation.
The FMA-UE assessment is performed by a doctor or
physiotherapist by directly observing the patient’s finger
movements. However, the subjective nature of thisprocess increases the risk of error. Furthermore, clinicalCorresponding Author:

rehabilitation studies are difficult to conduct, as they
encompass numerous variables that influence choice andoutcome. Some problems limit the comparability ofrehabilitation values arising from descriptive studies,and the control groups in many studies are inadequately
correlated or incorrectly defined [4].
In this regard, this paper presents a Fugl-Meyer
evaluation method (FMA) to assess the impairment levelof post-stroke finger movement using surface EMG. The
proposed method is designed to assist medical
professionals in conducting accurate and objective
evaluations.
II.

RELATED RESEARCH

A. Automation of Fugl-Meyer Assessment
Several studies have been conducted to address the
subjectivity and inflexibility inherent to assessing therehabilitation process. A study in 2014 [5] wasperformed on 24 patients with varying levels of upper
extremity hemiplegia after stroke. An accelerometer
sensor was employed to measure changes in FMA-based
shoulder-elbow acceleration. Patients were asked toperform four tasks related to shoulder-elbow exercises:
flex the shoulders through the full range of motion(ROM), with the elbows at 0° and wrists in the middle

Tsutomu Terada. Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada Ward,
Hyogo, Japan, Tel/ Fax: +81-3-5286-3759, E-mail: tsutomu@eedept.kobe-u.ac.jp425

Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022

Fig. 2 Location of Extensor Digitorum and Flexor
Digitorum Muscles

Fig. 1 Data Collection Environment
position; abduct the shoulder on the entire range of
motion with the elbow at 0° and the wrist in the middle
position; pronation/supination of the wrist with 90°
elbow and 0° shoulder; and move the hand to the lumbar
spine.
Support vector regression (SVR) was implemented
to predict the values of attributes obtained from theaccelerometer signals. Two kernels – RBF andpolykernel – were used to predict 14 selected features.
The comprehensive single-subject cross-validation
model produced the best results, with a standard
deviation of 2.1594 and a mean square error score(RMSE) of 2.1273.
Another study proposed an automatic dataacquisition system specifically for pinch evaluation
based on the FMA protocol. They evaluatedcompression and adhesion using force detection resistors
and displacement sensors [6]. The 50 subjects of thestudy were healthy, right-handed male students with nohistory of upper limb trauma, all of whom completed ahealth questionnaire (SF-36). The study found that the
subjectivity of measurement can be eliminated byreplacing the therapist’s soft pull with a linear drive
subsystem that uses a constant amount of pull. However,the final verdict on the proposed system was the doctor’s
decision, as further studies were required to investigate
the development of slipping and trapping in stroke
patients.

hands-free function showed that the combination ofkinematic and myographic data reached an averageclassification accuracy of 50.5%, compared to 62.0%
obtained only with kinematic data. Results pertaining to
the hand function showed a high classification accuracyof 62.4% with only myographic data. These results imply
that kinematic sensors work well in classifying
movements in non-passive loads for the Fugl-Meierestimation. However, for hand function tasks,
information from muscle sensors, such as MMG, is a
crucial factor in improving the quality of classification.
Based on the aforementioned studies, we developeda recognition method that uses electromyography(EMG) sensors to measure the electrical activity ofmuscles for the purpose of impairment classification infunctional hand tasks.
III. METHODS
A. Participants
EMG data were collected from four stroke survivors
in their 50s: three men and a woman. All subjects werepartially disabled in one arm. Three subjects had
impairment in their left hand, and one had impairment intheir right hand. A Doctor of Physical Medicine andRehabilitation of Dr. Soetomo General Hospital assisted
in all experiment processes, and all appropriate
guidelines were followed. The experimental
environment is illustrated in Fig. 1. All procedures wereapproved by the ethical committee of Dr. Soetomo
GeneralHospital,
Surabaya,Indonesia(No.
0776/116/3/VI/2021).
B. Instruments

B. Automation of Fugl-Meyer Assessment
In 2019, an automated FMA evaluation system wasdeveloped for the motor functions of upper extremities
after stroke [7]. The system uses kinematic informationand mechanomyography sensors (MMG) to build andlearn the motor functions of the hand using a vector
support machine (SVM). A total of 28 subjects withacute stroke were recruited and trained to perform a
motor task based on the FMA-UE. The results for the

EMG signals were collected using two Myoware
muscle sensors (SparkFun Electronics, Niwot, CO,USA) attached to the flexor and extensor muscles, as
shown in Fig. 2. The EMG sensor was powered by a426

Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022
1. Mass
Extension

2. Mass
Flexion

3. HookGrasp

4. Thumb
Adduction

5. Pincher
Grasp

6. Cylinder
Grasp

7. Spherical
Grasp

Fig. 3 Seven Finger Movement Based on Fugl-Meyer Assessment
minimum 3.1 V supply, and supported by a
201Rgain/1kΩ adjustable differential amplifier by apotentiometer [8]. Myoware provides two EMG modes:
Envelope EMG and RAW EMG. Because enveloped
EMG specifically analyzes the amplitude of muscle
activation [9], we employed RAW EMG signals toanalyze both the time and frequency domains.
MyoWare's RAW EMG signal was amplified 201 times
without filtering. Accordingly, signal filtering is required
to obtain a clean EMG signal.
An Arduino UNO, equipped with an analog-todigital converter (ADC), was used to obtain EMG
signals from two Myoware sensors. A 10-ms delay was
introduced in the ADC process of the EMG signal to
avoid ADC overload. EMG data was transferred directly
to the computer and saved in the .csv format using
PyCharm, an open-source integrated development
environment (IDE) for Python programming developedby JetBrains. All data processing and machine learning
simulations were performed in a Python environment.
The subject's finger movement condition wasassessed using the Fugl-Meyer Upper Extremity
Assessment (FMA-UE). The FMA is a widely-used toolfor evaluating clinical rehabilitation in stroke patients [1],[2], and the FMA-UE specifically assesses the subject’s
finger mobility as Full, Partial, or None [10]. As shownin Fig. 3, the following seven movements were
measured: 1) mass extension (ME), 2) mass flexion (MF),3) hook grasp (HG), 4) thumb adduction (TA), 5) pincher
grasp (PG), 6) cylinder grasp (CG), and 7) sphericalgrasp (SG).
C. Data Collection
Two MyoWare EMG sensors were attached to theextensor and flexor digitorum muscles, which control theextension and flexion movements of the fingers,
respectively. The sensor attached to the extensor
digitorum muscle was labeled Channel 1 (Ch1), whereasthat attached to the flexor digitorum muscle was labeled
Channel 2 (Ch2). EMG signals were recorded when thesubjects performed seven movements based on FMAUE. A previous study found that muscle fatigue affects
427

the feature information of EMG signals, with effects that
include an increasing RMS and decreasing mean power
frequency [11]. Therefore, we avoided the issue ofmuscle fatigue by allowing subjects rest periods after
each movement. The resting time ranged between 5 and20 s according to the patients’ preferences. The doctor
assessed the level of impairment for each movement
using FMA-UE, and the assessment was subsequently
used as the ground truth for a machine learning model.
All four subjects exhibited only full and partial levels of
impairment. We adopted the transition phase of muscle
contraction as a classification target. This transition
phase corresponds to a muscle fiber recruitment processthat leads to contraction. Although this phase is notcorrelated with the outcome of the Fugl-Meyer
assessment, it is a factor that indicates the contraction
quality of the muscle [12].
Due to the 10 ms delay in the Arduino UNO, thesampling rate obtained from the sensor was 100 Hz. This
low sampling rate produces an EMG signal with amaximum frequency of 50 Hz, which is within the ideal
range for EMG signals [13].
D. High-Pass Filter (HPF)
Major sources of noise in EMG signals include
motion artifacts, baseline, and power line interference[14], [15]. In this study, power line interference wasignored because the maximum frequency was 50 Hz.
The range of baseline and motion disturbances was
between 0 and 20 Hz. Accordingly, a high-pass filter(HPF) was used to eliminate noise. The HPF process was
implemented using the Python tools from the Scipy
module. The Butterworth high-pass filter was selectedwith a filter order of 5 and a cutoff frequency of 10 Hz.
Fig. 4 shows the EMG signal of the first subject, alongwith the corresponding amplitude response andfrequency. It is apparent that the EMG signal was
contaminated with baseline noise, as that the frequency
below 1 Hz had a very high magnitude. After filtering,
the baseline of the EMG signal was centered at 0 mV,
and the frequency response showed no abnormal

Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022

Fig. 4 EMG Signal and Amplitude Response Before
FilterFig. 5 EMG Signal and Amplitude Response After
Filtermagnitude (Fig. 5). After high-pass filtering, the EMG
data were exported so that each event was stored in adifferent file (Fig. 6).
E. Data Splitting
Upon completion of the export process, data wereallocated among files using the Sklearn module forclassification purposes. Specifically, 50% of the data
were allocated for training, while the remaining 50%
were designated for testing. The split size was selectedto accommodate the small amount of EMG data for theTransition class. Data splitting was performed without
shuffling to avoid randomness.

Fig. 6 Process of Event Exporting into Different Files.
(MET: Mass Extension Transition; MEF: Mass Extension
Full; MFT: Mass Flexion Transition; MFF: Mass Flexion
Full)

G. Classification

F. Feature Extraction
The time and frequency domains were used to extract
important features from the EMG data. A total of sevenfeatures in the time domain (TD) and one feature in thefrequency domain (FD) were extracted from a window
size of 100 ms with a slide of 10 ms. The features
extracted were: mean absolute value (MAV), variance(VAR), RMS, waveform length (WL), slope sign change(SSC), zero crossing (ZC), Willison amplitude (WAMP),
and mean power frequency (MNF) [11], [16]–[18]. Allof these features have been widely employed in priorstudies to obtain accurate classification results. After the
extraction process, data were standardized using astandard scaler, and concatenated into a training file andtest file.

428

As traditional machine leaning algorithms, thesupport vector machine (SVM) and random forest (RF)algorithms are capable of recognizing complex patterns
in the field of EMG signal classification [19]–[22]. To
ensure that the final output of each classifier was focusedon the impairment level, we built a machine learningmodel based on each movement. However, the amountof data for each target class was unbalanced, resulting inpoor model performance. To mitigate this issue, we
employed the synthetic minority oversampling technique(SMOTE) to generate synthetic data, thus equalizing theamount of data in each target class. SMOTE performs
oversampling by synthesizing nearest-neighbor data forthe class that has the least amount of data. This processcontinues over several iterations to ensure an equivalentamount of data in each class. This technique isconsidered a pioneer data balancing method, as it avoids
overfitting of the classifier model [23].

Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022
Table 1 Best Parameters of SVM and RF for Each
Movement
Movement
1
2
3
4 5
6
7
Kernel Linear RBF Poly Poly Poly Poly PolyC
1000 1010
10 1000 10
1
Gamma 0.001 1
0.1
1 1
1
1
Degree
0
0
0
3 2
2
3
Criterion Gini Gini Entropy Gini Gini Entropy Gini
Number of
700 100 100 100 500 1000 700
Tree

Method Parameter

SVM

RF

Table 2 Accuracy Score of Each Classifier andFinger Movement
MethodSVM
RF

Movement (%)
Mean
1
2
3
4
5
6
7
67.4 45.2 48.4 91.7 69.9 80.1 66.8 67.1
61.2 41.0 44.4 90.1 77.0 80.8 57.6 64.6

Table 3 F1 Score for Each Movement and Machine
Learning Method
Target Class
Method Movement

mean

std

Full Partial None Transition

SVM

Fig. 7 Data Processing Flow
For SVM and RF, we employed the hyperparameterssuggested in [16], and tuned them using a randomized
search process with ten-fold cross-validation in the
training dataset, as shown in Fig. 7. Optimal parameters
were then selected based on the F1-micro score. For the
SVM classifier, we adjusted the kernel (linear,
polykernel, and rbf), polykernel degrees (1, 2, and 3), C
value (1, 10, 100, and 1000), and gamma value (1, 0.1,
0.01, 0.001). For the RF classifier, we adjusted thecriteria (Gini and Entropy) and number of trees (100, 300,
500, 700, 1000).
IV. RESULTWe developed a machine-learning-based classifier toassess the impairment level of subjects' finger mobility
using SVM and RF. In this study, the target classes
learned and recognized by the machine learning models

RF

1

0.60

0.58

0.00

0.99

0.72

0.23

2

0.32

0.54

0.00

0.53

0.46

0.12

3

0.54

0.52

0.00

0.18

0.41

0.20

4

0.98

0.83

0.00

0.53

0.78

0.23

5

0.85

0.20

0.00

0.00

0.35

0.44

6

0.88

0.58

0.00

0.42

0.63

0.23

7

0.76

0.74

0.00

0.19

0.56

0.32

1

0.57

0.41

0.00

1.00

0.66

0.31

2

0.29

0.45

0.00

0.88

0.54

0.31

3

0.38

0.61

0.00

0.19

0.39

0.21

4

0.99

0.70

0.00

0.57

0.75

0.22

5

0.90

0.40

0.00

0.00

0.43

0.45

6

0.89

0.60

0.00

0.59

0.69

0.17

7

0.70

0.49

0.00

0.11

0.43

0.30

were Full, Partial, and Transition. Because FMA-UE
includes the None condition for hand evaluation, we
assigned the corresponding None class a value of zero in
the confusion matrix. Therefore, performance metrics
were calculated only for the Full, Partial, and Transition
classes.
A. Classifier Parameters
The machine learning model’s parameters were
optimized using a randomized search procedure based on

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Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022

Fig. 8 Confusion Matrix of The Highest Accuracy Score for 7 Finger Movement of Each Classifier
the F1-micro score (Table 1). The maximum value of the
C parameter for the SVM classifier was 1000, while the
gamma parameter varied in the specified range from 1 to
0.001. The degree parameter worked only for polykernelparameters. Hence, only the SVM model for Movements
4, 5, 6, and 7 outputs degree parameters 2 and 3. For RF
parameters, the maximum number of trees is 1000,
which is achieved by Movement 6. Entropy is the best
criterion parameter of Movements 3 and 6. Meanwhile,
the Gini criterion is the best parameter for Movements 1,
2, 3, 4, 5, and 7.

Movement 2 by RF. Furthermore, both machine learningmodels exhibited the best performance for Movement 4,
whereas the performance for Movements 2 and 3 wasrelatively poor. To further examine the performance ofboth models, we need the corresponding F1 scores.
Table 3 lists the F1 scores of each impairment level
per movement. By averaging the results, we found thatboth classifiers obtained the highest F1 scores for
Movement 4, which correlates with the corresponding
accuracy scores. The RF classifier showed better
performance in classifying the impairment level in
Movements 5 and 6.

B. Classifier Performance
As shown in Table 2, the highest accuracy of 91.7%
was obtained for Movement 4 by the SVM classifier,whereas the lowest accuracy of 41.0% was obtained for430

Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022
of 0.00. Although misclassification was also observed
for the Partial class, the corresponding F1 scores arehigher than those of the Transitional class due to a larger
amount of data. Both classifiers showed biased results
for the Partial class.
To compensate for the data imbalance between
classes, a 50% data split was performed. This condition
results in a small number of features learned by themodels. Another study recommends allocating 90% of
the data to the training dataset and 10% to the test dataset[16]. The expected condition also requires more EMG
data by increasing the sampling rate. Although thesampling rate used in this study was still in the Nyquist
frequency range of EMG [13], a higher sampling rate isrequired to acquire more EMG data within 1 s.
B. The Comparison of The Established Machine
Learning Model with The Related Research
Fig. 9 Comparison of Average Accuracy Score withAnother Related Research [7]In this study, we also evaluate the performance of theestablished machine learning model by comparing the
average accuracy score with the previous related study.
A study by Formstone (2019) [7] was selected as thecomparison since the related study also focused on the
impairment level of finger movement based on the FuglMeyer Assessment.
As shown in Fig. 9, the previous study achieved anaverage accuracy of 62.4% using an SVM classifier.
Subsequently, the proposed method achieved a higher
average accuracy of 2.2 for RF and 4.7 for the SVM
model. The result confirmed that the proposed method
showed relatively better results by employing the EMG
signal as the modality rather than MMG. Furthermore,
the hyperparameter process conducted in this study
enhanced the performance of both RF and SVM byselecting the most suitable parameters according to thedataset. Thus, the established SVM model in this study
showed a higher score than the previous study.

V. DISCUSSION
A. The Performance of The Established Machine
Learning ModelIn this study, we developed a recognition method toevaluate the finger mobility of stroke patients. Twomachine learning methods were implemented to classify
three target classes for each movement. The SVM
method achieved the highest accuracy and F1 score forthe fourth movement (thumb adduction). These results
indicate that the detection of impairment levels fromfinger movements of post-stroke subjects is feasible.
The Transition class corresponds to a change phase fromthe resting state to the contraction state and is also known
as the recruitment of motor units. The duration of thisphase is very short in healthy humans, ranging from 80
to 150 ms [24]. Because the sampling rate used in thisstudy acquired one EMG data every 10 ms, only 8–15
EMG data could be obtained for the transition phase.
However, this phase may have a longer duration in thecase of disabled muscles. Consequently, a variety of F1scores were obtained for the Transition class. While both
RF and SVM achieved high F1 scores in this class forthe first movement, the transitions of several othermovements were poorly classified.
Fig. 8 shows that Transition class for Movements 2,
4, and 7 was mostly aligned with the Full class. For
Movement 5, the transition class was incorrectlyclassified as Full, and both classifiers achieved F1 scores

C. Real-Time Implementation of The Proposed
MethodFig. 10 illustrates the use case of the proposed
method in deciding the impairment level of finger
movement. In the first step, a doctor will perform amanual assessment of the patient's impairment level.
Simultaneously, a recognition system will capture thepatient's EMG signal and subsequently classify the
impairment level for the corresponding finger movement.
Finally, the doctor must conduct double-check the result
from the manual assessment with the output of the431

Journal of Bioinformatics and Neuroscience (JBINS), 8 (1): 425-433, December 30, 2022
VI. CONCLUSION AND FUTURE WORKIn this study, a recognition method was designed toevaluate the finger mobility of post-stroke patients
through Fugl-Meyer assessment. The SVM and RFclassifiers were employed to detect all target classes inthe study. Both classifier parameters were optimized via
10-fold cross-validation (CV) of the randomized searchmethod. The SVM classifier achieved the highest
accuracy and mean F1 score for Movement 4 (thumb
adduction), with values of 91.7% and 0.78, respectively.
The RF classifier achieved the highest accuracy and F1score for the same movement, with values of 90.1% and0.75, respectively. In the following study, more EMG
data should be collected from post-stroke patients,
especially those with impairment levels of None, and ahigher sampling rate should be employed.

ACKNOWLEDGEMENTS
Fig. 10 Overview of Real-Time Implementation of
The Proposed Method

This work was supported by JST, CREST Grant
Number JPMJCR18A3, Japan.

system before deciding on the medication procedure.
In this study, the proposed method was aimed at
assisting the doctor in making a proper judgment of thepatient's finger movement impairment level. Therefore,
there will be two conditions in the double-checking
process:
1. Doctor and System Output The Same Result
In this case, the system strengthens the result
from manual assessment. Therefore, further
treatment can be confidently decided.
2.

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D. Limitation
There were several limitations in this study. In theexperiment, the number of involved patients wasconsidered to be low. In addition, we couldn't obtain apatient with None impairment level. Lastly, several
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