Use of Surface Electromyography as a Tools to Predict Upper Extremity Recovery Function After Stroke
Conditions: Upper Extremity Paresis, Muscle Contracture Upper Arm, Stroke
Sponsor: Universitas Padjadjaran

 
 
USE OF SURFACE ELECTROMYOGRAPHY  
AS A TOOLS TO PREDICT UPPER EXTREMITY  
RECOVERY FUNCTION AFTER STROKE 
 
 
 
 
Written by: 
Berthy Al Mungiza, dr. 
132021210501 
 
 
RESEARCH PROPOSAL 
 
 
 
To fulfill the exam requirement to obtain a specialist degree in  
Physical Medicine and Rehabilitation program  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
PHYSICAL AND REHABILITATION MEDICINE RESIDENCY 
PROGRAM FACULTY OF MEDICINE PADJADJARAN UNIVERSITY 
Dr. HASAN SADIKIN GENERAL HOSPITAL BANDUNG  
2023 

 
 
 
APPROVAL SHEET 
 
 
USE OF SURFACE ELECTROMYOGRAPHY  
AS A TOOLS TO PREDICT UPPER EXTREMITY  
RECOVERY FUNCTION AFTER STROKE 
 
 
 
Written by: 
Berthy Al Mungiza, dr. 
132021210501 
 
RESEARCH PROPOSAL 
 
 
 
To fulfill the exam requirement to obtain a specialist degree in  
Physical Medicine and Rehabilitation program  
 
 
After being checked and observed this research proposal has fulfilled the 
qualifications to be examined.  
 
 
      
 
 
 
      
Supervisor I 
 
 
 
 
 
 
Dr. Vitriana Biben, dr., Sp.K.F.R, NM(K) 
Supervisor II 
 
 
 
 
 
 
Dr. Med. Sc. Irma Ruslina Defi, dr., 
Sp.K.F.R., Ger(K) 
Supervisor III 
 
 
 
 
 
 
Dr. Lisda Amalia, dr., Sp.S(K) 
Supervisor IV 
 
 
 
 
 
 
Nurvita Trianasari, PhD, S.Si, 
M.St 

1 
 
 
USE OF SURFACE ELECTROMYOGRAPHY  
AS A TOOLS TO PREDICT UPPER EXTREMITY  
RECOVERY FUNCTION AFTER STROKE 
 
Vitriana B, Irma R.D, Lisda A, Nurvita T, Berthy A 
 
 
INTRODUCTION 
 
Study Background  
 
In the past 3 decades, the disease pattern in 80% of developing countries is 
shifting from communicable to noncommunicable diseases, of which stroke is one 
of the most common debilitating diseases, the second most common cause of death, 
and the third most common cause of disability adjusted life years.1 Compared to 
other countries in Southeast Asia, Indonesia has the highest age- and sex-
standardized mortality (193.3/100,000) and disability-adjusted life years lost 
(3,382.2/100,000), which are caused by stroke.2 
In the majority of stroke patients, the upper limb is more severely involved 
than the lower limb, as most strokes occur in the territory of the middle cerebral 
artery. Lawrence et al. found upper limb weakness in 77% of patients and lower 
limb weakness in 72%.3,4 About half of all stroke survivors also report challenges 
in upper extremity motor function six months post-stroke, which can include 
hemiparesis, spasticity, co-contraction, pain, or other limitations which impact 
quality of life.5  
Clinicians and patients struggle to determine if and when muscle activity 
and movement will return after a stroke. During the early weeks, movement can 
return rapidly and seemingly unexpectedly, which makes every twitch or sensation 
a potential positive sign.6  

2 
 
 
 
Prediction which applied early, and preferably within the first week after 
stroke onset are most useful for rehabilitation and discharge planning. Early 
prediction is also important when considering the constantly decreasing length of 
hospital stay and improved acute care. The median time in stroke unit was reported 
to be 7 days in Sweden and 2–8 days in Australia in 2019. These numbers point out 
the need to implement simple and informative prognosis indicators during the first 
days after stroke onset.7 
In acute care, function-based clinical exams remain the standard for 
evaluating and monitoring muscle activity and movement. The Manual Muscle Test 
(MMT) and NIH Stroke Scale (NIHSS) are among the most common evaluation 
measures used in the United States. instructions. The Fugl-Meyer Assessment 
(FMA) also expands the repertoire of movements to evaluate synergistic or other 
inappropriate muscle activity. The FMA has shown promise for predicting recovery 
and future function of upper extremity in stroke patients.6 
Individuals with no volitional movement, however, the FMA-UE or MMT 
alone cannot determine whether muscle activity is present. MMT score of 0 can 
indicate severe paresis with no muscle activity, that the muscle activity is below the 
level necessary to produce observable contractions, or that the patient was unable 
to fully participate in the assessment. Patients with an MMT of 0, this can be 
frustrating as the patient and their clinical team take a “wait-and-see” approach to 
determine whether muscle activity will return.8  
Ongoing research continues to explore areas of brain plasticity, neural 
recovery mechanisms, and prognostication of stroke outcomes to maximize 

3 
 
 
 
recovery, while there has been simultaneous advancement in wearable sensor-based 
technologies that provide additional, non-invasive means of examining 
neuromuscular pathways and recovery processes following stroke.5  
One 
of 
these 
wearable 
sensor-based 
technologies 
is 
surface 
electromyography (sEMG). Surface electromyography (sEMG) provides a non-
invasive window into the nervous system that can be used to monitor muscle 
activity, but is rarely used in acute care. Steele et al have demonstrated that 
deploying sEMG in acute care is both feasible and useful. They found that current 
technology can be used to comfortably and non-obtrusively monitor muscle 
activity, even for patients with no detectable muscle activity by traditional clinical 
assessments. They perceive opportunities in using sEMG to inform prognosis, 
enable biofeedback training, and provide metrics necessary for supporting and 
justifying care for further research.6 
Problem Statement 
Nijland et al subsequently carried out a prospective study to determine if 
outcome in terms of upper limb function at 6 months after stroke can be predicted 
using clinical parameters measured within 72 hours after stroke in hospital stroke 
units.  This study found that the probability of an ARAT score of at least 10 out of 
57 at 6 months poststroke could be predicted by paretic finger extension (binarized 
at a score of 1 or more out of 2 on the UE-FMA) and paretic shoulder abduction 
(binarized at a score of at least 9 out of 33 on the Motricity Index) evaluated 2 days 
poststroke. The authors present a helpful table for making predictions for individual 
patients. The clinical usefulness of making this prediction, however, is unclear as 

4 
 
 
 
the range of functional abilities represented by an ARAT score between 10 and 57 
is very wide, and neither model has been validated.9  
Stinear et al combined clinical predictors and corticospinal tract biomarkers 
to predict 1 of 4 upper limb functional outcome categories at 3 months poststroke. 
The Predict Recovery Potential (PREP) algorithm was developed in a study of 40 
participants and externally validated in a subsequent study of 157 participants. The 
PREP2 algorithm begins with the Shoulder Abduction, Finger Extension score 
(SAFE) determined within 72 hours of stroke. Each of these movements made with 
the paretic upper limb is evaluated with manual muscle testing and scored out of 5 
with the Medical Research Council grades. The scores are summed to determine 
the SAFE score, and if the score is at least 5 out of 10, the patient’s age is all that 
is required to predict either an Excellent or Good upper limb outcome, but if the 
SAFE score is <5 out of 10, then transcranial magnetic stimulation is needed to test 
the function of the corticomotor system. A good outcome is predicted if motor-
evoked potentials can be elicited in the paretic upper limb. The NIHSS score is used 
to predict either a limited or poor upper limb outcome if motor-evoked potentials 
cannot be elicited. The PREP2 algorithm is accurate for 75% of patients, and using 
it in clinical practice increases therapist confidence, allows tailoring of upper limb 
therapy, and is associated with a reduced length of stay. A recent follow-up study 
found that PREP2 predictions remained accurate for most patients at 2 years 
poststroke. A major limitation of PREP2 is that transcranial magnetic stimulation is 
not readily available in many clinical settings.9  

5 
 
 
 
Adopting wearable sensors likes sEMG in acute care will ultimately require 
that these sensors provide unique and valuable insights that are not available with 
current methods. Beyond detecting muscle activity, longitudinal evaluations will 
determine the diagnostic and prognostic value of sEMG in acute care.8  
Christina Papazian et al. study found that monitoring muscle activity with 
dual mode sensors of sEMG during acute care could detect contractions in the major 
arm muscles of stroke survivors, even among those who had no observable activity 
from clinical examination. In their exploratory analyses, they found modest 
correlations between contraction characteristics and future function for the 
participants with an MMT of 0.8 
Regarding the accessibility of sEMG devices, Hachi et al found that its met 
predefined criteria of being portable, affordable, and easy to operate. This suggests 
that accessibility or price does not constitute in itself a barrier for the use of sEMG 
in the clinical practice.10 Recently, clinicians have recognized the potential of 
sEMG as a prognostic tool, but there is limited study has explained correlation of 
sEMG parameters as a predictor of upper extremity motor recovery in adult acute 
stroke patients. 
 
Research Question 
More specifically, the following research question needs to be addressed :  
Can sEMG parameters predict upper extremity motor function recovery in acute 
stroke patients? 
 

6 
 
 
 
Study Objective 
The aim of the study are: 
To know if sEMG parameters can predict upper extremity motor function 
recovery in acute stroke patients 
 
Study Benefits 
This study has numerous benefits, it can help us gain a better understanding of 
complex issues, develop new technique, determine discharge planning, improve 
outcome of rehabilitation program, and inform public policy.  
The results of this study are expected to enrich our knowledge and become a 
base for future research. The results of this study are also expected to be an aid in 
determine prognostication to promote functional outcomes in stroke patients. 

7 
 
 
LITERATURE REVIEW 
 
Upper Extremity Motor Impairment Post Stroke 
Upper extremity weakness is the most common impairment of stroke 
patients, which plays an important role in motor function deficiency and has a 
detrimental effect on the quality of life.11,12  
Upper extremity impairments after stroke are the cause of activity 
limitations, participation restrictions, and reduced independence in daily life with 
regard to use of the affected upper extremity after stroke, so a clear understanding 
of the underlying impairments is necessary to provide appropriate treatment. 
Understanding upper extremity impairments in any given patient is complex for 
two reasons:13  
1) The impairments are not static, for example as motor recovery proceeds, the 
type and nature of the impairments may change, therefore the treatment needs 
to evolve to target the impairment contributing to dysfunction at a given point 
in time.  
2) Multiple impairments may be present simultaneously, for example a patient 
may present with weakness of the arm and hand immediately after a stroke, 
which may not have resolved when spasticity sets in a few weeks or months 
later, hence there may be a layering of impairments over time making it 
difficult to decide what to treat first.  

8 
 
 
 
It is useful to review the progression of motor recovery as described by 
Twitchell and Brunnstrom to understand how impairments may be layered over 
time (Figure 1).13 
 
Figure 1. Sequential progression of motor recovery. Quoted from: Raghavan P. 
Upper Limb Motor Impairment After Stroke. Phys Med Rehabil Clin N Am. 
2015;26(4):599-610 
 
Prabhakaran et al. proposed a proportional recovery rule. According to this 
rule, the majority of stroke survivors are expected to recover approximately 70% of 
their maximum potentials at 3 months after a stroke.14 
A rule of thumb in stroke recovery is that patients with mild deficits are 
more likely to make a good recovery than patients with initially more severe deficits 
(Figure 2). The most significant improvements occur in the first few week post-
stroke, often reaching a relative plateau after 3 months with less significant recovery 
subsequently. After 6 months, spontaneous recovery is usually at its limit, leading 
to a more or less stable, for example, chronic deficit.15 

9 
 
 
 
 
Figure 2. Motor recovery after stroke in a sample of n = 412 ischemic stroke 
patients based on the Fugl-Meyer upper extremity (FM-UE) score. Quoted from: 
Grefkes C, Fink GR. Recovery from stroke: current concepts and future 
perspectives. Neurol Res Pract.2020;2:17 
 
Recent studies confirmed that the patient clinical features assessed at the 
baseline, predicted the final outcomes of the motor treatment.16 Some of the 
following features have positive predictive value on upper extremity prognosis after 
stroke, and one of them is the presence of any muscle contraction when attempting 
to perform a movement.17  
Ability to volitionally perform a movement of in individual’s suffering 
stroke usually done through manual muscle testing (MMT). Manual muscle testing 

10 
 
 
 
is recommended for initial assessments because it is quick and provides 
standardized measures of motor function.12  
MMT is performed with the hands of the therapist or physician.  MMT use 
a five-point scale defined as normal, good, fair, poor, trace and zero, and assesses 
muscles that perform a joint motion, rather than individual muscles as described in 
Table 1.18,19 The required assessment time is shorter, and at the same time, important 
information is provided about the patient’s muscle strength.18 Some MMT can be 
performed in the supine position.20  
Table 1. Manual Muscle Testing Scoring System. 
Grade 
(%) 
Qualitative 
value 
Muscle strength 
5 
100 
Normal 
Complete range of motion (ROM) against gravity, 
with full resistance 
4 
75 
Good 
Complete ROM against gravity, with some resistance 
3 
50 
Fair 
Complete ROM against gravity, with no resistance 
2 
25 
Poor 
Complete ROM with a gravity omitted 
1 
10 
Trace 
Evidence of slight contractility, with no joint motion 
0 
0 
Zero 
No evidence of muscle contractility 
Quoted from : Brown M, Hislop H, Avers D. Daniels and Worthingham's muscle Testing-
E-Book: Techniques of manual examination and performance testing: Elsevier Health 
Sciences; 2013 
 
Currently, besides MMT, patients with upper extremity motor impairment 
also assessed mainly by clinical scales, with the Fugl–Meyer test being one of the 
most commonly used measures of motor impairment after stroke to provide a more 
detailed description of the patient’s clinical condition. The functional diagnosis 

11 
 
 
 
allows the medical practitioners to set the post-stroke patients’ rehabilitation 
objectives, and can also provide a perspective on the future rehabilitation potential 
and prognosis, and on the necessary timeframe.18  
Fugl-Meyer Assessment (FMA) is the first stroke-specific assessment tool 
that was developed on the basis of Brunnstrom’s motor recovery stages. It is a 
feasible, well-designed, responsive, and efficient tool. The FMA is considered by 
many in the field of stroke rehabilitation to be one of the most comprehensive 
quantitative measures of motor impairment following stroke, and its use has been 
recommended for clinical trials of stroke rehabilitation.21  
The FMA is based on the natural progression in post-stroke hemiparesis. 
The upper extremity subsection of FMA (FMA-UE) is commonly used as compared 
with other parts in post-stroke patients. The FMA-UE comprises 33 items related 
to the movements of proximal and distal upper extremity. The maximum motor 
performance score is 66 points for upper extremity. Scoring is done on a 3-point 
ordinal scale ranging from 0 (item cannot be performed) to 2 (items can be 
performed faultlessly) by the direct observation of the movement performance. The 
FMA-UE requires 10-20 min for its administration.21  
The FMA-UE assesses the post-stroke subjects as per the sequential 
recovery stages. The items are hierarchically organized from synergistic to 
voluntary movements. Synergistic movement exhibits an abnormal stereotyped 
behaviour that does not allow the combination of different movement patterns, for 
example, an attempt to raise the arm results in elbow flexion, shoulder abduction 
and internal rotation. The components of flexor and extensor synergy are tested 

12 
 
 
 
before the movements combining synergies and out of synergy. Each item of FMA-
UE subsection, further, has multiple components that allow an examination of the 
wide and complex motor changes.21  
 
Surface Electromyography 
Electromyography (EMG) is the recording of the electrical activity of 
muscles, and therefore constitutes an extension of the physical exploration and 
testing of the integrity of the motor system. It can be said that surface 
electromyography (sEMG), sometimes called kinesiological electromyography, is 
the electromyographical analysis that makes it possible to obtain an electrical signal 
from a muscle in a moving body.22 
Surface EMG’s is only requiring adequate electrode contact with the skin to 
function. This sensing capability does not require sophisticated hardware and can 
be made either in a benchtop or portable scale. It does not require dedicated 
personnel to operate, and can be constructed cost-effectively for a variety of 
applications.23  
When the brain instructs the body to move, it sends an electrical impulse 
signal down the spinal cord and through an intricate network of peripheral nerves 
to the targeted muscle. This neuronal signal is transduced into a muscular 
contraction by numerous neurons known as motor units, each consisting of a motor 
neuron (anterior horn cell), its axon, and all the individual muscle fibres it 
innervates (Figure 3). Upon arrival of the electrical impulse from the brain, the 
motor units quickly depolarize the cell membrane space of their respective axon 

13 
 
 
 
terminals, leading to a propagating action potential wave that travels across the 
muscle fibres. Since an activation impulse from the brain can recruit multiple motor 
units, all the resultant motor unit action potentials (MUAPs) become superposed as 
their electrical signals radiate through the muscle. The resultant electrical signal can 
be sensed on the skin by the surface of electrodes, giving the characteristics EMG 
signal. Smaller muscle motions with fewer recruited motor units therefore exhibit 
low amplitude EMG signals, while large muscle motions conversely result in high 
amplitude EMG signals.23  
 
 
Figure 3. A single motor unit and the muscle fibers it innervates. Quotes from: 
Zheng M, Crouch MS, Eggleston MS. Surface Electromyography as a Natural 
Human–Machine Interface: A Review. IEEE Sensors Journal. 2022;22(10):9198-
9214. 
 
Steele et al. study showed that among 21 patients they were monitored, 
muscle contractions were detected from all five muscles (deltoid, biceps, triceps, 
wrist flexors, and wrist extensors) during a single four hours collection period 

14 
 
 
 
during standard care even for the 11 patients who had an MMT score of zero, 
indicating no voluntary movement or muscle activity detected via palpation. In the 
patients who were initially flaccid, they did find moderate correlations between 
early contraction characteristics and scores on the MMT at follow-up. These 
findings indicate that muscle activity is present during the first week after stroke, 
even among participants characterized as flaccid, and EMG can provide 
quantitative metrics that may have prognostic value for predicting future function.6  
The use of sEMG involves three phases (the preparation phase, the 
recording phase and the processing phase) which are described below:22  
1. Preparation phase 
a) Preparation of the individual and the provision of information in advance. 
It is essential to adequately inform the individual about the procedure to be 
followed during the recording session and about particular aspects of the 
study, such as its objectives, usefulness and possible applications. It is 
necessary to obtain the individual’s informed consent in writing. 
b) Preparing the skin.  
The skin’s impedance must be reduced so as to obtain a good quality 
electrical signal. It is advisable to shared and rub the skin with an abrasive 
gel to reduce the dry layer of the skin or dead cells, and to eliminate sweat 
by cleaning the skin with alcohol. 
c) Positioning the electrodes 
It is essential to position the electrodes correctly to obtain a good signal. 
The best position, provided it is possible, is on the mid line of the muscle 

15 
 
 
 
belly, between the myotendinous junction and the motor point. The 
published guides like Surface Electromyography for the Noninvasive 
Assessment of Muscles (SENIAM) guidelines can be used to ensure the 
methodology is followed correctly. It is very important to always use the 
same position on different individuals and for different recordings taken 
from the same individual, given that the signal recorded varies according to 
the area of the muscle on which the electrodes are positioned. It is also 
advisable to maintain an optimum distance between the electrodes. Each 
muscle is examined by positioning two electrodes on it, separated by a 
distance of one or two centimetres. 
2. Recording phase 
Recording is the phase in which the electromyographic signal corresponding 
to the action or movement being studied is obtained 
3. Processing phase 
Processing the signal is proses that the raw signal has to be prepared so that it 
can be easily observed and analysed. The type of processing will depend on the 
type of analysis we wish to make from the trace.  
 
Many different quantitative parameters can be evaluated from sEMG data 
such as number of contractions, contraction magnitude, contraction duration, 
presence of synergistic activations, evaluations of spasticity, or measures of 
voluntary vs. involuntary contractions.6 

16 
 
 
 
Surface EMG’s signal parameters related to muscle contraction include time 
analysis and amplitude analysis. The time analysis includes length of contraction. 
The length of contraction is a parameter for evaluating the period of muscle 
contraction from manually identified at start until end times.8  
Papazian et al. successfully used the number of contractions, maximum 
amplitude, median amplitude, and duration from the manually identified start and 
end times the contractions to measured muscle activity in acute stroke patients.  
They also evaluated the correlation between the number of contractions, amplitude, 
and duration. The participants with an MMT of 0, individuals with more 
contractions had greater maximum amplitude and longer duration contractions.8 
The primary advantage of sEMG along with its non-invasive nature, is that 
the signals are relatively straightforward to record and analyse (with some basic 
signal processing knowledge). It also provides an estimate of the overall activity of 
a muscle or group of muscles in contrast to the more selective nature of 
intramuscular recordings.24 
Surface EMG, however, is only suitable for recording from superficial 
muscles and is not appropriate for recording deep muscle activity. Recording from 
small muscles without contamination from surrounding muscles can be difficult and 
signals can be prone to cross-talk, particularly over regions where there is 
substantial subcutaneous fat.24 
Surface EMG’s signal has complex nature, and the huge number of 
variables upon which it depends, so it is important to critically analyse the signal 
knowing the effects of these variables on its properties. Several years now, an 

17 
 
 
 
increasing number of methodological studies have been undertaken in order to 
clarify the role of certain experimental factors that can modify signal 
characteristics, thereby biasing their interpretation. These factors can be classified 
according to their level of influence on the signal:25  
1. Technical 
level: 
environmental 
conditions 
(temperature, 
humidity, 
electromagnetic fields) and technical specifications of the equipment 
(electrodes, electrode-skin interface properties, amplifiers, filters, data 
acquisition card) 
2. Experimental level: measurement procedure (skin preparation, electrode 
configuration, electrode localisation and orientation) and contraction 
conditions (ergometer utilised, contraction type, muscle length, contraction 
level, exercise duration) 
3. Descriptive level: signal processing (digitisation, signal characteristics, chosen 
parameters, used parameter estimates), statistical data analysis (statistical tests) 
4. Physiological level: physiological characteristics of the neuromuscular system, 
either structural (diameter of the active fibres, spatial organisation of fibres in 
the motor unit and of motor units in the muscle, filtering properties of the 
tissues, etc) or functional (myotypology, motor unit recruitment, fatigue, 
muscular coordination, etc.) 
 
 

18 
 
 
 
Conceptual framework 
 
 
 
Figure 4. Conceptual framework 
Premise 
Based on the above frame of mind, several premises are compiled as follows: 
Premise 1 
: Upper extremity weakness is the most common impairment of 
stroke patients.11 
Premise 2 
: The most significant improvements of upper extremity motor 
function occur in the first few week post-stroke, often reaching a relative plateau 
after 3 months with less significant recovery subsequently.15 
Premise 3  
: The presence of any muscle contraction when attempting to 
perform a movement has positive predictive value on upper extremity prognosis 
after stroke.17 
Premise 4 
: Muscle contraction is present even among stroke patients 
characterized as flaccid.6 

19 
 
 
 
Premise 5 
: Surface EMG is useful for the initial evaluation of muscle 
contraction even for the stroke patients who had an MMT score of zero.6,23 
 
Hypothesis : 
Based on the framework and premises above, the hypothesis is formulated as 
follows: 
H0 : sEMG parameters cannot predicts upper extremity motor function recovery in 
stroke patients 
H1: sEMG parameters can predicts upper extremity motor function recovery in 
stroke patients (premise 1,2,3,4,5)

20 
 
 
MATERIAL AND METHODS 
 
Study design and setting 
This study is an analytic quantitative observational study, to analyse the 
quantification of MMT score, FMA-UE score and electrical signals of the anterior 
deltoid, biceps, triceps, wrist flexors and wrist extensors muscles based on 
contractions, length of contraction and amplitude parameters in patients with acute 
phase stroke. This study performed at Hasan Sadikin Hospital between February 
2024 – July 2024. 
 
Study participants 
Potential participants will be recruited by the main researcher 
Inclusion criteria:  
1. Patients who are hospitalized with upper extremity weakness due to stroke on 
the same side which confirmed with computed tomography (CT) scan or 
structural magnetic resonance imaging (MRI), in the acute phase 
2. Men and women aged more than 18 years old 
3. Willing to participate  
Exclusion criteria: 
1. Cognitive impairment (MoCA-Ina score less than 26) 
2. Impaired consciousness (GCS score less than 15) 
3. Unstable medical conditions at time of hospitalization  
4. Patients with pacemaker  

21 
 
 
 
5. Having other injury or dysfunction in the impaired side of upper extremity that 
caused restrictions on the range of joint movement and muscle weakness, such 
as fractures, periarthritis, or moderate-severe pain 
6. Diagnosis of other neurological disease or disorders in addition to stroke (e.g., 
traumatic brain injury, neuropathy or radiculopathy) 
7. Get treated in isolation room  
8. Hypersensitivity to gel electrodes 
Drop out criteria:  
1. Unable to do 2 or more follow up session  
2. Withdrawn by themselves 
Confounding factor:  
1. Stroke location  
2. Stroke size 
3. Spasticity 
4. Rehabilitation program 
5. Compliance rehabilitation 
6. Stroke risk factor 
Sampling Technique 
 
The method of sampling based on consecutive samplings, that is, all stroke 
patients who meet the inclusion and exclusion criteria during the duration of the 
study.  
      

22 
 
 
 
Study Variable 
 
Independent variables: 
1. Type of stroke 
2. Stroke severity 
3. Recurrent stroke 
 
Dependent variables:  
1. MMT score 
2. FMA-UE score 
3. Electrical activity of deltoid muscle 
4. Electrical activity of biceps muscle 
5. Electrical activity of triceps muscle 
6. Electrical activity of wrist flexors muscle 
7. Electrical activity of wrist extensors muscle 
 
Sample Size Determination 
Samples are taken from research subjects that meet the inclusion criteria. 
The sample size to test the pair averages then the large determination of the sample, 
carried out on the basis of statistical calculations by setting the 95% confidence 
level of the two-way hypothesis and the power test 95%. 
Sample determination for categorical numerical analytical research pairs 
using the formula:  
 

23 
 
 
 
Where: 
Z= alpha standard deviation 
Z= beta standard deviation  
S = combined standard deviation. 
X1-X2 = average minimal difference that is considered significant  
Type I error is determined as 5%, hence two-ways hypothesis Zα = 1,96  
Type 2 error determined as 5%, hence Zβ = 1,64   
Based on the formula, the value is entered into the sample size formula as 
follows: 


2
1
2
1
2
Z
Z
S
n
n
X
X













 


2
1
2
1,96 1,64 *1
1
n
n








 
12,96
13


 
The minimum sample size for the pre-post test group is 13 people. The 
minimum number of samples required is 13 samples. The minimum sample number 
is added to the 10% possibility of sample exclusion so that the minimum sample 
size is 13+1.3 = 14.3 ≈ 15 samples. 
According to Gay and Diehl (1992), the sample must be as large as possible. 
This opinion assumes that the more samples taken, the more representative they 
will be and the results can be generalized. The sample size depends on the type of 
research: 

24 
 
 
 
1. The research is descriptive, so the minimum sample = 10% of the 
population 
2. Correlational research, minimum sample is 30 units 
3. Comparative causal research, the sample is 30 units per group 
4. Experimental research, the minimum sample is 15 units per group 
Based on the opinion of Gay and Diehl (1992), the minimum sample for this 
study for comparative analysis is 30 people. Based on the formula above, the 
minimum sample size for this research is adequate, so the minimum sample size is 
30 samples. 
 
Data Analysis 
Before data analysis is carried out, the data is checked for completeness and 
accuracy, tabulated, coded, and entered into the computer. The next data analysis 
aims to describe the dependent and independent variables so that it can help further 
analysis in more depth. Apart from that, descriptive analysis is also used to 
determine the characteristics of the research subjects, who are the research samples. 
Data analysis to see an overview of the proportions of each variable that 
will be presented descriptively can be broken down into descriptive analysis and 
hypothesis testing. Numerical scale data such as age and others are presented with 
a mean and standard deviation, a maximum value, and a minimum value. The 
sample characteristic data in the form of categorical data is presented in a frequency 
distribution table. Data is recorded in the research form that has been created, then 
editing, verification, coding, and data entry are carried out, and then data analysis 

25 
 
 
 
is carried out. Statistical analysis according to research objectives and research 
hypotheses Data are presented as percentages (%) for categorical variables and 
mean ± standard deviation (SD) for continuous variables. Statistical analysis begins 
with this normality test aims to find out whether the data is normally distributed or 
not. The statistical test used to test the data is normally distributed or not, with 
Shapiro Wilks (because n is less than 50). Numerical variables between pairs of two 
groups is compared using the paired T test if the data is normally distributed and 
the alternative Wilcoxon test. Numerical variables between pairs of more than two 
groups is compared using the repeated ANOVA test if the data is normally 
distributed and the alternative Fredman test. The McNemar and Marginal 
Homogeneity tests are used for paired categorical data. The significance of 
statistical test results is determined based on a p value of <0.05. 
The data obtained was recorded in a special form and then processed using 
the SPSS version 24.0 for Windows program. 
 
 

26 
 
 
 
Ethics consideration  
The study was conducted using human trials, patients with acute stroke 
treated in RSHS. The observation is a non-invasive procedure that is relatively safe 
to administer. The description of research ethics is as follows: 
1. The research will be submitted to the Ethics Committee and obtain 
Ethical Approval. 
2. The research began after obtaining a research permit from the hospital 
3. All subjects were given an explanation of the purpose, benefits, and 
work of the research including the subject's discomfort. 
4. Subjects who are willing to participate in the research are asked to sign 
a letter of consent to participate in the research or informed consent. 
5. All data and information of the subject are kept confidential. 
Dummy Table 
 
Table 2. Dummy Table 
Variable 
In Hospital 
 
Follow Up 
Difference in 
change at 3 
months 
3 days 
Discharge 
P-Value 
1 
Months 
2 
Months 
3 
Months 
P-Value 
MMT score  
 
 
 
 
 
 
 
FMA-UE 
score 
 
 
 
 
 
 
 
 
Contraction 
 
 
 
 
 
 
 
 
Length of 
contraction 
 
 
 
 
 
 
 
 
Amplitude 
 
 
 
 
 
 
 
 
Note: For numerical data, the p value is tested using the paired T test if the data is 
normally distributed and the alternative Wilcoxon test if the data is not normally 
distributed. Then, to compare numerical variables between pairs of more than two 
groups using the repeated ANOVA test if the data is normally distributed and the 
alternative Fredman test, The significance value is based on a p value of <0.05. The 
* sign indicates a p value <0.05, meaning it is significant or statistically significant.  
 
 
 

27 
 
 
 
Table 3. Characteristic Table 
 
 
 
 
 
 
 
 
 
Note: categorical data is presented in number, frequency, and 
percentage, while numerical data is presented in mean, 
median, standard deviation, and range. 
 
 
 
 
Variables 
 
p-Value 
Sex 
   Women  
   Men 
 
 
Age 
 
 
Length of stay 
Stroke diagnosis 
   Ischemic stroke 
   Haemorrhagic stroke 
 
 
Stroke side 
   Right hemisphere 
   Left hemisphere 
Dominant Hand 
   Right 
   Left 
Stroke severity 
  Minor stroke 
  Moderate stroke 
  Moderate to severe stroke 
  Severe stroke 
Stroke risk factor 
  Hypertension 
  Diabetes Mellitus 
  Heart disease 
  Dyslipidemia 
  Smoking 
Recurrent stroke 
  Yes 
   No 
 
 

28 
 
 
Operational Definition 
Table 4. Operational Definition of Research 
 
Research Variables 
Operational Definition 
Measuring Instrument 
Result 
Measurement 
Scale 
Sex 
Sex corresponds which stated  
on the valid ID card 
ID card 
Family card 
Male 
Female 
Nominal 
Age 
The age limit is stated in years 
according to the last birthday 
ID card 
Family card 
Years 
Numerical 
Length of stay 
Clinical metric that measures the 
length of time elapsed between a 
patient's hospital admittance and 
discharge 
Medical record 
Days 
Numerical 
Stroke diagnosis 
Acute episode of focal or global 
neurological dysfunction that 
persists for more than 24 hours 
Medical record and 
imaging result (CT-Scan, 
MRI) 
Stroke Ischemic 
Stroke Hemorrhagic 
Categorical 
Recovery upper motor 
function in stroke 
Improvements of upper 
extremity motor function, occur 
in the first few week post-stroke, 
often reaching a relative plateau 
after 3 months 
FMA-UE 
≤25 : severe 
26-45 : moderate 
46-66 : mild 
Categorical 
Stroke side 
Hemisphere (side) of blood 
cannot flow to in the brain 
Medical record and 
imaging result (CT-Scan, 
MRI) 
Right hemisphere 
Left hemisphere 
Categorical 
Dominant hand 
Individual's preferential use of 
one hand 
Medical record 
Right 
Left 
Nominal 
Stroke severity 
The extent of the impact caused 
by a stroke 
NIHSS score 
1-4 : Minor stroke 
5-15 : Moderate stroke 
Categorical 

29 
 
 
 
15-20 : Moderate to severe 
stroke 
21-42 : Severe stroke 
Stroke risk factor 
The subject's risk factors for 
stroke 
Medical record 
Hypertension 
Diabetes Mellitus 
Heart disease 
Dyslipidemia 
Smoking 
Nominal 
Recurrent stroke 
The subject has had their brain 
deprived of oxygen at least 
twice, increasing the chances of 
possible damage to the brain 
Medical record and 
imaging result (CT-Scan, 
MRI) 
Yes 
No 
Nominal 
MMT score 
Physical exam to testing muscle 
strength. Scoring use a five-
point scale defined as normal, 
good, fair, poor, trace and zero 
Medical record 
0-5 
Categorical 
FMA-UE score 
Stroke-specific assessment tool 
to assess motor functioning, 
balance, sensation and joint 
functioning. Comprises 33 items 
related to the movements of 
proximal and distal upper 
extremity. The maximum motor 
performance score is 66 points 
Medical record 
≤25 : severe 
26-45 : moderate 
46-66 : mild 
Numerical 
Electrical activity 
deltoid muscle 
Electrical activity of the deltoid 
muscle recorded with a sEMG 
machine during muscle 
contraction 
Flexcomp Infiniti sEMG 
 Contraction 
(number/minute) 
 Length of contraction 
(millisecond) 
 Amplitude (mV) 
Numerical 
Electrical activity biceps 
muscle 
Electrical activity of the biceps 
muscle recorded with a sEMG 
Flexcomp Infiniti sEMG 
 Contraction 
(number/minute) 
Numerical 

30 
 
 
 
machine during muscle 
contraction 
 Length of contraction 
(millisecond) 
 Amplitude (mV) 
Electrical activity 
triceps muscle 
Electrical activity of the triceps 
muscle recorded with a sEMG 
machine during muscle 
contraction 
Flexcomp Infiniti sEMG 
 Contraction 
(number/minute) 
 Length of contraction 
(millisecond) 
 Amplitude (mV) 
Numerical 
Electrical activity wrist 
flexors muscle 
Electrical activity of the wrist 
flexors muscles recorded with a 
sEMG machine during muscle 
contraction 
Flexcomp Infiniti sEMG 
 Contraction 
(number/minute) 
 Length of contraction 
(millisecond) 
 Amplitude (mV) 
Numerical 
Electrical activity wrist 
extensors muscle 
Electrical activity of the wrist 
extensors muscles recorded with 
a sEMG machine during muscle 
contraction 
Flexcomp Infiniti sEMG 
 Contraction 
(number/minute) 
 Length of contraction 
(millisecond) 
 Amplitude (mV) 
Numerical 
 
 
 
 
 
 
Outcome Measures 
The investigator assessed the outcome measures. The outcome measures are MMT score, FMA UE score, sEMG parameters 
(contractions, length of contraction, and amplitude). It is measured at 3 days of stroke onset, the day of discharge from hospital, 1 
months, 2 months and 3 months after stroke onset.  

31 
 
 
Research Work Procedure 
This research collaborates with the ITB engineering team and the procedure 
of research is carried out as follows:  
1. Selection of subjects according to the criteria of inclusion and 
exclusion. 
2. Research subjects are given an explanation of the research procedure as 
well as signing informed consent to participate the research. 
3. Research subjects who agree to participate in the study sign the 
informed consent 
4. Data registration of the examination form in the medical record 
5. Examination of vital signs to see contraindications for further 
examination. The subjects used a pulse oximetry during the subsequent 
examination. 
6. Upper extremity function assessment is carried out using MMT and 
FMA UE. 
7. Electrical activity recording of deltoid, biceps, triceps, wrist flexors and 
wrist extensors muscles is performed using sEMG is carried out as 
follows: 
a. The subjects are positioned sitting at 60-90 degrees during 
recording. Put an arm pillow below the impaired hand.  
b. Preparation of the sEMG (Biograph Infinity) machine for 
observation the contraction of muscles. 
c. Clean the skin first using alcohol swab to reduce skin impedance  

32 
 
 
 
d. Ground electrodes are attached to the bone structures in the elbow 
in the impaired side (olecranon) 
e. Electrodes with diameter 50 mm are placed in impaired side at the 
anterior deltoid, long head of the biceps, lateral head of the triceps 
according to the Surface Electromyography for the Noninvasive 
Assessment of Muscles (SENIAM) guidelines standard placement 
procedures (Figure 5). Bony anatomical landmarks of the medial 
and lateral humeral epicondyles were used for standard placement 
of the subsequent electrodes the wrist flexor and extensor muscle 
groups, as SENIAM does not offer placement guidelines for these 
muscles  
 
Figure 5. Electrodes/sensor placement 
f. Each muscle is examined by positioning two electrodes on it, 
separated by a distance of one or two centimeters 
g. The electrodes are fixed when the electrical activity is clearly 
visible on the monitor with minimal noise 

33 
 
 
 
h. Muscle signal data were collected for 30 minutes 
i. During 30 minutes, the subjects do the protocol activity (Figure 6) 
as describe, for subjects with MMT 0, the researcher then 
performed a passive range of motion: 
 
00:01:00 (shoulder flexion)  
 
00:02:00 (rest) 
 
00:03:00 (shoulder flexion) 
 
00:04:00 (rest) 
 
00:05:00 (shoulder flexion)  
 
00:06:00 (rest) 
 
00:07:00 (elbow flexion) 
 
00:08:00 (rest) 
 
00:09:00 (elbow flexion) 
 
00:10:00 (rest) 
 
00:11:00 (elbow flexion) 
 
00:12:00 (rest) 
 
00:13:00 (elbow extension) 
 
00:14:00 (rest) 
 
00:15:00 (elbow extension) 
 
00:16:00 (rest) 
 
00:17:00 (elbow extension) 
 
00:18:00 (rest) 
 
00:19:00 (wrist flexion) 

34 
 
 
 
 
00:20:00 (rest) 
 
00:21:00 (wrist flexion) 
 
00:22:00 (rest) 
 
00:23:00 (wrist flexion) 
 
00:24:00 (rest) 
 
00:25:00 (wrist extension) 
 
00:26:00 (rest) 
 
00:27:00 (wrist extension) 
 
00:28:00 rest 
 
00:29:00 (wrist extension) 
 
00:30:00 (rest) 
 
Figure 6. The five movements tested in the experimental setting. 
Shoulder flexion (a), elbow flexion (b), elbow extension (c), wrist 
flexion (d), wrist extension (e). 
j. The electrodes are released and the scratch area is checked to see 
if there are any signs of irritation 

35 
 
 
 
k. The subjects are informed that the data collection has been 
completed, the sEMG engine cleaned up. 
8. Surface EMG’s signal recording results in digital form were submitted 
to the ITB Engineering team for the processing of the signal. The results 
of the electrical activity features of the muscle are contraction, length 
of contraction and amplitude will be analyzed. 
9. All assessment (MMT, FMA UE and sEMG) is carried out at 3 days of 
stroke onset, the day of discharge from hospital, 1 month, 2 months and 
3 months after stroke onset. 
 
 

36 
 
 
 
Research Flow 
 
Figure 7.  Research flow

37 
 
 
BIBLIOGRAPHY 
1. Fan J, Li X, Yu X, Liu Z, Jiang Y, Fang Y, et al. Global burden, risk factor 
analysis, and prediction study of ischemic stroke, 1990–2030. Neurology. 
2023;101(2):e137-e50. 
2. Venketasubramanian N, Yudiarto FL, Tugasworo D. Stroke burden and stroke 
services in Indonesia. Cerebrovascular Diseases Extra. 2022;12(1):53-7. 
3. Shelton FDN, Reding MJ. Effect of lesion location on upper limb motor 
recovery after stroke. Stroke. 2001;32(1):107-12. 
4. Lawrence ES, Coshall C, Dundas R, Stewart J, Rudd AG, Howard R, et al. 
Estimates of the prevalence of acute stroke impairments and disability in a 
multiethnic population. Stroke. 2001;32(6):1279-84. 
5. Feldner HA, Papazian C, Peters KM, Creutzfeldt CJ, Steele KM. Clinical use 
of surface electromyography to track acute upper extremity muscle recovery 
after stroke: a descriptive case study of a single patient. Applied system 
innovation. 2021;4(2):32. 
6. Steele KM, Papazian C, Feldner HA. Muscle activity after stroke: perspectives 
on deploying surface electromyography in acute care. Frontiers in neurology. 
2020;11:576757. 
7. Alt Murphy M, Al-Shallawi A, Sunnerhagen KS, Pandyan A. Early prediction 
of upper limb functioning after stroke using clinical bedside assessments: a 
prospective longitudinal study. Scientific Reports. 2022;12(1):22053. 
8. Papazian C, Baicoianu NA, Peters KM, Feldner HA, Steele KM. 
Electromyography recordings detect muscle activity before observable 
contractions in acute stroke care. Archives of Rehabilitation Research and 
Clinical Translation. 2021;3(3):100136. 
9. Stinear CM, Smith M-C, Byblow WD. Prediction tools for stroke 
rehabilitation. Stroke. 2019;50(11):3314-22. 
10. Manzur-Valdivia H, Alvarez-Ruf J. Surface electromyography in clinical 
practice. A perspective from a developing country. Frontiers in neurology. 
2020;11:578829. 

38 
 
 
 
11. Ueda S, Aoki H, Yasuda Y, Nishiyama A, Hayashi Y, Honaga K, et al. The 
MMT of Elbow Flexion and the AFE Predict Impairment and Disability at 3 
Weeks in Patients With Acute Stroke. Frontiers in Neurology. 2022;13:831800. 
12. Lv W, Liu K, Zhou P, Huang F, Lu Z. Surface EMG analysis of weakness 
distribution in upper limb muscles post-stroke. Frontiers in Neurology. 
2023;14:1135564. 
13. Raghavan P. Upper limb motor impairment after stroke. Physical Medicine and 
Rehabilitation Clinics. 2015;26(4):599-610. 
14. Li S. Stroke Recovery Is a Journey: Prediction and Potentials of Motor 
Recovery after a Stroke from a Practical Perspective. Life. 2023;13(10):2061. 
15. Grefkes C, Fink GR. Recovery from stroke: current concepts and future 
perspectives. Neurological research and practice. 2020;2(1):1-10. 
16. Pregnolato G, Rimini D, Baldan F, Maistrello L, Salvalaggio S, Celadon N, et 
al. Clinical Features to Predict the Use of a sEMG Wearable Device (REMO®) 
for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study. 
International Journal of Environmental Research and Public Health. 
2023;20(6):5082. 
17. Salvalaggio S, Boccuni L, Turolla A. Patient’s assessment and prediction of 
recovery after stroke: a roadmap for clinicians. Archives of Physiotherapy. 
2023;13(1):13. 
18. Roman NA, Miclaus RS, Nicolau C, Sechel G. Customized manual muscle 
testing for post-stroke upper extremity assessment. Brain Sciences. 
2022;12(4):457. 
19. Brown M, Hislop H, Avers D. Daniels and Worthingham's muscle Testing-E-
Book: Techniques of manual examination and performance testing: Elsevier 
Health Sciences; 2013. 
20. Kostka J, Niwald M, Guligowska A, Kostka T, Miller E. Muscle power, 
contraction velocity and functional performance after stroke. Brain and 
behavior. 2019;9(4):e01243. 

39 
 
 
 
21. Pandian S, Arya KN. Stroke-related motor outcome measures: do they quantify 
the neurophysiological aspects of upper extremity recovery? Journal of 
bodywork and movement therapies. 2014;18(3):412-23. 
22. Massó N, Rey F, Romero D, Gual G, Costa L, Germán A. Surface 
electromyography 
applications 
in 
the 
sport. Apunts 
Med 
Esport. 
2010;45(165):121-30. 
23. Zheng M, Crouch MS, Eggleston MS. Surface electromyography as a natural 
human–machine interface: a review. IEEE Sensors Journal. 2022;22(10):9198-
214. 
24. McManus L, De Vito G, Lowery MM. Analysis and biophysics of surface EMG 
for physiotherapists and kinesiologists: Toward a common language with 
rehabilitation engineers. Frontiers in neurology. 2020;11:576729. 
25. Hogrel J-Y. Clinical applications of surface electromyography 
in 
neuromuscular 
disorders. 
Neurophysiologie 
Clinique/Clinical 
Neurophysiology. 2005;35(2-3):59-71. 
 
 

40 
 
 
Lampiran 1 Formulir Pemeriksaan 
 
 
RUMAH SAKIT UMUM PUSAT Dr. HASAN SADIKIN BANDUNG 
Dr. HASAN SADIKIN GENERAL HOSPITAL BANDUNG 
KOMITE ETIK PENELITIAN KESEHATAN 
HEALTH RESEARCH ETHICS COMMITTEE 
Jl. Pasteur No. 38 Bandung 40161 
 
 
________________________________________________________________________________ 
 
FORMULIR PEMERIKSAAN 
No. Subjek 
 
: 
Tanggal Pemeriksaan : 
Tanggal Onset Stroke : 
Keluhan 
 
: 
 
Data Dasar 
Nama  
 
: 
No. MR 
 
: 
Jenis Kelamin  
: L/P 
Tanggal lahir  
: 
Usia 
 
 
: 
Pendidikan 
 
: 
Faktor Risiko  
:  
 
Skrining Kriteria Eksklusi 
Gangguan kognitif (MOCA-INA) 
 
 
 
 
: 
Gangguan kesadaran (GCS)  
 
 
 
 
: 
Kondisi medis tidak stabil  
 
 
 
 
 
: 
Memakai alat pacu jantung 
 
 
 
 
 
: 
Cedera atau keterbatasan gerak dan kelemahan otot sebelum stroke : 
Perawatan ruang isolasi 
 
 
 
 
 
: 
Hipersensitivitas terhadap elektroda gel 
 
 
 
: 
 
Pemeriksaan Fisik 
Tekanan Darah 
: 
 
 
RR 
 
: 
Nadi 
 
 
: 
 
 
Suhu  
: 
SpO2  
 
: 
 
 
BMI 
 
: 
VAS 
 
 
: 
 
 
NIHSS score :
Lama tinggal RS 
: 
Diagnosa 
 
: 
Stroke ulang  
: Ya/Tidak 
Sisi Lesi Otak  
: 
Sisi Stroke 
 
: 
Tangan dominan 
: 
 

41 
 
 
 
Penilaian dan Pemeriksaan MMT, FMA-UE dan sEMG 
 
 
No 
MMT 
Score 
FMA-
UE 
Score 
sEMG 
Deltoid 
Biceps 
Triceps 
Wrist flexors 
Wrist extensors 
Pemeriksaan 
1 
 
 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
Pemeriksaan 
2 
 
 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
Follow Up 
1 
 
 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
Follow Up 
2 
 
 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
Follow Up 
3 
 
 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
K : 
LK : 
A : 
 
*MMT: manual muscle testing, FMA - UE: Fugl Meyer Assessment – Upper Extremity, sEMG : surface electromyography,  
K: kontraksi, LK: lama kontraksi, A: amplitudo

42 
 
 
Lampiran 2. Informasi/Penjelasan Subjek Penelitian 
 
“Penggunaan Permukaan Elektromiografi sebagai Alat Prediksi Pemulihan 
Fungsi Ekstremitas Atas Setelah Stroke” 
 
Saya adalah mahasiswa yang berasal dari Universitas Padjadjaran yang sedang 
melakukan penelitian untuk disertasi, mengundang Anda untuk berpartisipasi 
dalam penelitian ini, keikutsertaan Anda dalam penelitian ini bersifat sukarela, jadi 
Anda  dapat memutuskan untuk berpartisipasi atau sebaliknya. 
Tujuan Penelitian: 
Untuk mengetahui parameter alat sEMG dapat memprediksi pemulihan fungsi 
motorik anggota gerak atas pada pasien stroke 
Mengapa Subjek terpilih: 
Kriteria Inklusi:  
1. Pasien yang dirawat di rumah sakit dengan kelemahan anggota gerak atas akibat 
stroke fase akut pada sisi yang sama yang dikonfirmasi dengan computerized 
tomography (CT) scan atau magnetic resonance imaging (MRI) 
2. Pria dan wanita berusia diatas 18 tahun 
3. Bersedia berpartisipasi 
Tata Cara/Prosedur: 
Penelitian ini bekerjasama dengan tim teknik ITB dan prosedur penelitian yang 
dilakukan sebagai berikut: 
1. Pemilihan subjek sesuai kriteria inklusi dan eksklusi. 
2. Subyek penelitian diberikan penjelasan mengenai prosedur penelitian serta 
menandatangani informed consent untuk berpartisipasi dalam penelitian. 
3. Subyek penelitian yang bersedia mengikuti penelitian menandatangani informed 
consent 
4. Data registrasi formulir pemeriksaan di rekam medis 
5. Pemeriksaan tanda vital untuk melihat kontraindikasi pemeriksaan lebih lanjut.  
6. Penilaian fungsi anggota gerak atas dilakukan dengan menggunakan 
pemeriksaan MMT dan FMA UE. 
7. Perekaman aktivitas listrik otot utama yaitu deltoid, bisep, trisep, fleksor 
pergelangan tangan, dan ekstensor pergelangan tangan dilakukan dengan 
menggunakan sEMG dengan cara sebagai berikut: 

43 
 
 
 
A. Subyek diposisikan duduk dengan sudut 60-90 derajat selama perekaman. 
Letakkan bantal lengan di bawah tangan yang mengalami gangguan. 
B. Persiapan mesin sEMG (Biograph Infinity) untuk observasi kontraksi otot. 
C. Bersihkan kulit terlebih dahulu menggunakan kapas alkohol  
D. Elektroda ground dipasang pada struktur tulang di siku pada sisi yang 
mengalami gangguan  
E. Elektroda dengan diameter 50 mm ditempatkan pada kelima otot di sisi 
yang terganggu  
 F. Setiap otot diperiksa dengan menempatkan dua elektroda di atasnya, 
dipisahkan dengan jarak satu atau dua sentimeter 
G. Elektroda dipasang ketika aktivitas listrik terlihat jelas di monitor  
H. Data sinyal otot dikumpulkan selama 30 menit. Selama 30 menit, subjek 
melakukan aktivitas protokol seperti yang dijelaskan, untuk subjek dengan 
MMT 0, peneliti kemudian melakukan rentang gerak pasif: 
• 00:01:00 (fleksi bahu) 
• 00:02:00 (istirahat) 
• 00:03:00 (fleksi bahu) 
• 00:04:00 (istirahat) 
• 00:05:00 (fleksi bahu) 
• 00:06:00 (istirahat) 
• 00:07:00 (fleksi siku) 
• 00:08:00 (istirahat) 
• 00:09:00 (fleksi siku) 
• 00:10:00 (istirahat) 
• 00:11:00 (fleksi siku) 
• 00:12:00 (istirahat) 
• 00:13:00 (ekstensi siku) 
• 00:14:00 (istirahat) 
• 00:15:00 (ekstensi siku) 
• 00:16:00 (istirahat) 
• 00:17:00 (ekstensi siku) 

44 
 
 
 
• 00:18:00 (istirahat) 
• 00:19:00 (fleksi pergelangan tangan) 
• 00:20:00 (istirahat) 
• 00:21:00 (fleksi pergelangan tangan) 
• 00:22:00 (istirahat) 
• 00:23:00 (fleksi pergelangan tangan) 
• 00:24:00 (istirahat) 
• 00:25:00 (ekstensi pergelangan tangan) 
• 00:26:00 (istirahat) 
• 00:27:00 (ekstensi pergelangan tangan) 
• 00:28:00 istirahat 
• 00:29:00 (ekstensi pergelangan tangan) 
• 00:30:00 (istirahat)  
J. Elektroda dilepaskan dan area goresan diperiksa untuk melihat apakah ada 
tanda-tanda iritasi 
K. Subyek diberitahu bahwa pengumpulan data telah selesai, mesin sEMG 
dibersihkan. 
8. Hasil rekaman sinyal sEMG dalam bentuk digital diserahkan kepada tim Teknik 
ITB untuk diproses sinyalnya. Hasil gambaran aktivitas listrik otot berupa 
kontraksi, panjang kontraksi dan amplitudo akan dianalisis. 
9. Seluruh penilaian (MMT, FMA UE dan sEMG) dilakukan pada 3 hari setelah 
timbulnya stroke, hari keluar dari rumah sakit, 1 bulan, 2 bulan dan 3 bulan setelah 
timbulnya stroke. 
Risiko dan ketidaknyamanan: 
Tidak ada 
Manfaat (langsung untuk subjek dan umum): 
Manfaat langsung ke subjek dapat memberikan prediksi untuk pemulihan motorik 
anggota gerak atas 
Manfaat untuk masyarakat dapat memberikan pemahaman pada masalah yang 
kompleks terkait stroke, mengembangkan teknik baru, menentukan rencana 
kepulangan pasien, meningkatkan hasil keluaran program rehabilitasi dan sebagai 
informasi kebijakan publik. Hasil penelitian juga diharapkan dapat meningkatkan 
pengetahuan dan menjadi dasar penelitian selanjutnya. 
Prosedur alternatif: 

45 
 
 
 
Tidak ada 
Kerahasiaan data: 
Dokumen/berkas penelitian akan disimpan pada lokasi yang aman dan hanya dapat 
diakses oleh   petugas yang terlibat dalam penelitian  
Perkiraan jumlah subjek yang akan diikut sertakan: 
30 subjek penelitian 
Kesukarelaan: 
Subjek penelitian dijelaskan terkait prosedur dan kesediaan untuk terlibat dalam 
penelitian. Jika subjek bersedia, subjek wajib menandatangani surat persetujuaan 
kesediaan ikut penelitian 
Periode Keikutsertaan Subjek: 
Februari – Agustus 2024 
Subjek dapat dikeluarkan/mengundurkan diri dari penelitian: 
Subjek akan dikeluarkan jika tidak datang follow up 2x atau lebih. 
Subjek dapat mengundurkan diri dari penelitian sewaktu-waktu (kapanpun) tanpa 
konsekuensi apapun 
Kemungkinan timbulnya pembiayaan dari perusahaan asuransi kesehatan 
atau peneliti: 
Tidak ada 
Insentif dan kompensasi: 
Adanya kompensasi dan insentif akan diberikan kepada subjek penelitian pada 
akhir penelitian berupa uang transport senilai Rp 200.000,- dan bingkisan senilai 
Rp 150.000,-  
Pertanyaan: 
Contact person : Berthy : 082257297452 
  
 
 

46 
 
 
 
Lampiran 3. Surat Pernyataan Persetujuan 
 
 
RUMAH SAKIT UMUM PUSAT Dr. HASAN SADIKIN BANDUNG 
Dr. HASAN SADIKIN GENERAL HOSPITAL BANDUNG 
KOMITE ETIK PENELITIAN KESEHATAN 
HEALTH RESEARCH ETHICS COMMITTEE 
Jl. Pasteur No. 38 Bandung 40161 
 
________________________________________________________________________________ 
 
PERSETUJUAN SETELAH PENJELASAN (PSP) 
UNTUK IKUT SERTA DALAM PENELITIAN  
(INFORMED CONSENT) 
 
Saya telah membaca atau memperoleh penjelasan, sepenuhnya menyadari, mengerti, dan 
memahami tentang tujuan, manfaat, dan risiko yang mungkin timbul dalam penelitian, serta 
telah diberi kesempatan untuk bertanya dan telah dijawab dengan memuaskan, juga  
sewaktu-waktu dapat   mengundurkan diri dari keikut sertaannya, maka saya setuju/tidak 
setuju*) ikut dalam penelitian ini,  yang berjudul: 
 
 
Saya dengan sukarela memilih untuk ikut serta dalam penelitian ini tanpa tekanan/paksaan 
siapapun. Saya akan diberikan salinan lembar penjelasan dan formulir persetujuan yang 
telah saya tandatangani untuk arsip saya. 
Saya setuju:        
Ya/Tidak*)     
 
 
Tgl.:  
 
Tanda tangan (bila tidak 
bisa dapat digunakan cap 
jempol) 
Nama Peserta:  
Usia: 
Alamat: 
 
 
Nama Peneliti: 
  
 
 
Nama Saksi: 
 
 
 
 
*) coret yang tidak perlu 
 
 
 
 
 

47 
 
 
 
Lampiran 4. Fugl Meyer Assessment Upper Extremity 
 
 
 

48 
 
 
 
 
 
 

49 
 
 
 
 
 
 
 
 

50 
 
 
 
Lampiran 5. National Institutes of Health Stroke Scale (NIHSS) 
 
 
 
 

51 
 
 
 
Lampiran 6. Montreal Cognitive Assessment (MoCA-Ina) 
 
 

52 
 
 
 
Lampiran 7. Glasgow Coma Scale