The effects of robot-assisted gait training using virtual reality and auditory stimulation on balance and gait abilities in persons with stroke

Abstract

BACKGROUND:

Robot-assisted gait training provide a big therapeutic advantage in functional mobility for postural control.

OBJECTIVES:

The purpose of this study was investigate the effects of robot-assisted gait training using virtual reality and auditory stimulation on balance and gait abilities in stroke patients.

METHODS:

All subjects were randomly divided into three groups where twelve subjects were in the Virtual reality robot-assisted gait training group (VRGT), twelve subjects in the auditory stimulation robot-assisted gait training group (ARGT), and sixteen subjects in the control group. Subjects received virtual reality and auditory stimulation while undergoing robot-assisted gait training for 45 minutes, three times a week for 6 weeks, and all subjects had undergone general physical therapy for 30 minutes, five times a week for 6 weeks. All subjects were assessed with the Medical Research Council (MRC), Berg balance scale (BBS), timed up and go test (TUG), 10-meter walk test (10MWT), Fugl-Myer Assessment (FMA) and Modified Barthel Index (MBI) pre- and post-intervention.

RESULTS:

Results showed that BBS, TUG, and 10MWT scores significantly improved post-intervention (p < 0.05), and the control group also had significantly improved in all areas post-treatment (p < 0.05). In addition, it has been confirmed that VRGT had significantly improved in MRC and FMA scores compared with the auditory stimulation. Also, it has significantly improved in MRC, BBS, TUG, 10MWT and FMA compared with control group (p < 0.05).

CONCLUSIONS:

The results of this study showed improve balance and gait abilities after VRGT compared with general physical therapy and were found to be effective in enhancing the functional activity of persons with stroke.

Keywords

Stroke rehabilitation gait balance muscular strength

1 Introduction

Stroke generally reduces balance due to muscle weakness and asymmetric muscle tension, and postural sway in standing posture is twice as large as normal (Perry, 1992; Nicholas, 1997). The decrease in the ability to move the weight due to such postural sway is an important factor that interferes with standing or walking (Carr, Shepherd, Nordholm, & Lynne, 1985). As a result, 75% of patients with stroke represent gait disorders (Chandramouli et al., 2013). Unlike normal gait, which is defined as the movement of harmonious limbs with minimal energy, it is difficult to perform normal gait in patients with stroke, so various compensating movements are used to perform functional gait. These compensatory mechanisms exhibit abnormal gait form, which causes higher energy consumption and loss of efficiency compared to normal mechanisms (Perry, 1992). Particularly in patients with stroke, slow gait cycle and gait speed, a difference in stride between the affected and unaffected side steps, a short stance phase on the affected side, and a relatively long swing phase are present, and this limits functional mobility (Anat et al., 2010). Therefore, a proper exercise method is needed for restoring balance ability and improving gait ability of stroke patients.

Recently, the robot-rehabilitation in the field has become more sophisticated, specialized and evolving with the development of industrialization and science and technology (Esquenazi et al., 2012). This robot-assisted gait training is advantageous in that it can perform safer gait training by repetitive task training with high intensity (Mehrholz et al., 2007). Robot-assisted gait training, which requires repetitive tasks, can increase the neuro-plasticity and motor learning that focus on reorganization of brain tissue, resulting in better balance and faster walking speed (Kwakkel et al., 2004). A study by Mayer et al. (2007) focusing on improving walking ability of stroke patients reported that there was significant effect in 10-meter walking ability test and the 6-minute walking test. These results were attributed to increase affected side stance phase and improve stability. As a basis for supplementing this, Celine et al. (2014) and Yang et al. (2014) mentioned the effect of walking speed increase by changing the torque such as hip extension and knee flexion according to the application of robot-assisted gait training, and symmetrical gait pattern results were derived.

In order to apply these robot-assisted gait training more efficiently, it is necessary to encourage voluntary participation of the patients and active efforts (Schuck et al., 2012). Robot-assisted gait training using virtual reality has been continuously studied as a way to bring about such active movement (Lum et al., 2002). This is because patients are motivated to improve their concentration and to engage in active participation (McBer & Co, 1985). Virtual Reality has been developed as a tool for evaluation and treatment of rehabilitation in the late 20th century, and it has been developed as a stage to acquire various skills through interacting and feedback based on realistic user experience (Reynolds & Day, 2005; Rizzo et al., 2005). Augmented feedback, a virtual reality program, provides a variety of bio-feedback, or force-feedback, applied to rehabilitation robots in the form of interactive simulations (Rizzo & Buckwalt, 1997; Lee et al., 2013). Looking at robot-assisted gait training applied based on virtual reality on stroke patients, in the study of Karin et al. (2011), the results showed that the participation rate of robot-assisted gait training using virtual reality program was higher than those of the gait training, and suggested the direction of efficient training of rehabilitation robot. After that, studies by Lee et al. (2013) and Ham et al. (2016) on robot-assisted gait training applied based on virtual reality reported significant effects on functional performance according to the positive change of balance ability and walking ability. As with this virtual reality program, there is another method that can lead to the active movement of the patient by using auditory stimulation (Molinari et al., 2003). This auditory stimulation training provides an auditory signal that immediately leads to the synchronization of neurological movements, and it has been reported that by structuring the movement pattern in time, it can perform efficient re-education and functional motor learning, thereby improving the spatio-temporal factors of movement, leading to more normal and rhythmical movement pattern (Whitall et al., 2000; Thaut, 2008). This mechanisms can be seen through the study of Jeong et al. (2007) showed that gait training using regular auditory stimulation improves the angle and flexibility of the joints in stroke patients. Roeldink et al. (2007) reported that the positive change was due to the improvement of gait ability with symmetrical spatio-temporal change as well as the improvement of joint angle and flexibility of stroke patients using auditory stimulation. Thus, training using auditory stimuli is also widely used and studied as a method of functional recovery of stroke patients (Pelton et al., 2010; Goldshtrom, Knorr, & Goldshtrom, 2010). However, there is insufficient research on robot-assisted gait training based on the progressive intensity and difficulty of virtual reality and auditory stimulation, and there is a lack of comparative studies on robot-assisted gait training using virtual reality and auditory stimulation, which makes it difficult to prove. Therefore, the purpose of this study compared the effects of robot-assisted gait training using virtual reality and auditory stimulation with progressive intensity and difficulty on the balance ability and gait ability of stroke patients, and is to evaluate the usefulness of robot-assisted gait training as an efficient rehabilitation exercise method after stroke.

2 Methods

2.1 Participants

The subjects of this study were selected as 40 patients who were admitted to Chung-Nam national university hospital in Daejeon. In addition, the subjects were not experienced in robot-assisted gait training, and the stroke patients were composed of patients who had no overlapping diseases within the past 6 months after the onset of stroke to minimize the possibility of natural restoration. To minimize the selection bias, the following selection criteria were applied randomly to the three groups. The inclusion criteria were as follows: (1) diagnosis of stroke (after minimum 6months); (2) ability to walk 10 minutes with or without an assistive device (Lee et al., 2013); (3) impairment of balance ability (maximum berg balance scale score 45) (Berg, Wood-Dauphinee, & Williams, 1995); (4) cognitive abilities enabling communication (minimum MMSE score 24) (Park & Kwon, 1989); (5) medically stable and free of major cardiovascular or other medical conditions; (6) no history of orthopedic surgery within the past 6 months and seizure. The general characteristics of the participants are shown in Table 1.

Table 1
Clinical characteristics of the participants in this study (N = 40)

VRGT (n = 12) ARGT (n = 12) Control (n = 16) F p

Age (yr) 55.58±10.42 56.66±4.39 57.50±9.90 0.851 NS

Height (cm) 167.41±6.84 166.58±5.40 165.18±7.83 0.689 NS

Weight (kg) 67.87±5.16 66.87±7.05 67.18±8.99 0.945 NS

Delay (months) 7.33±1.15 7.33±1.72 7.75±1.77 0.723 NS

MMSE-k 28.33±1.07 28.16±1.74 28.37±0.95 0.907 NS

Sex

male 7 9 9 0.581 NS

female 5 3 7

Hemiplegic side

left 9 5 8 0.241 NS

right 3 7 8

Mechanism

Haemorrhage 3 8 9 0.105 NS

Ischaemia 9 4 7

	VRGT (n = 12)	ARGT (n = 12)	Control (n = 16)	F	p
Age (yr)	55.58±10.42	56.66±4.39	57.50±9.90	0.851	NS
Height (cm)	167.41±6.84	166.58±5.40	165.18±7.83	0.689	NS
Weight (kg)	67.87±5.16	66.87±7.05	67.18±8.99	0.945	NS
Delay (months)	7.33±1.15	7.33±1.72	7.75±1.77	0.723	NS
MMSE-k	28.33±1.07	28.16±1.74	28.37±0.95	0.907	NS
Sex
male	7	9	9	0.581	NS
female	5	3	7
Hemiplegic side
left	9	5	8	0.241	NS
right	3	7	8
Mechanism
Haemorrhage	3	8	9	0.105	NS
Ischaemia	9	4	7

VRGT, Virtual reality Robot assisted Gait Training; ARGT, Auditory stimulation Robot-assisted Gait Training.

2.2 Protocol

This study included a pretest-posttest control group design where the subjects were divided according into intervention methods, such as the Virtual reality Robot-assisted Gait Training group (VRGT), Auditory stimulation Robot-assisted Gait Training group (ARGT), or control group. All groups measured balance and gait abilities before intervention. A single blinded method was used to train and evaluate different physiotherapists who had experience of five years or more in each of the trainers and evaluators as a way to increase the reliability of the evaluation by minimizing the measurement error. This study was approved by the Institutional Review Board of Sahmyook University (2-1040781-AB-N-01-2016113HR), and all participants signed informed consent forms after receiving a detailed explanation of the study.

2.3 Intervention

2.3.1 VRGT (Virtual Reality Robot-assisted Gait Training)

The experimental group was a group performing gait training on the treadmill using robotic device (Lokomat Pro, Hocoma AG, Zurich, Switzerland). The rehabilitation robot applied to the experiment is similar to the human skeletal structure, and the exo-skeletal type robot that can effectively simulate normal gait pattern was applied (Mehrholz et al., 2012; Kim & Chang, 2013). The control of the angle and force of the hip joints and the knee joints of the robot is controlled by sensors and main computer embedded in each joint, and the intensity of movement is controlled by walking speed, BWS (Body weight support) and guidance force. During the robot-assisted gait training, the walking speed intervention was applied at a speed of 1.5∼2.5 km/h, initial BWS was applied at 30%, during the gait training, the angles of the hip and knee were applied differently according to the individual leg length, and 100% guidance force (This means the ratio to the torque value of the hip joint and knee joint, and can be adjusted from 0 to 100% on one or both legs) was applied (Britta et al., 2007). In order to control the movement of progressive intensity, gait training was applied by increasing the rate of 5% of the initial applied speed in the 3rd and 5th weeks, and the BWS was also decreased by 5%. In the case of the guiding force, the intensity of the exercise was controlled by applying the robot-assisted gait training with the intensity decreased by 10%. The intervention periods for the subjects were 6 weeks, 3 times a week, for 45 minutes each. VRGT used a virtual reality program called Augmented feedback, which is software embedded in the walking robot. In this virtual reality program, the degree of mutual force between the patient and the robot is represented by the movement of the avatar in the screen through the sensor response. This program can lead to the patient’s reaction and motivation through the number and time of capturing animals by performing the task of catching animals while avoiding obstacles in the background of the forest of virtual reality (Lee et al., 2013; Ham et al., 2016).

2.3.2 ARGT (Auditory Stimulation Robot-assisted Gait Training)

ARGT is a method of adjusting the walking speed to the regular rhythm of the metronome producing the auditory signal. It means adjusting the walking speed by setting the tempo of the metronome according to initial patient’s cadence. The regular rhythm of metronome is a method to train the patient to set the auditory signal at the initial contact, and to walk according to the signal. ARGT also performed gait training using the rehabilitation robot used in VRGT. In order to control the movement of progressive intensity, the rhythm of the metronome was adjusted at the speed increased by 5% walking speed was intervened (Thaut et al., 2007).

2.3.3 Control group (General gait training)

The control group performed general gait training using a treadmill. The training was conducted for 45minutes three times a week for 6 weeks in same manner as the experimental group. All patients who participated in the study were conducted conventional physical therapy for 6 weeks, 5 times a week.

2.4 Outcome measures

2.4.1 Medical Research Council (MRC)

MRC is the clinical method for evaluating muscle strength as a sequence scale. MRC assesses the strength of the agonist and antagonist muscles of a variety of persons with neurological disease, including stroke. The MRC is divided into six grades: Normal (5), Good (4), Fair (3), Poor (2), Trace (1), Zero (0). Hip flexion, extension, abduction, knee flexion, extension, ankle dorsiflexion, plantar flexion of affected side lower extremity was assessed and was calculated as 0 points out of a total of 30 points. The mean value was taken from three measurements, and to minimize the degree of fatigue, there was a 30-second rest period between measurements (Gregson et al., 2000).

2.4.2 Berg Balance Scale (BBS)

The BBS is composed of 14 different items that can quantitatively evaluate the degree of balance and fall risk through direction observation. Items can be classified into three regions of sitting, standing, and postural changes, and each of the 14 items can be scored between 0–4 points, with 56 points being the maximum score. A score of 45 or less indicates the need for the use of a cane or other gait assistive devices, a score of 41–44 indicates a low fall-risk, 21–40 indicates a higher fall risk, and 0–20 indicates a very high risk for falls and injuries. The evaluation assessed dynamic and static balance ability and takes approximately 15 minutes to complete. A therapist with more than 3 years of clinical experience had performed the measurements prior to and after the intervention (Berg, Wood-Dauphinee & Williams, 1995).

2.4.3 Timed Up and Go Test (TUG)

The TUG measures the time it takes for a subject to rise from a seated position at the “start” signal, walk up to the 3 m mark, and then return back to the chair until they are completely seated. It has a reliability of r = 0.99 and a high inter-rater reliability of r = 0.98. The mean value was obtained from a total of three measurements (Podsiadlo & Richardson, 1991).

2.4.4 10 Meter Walk Test (10MWT)

With the presence of gait disabilities due to neurological damage, the 10MWT is a standard test used to investigate the extent of gait ability. We measured the time required to travel the 10 m interval except for the 15 m interval at the starting point and the arrive point while walking the 13 m interval. 10M Walking time measured the walking speed which I walked at the maximum speed and I measured 2 times and selected the highest one (Roth et al., 1997; Pohl et al., 2002).

2.4.5 Fugl-Myer Assessment (FMA)

FMA is a method of assessing motor impairment with a total score of 100 points for motor function, 66 points for upper limbs, and 34 points for lower limbs, giving a score of 0 ∼ 2, where a higher score indicates better exercise control. In this study, a total of 34 points were used for evaluation of FMA lower libs during exercise performance evaluation, and the intra-rater reliability was between 0.995 and 0.996, and the inter-rater reliability was high between 0.98 and 0.995 (Fugl-Meyer, Jääskö, Leyman, Olsson, & Steglind, 1975).

2.4.6 Modified Barthel Index (MBI)

MBI was developed by Mahoney and Barthel (1965) with the self-reliance of everyday life as an evaluation criterion, and it is an evaluation method that immediately reflects changes in the patient’s functional improvement. It is a valuable, reliable and change-sensitive evaluation method that indicates the level of competence in performing daily living activities. In daily life activities, the degree of dependency, i.e., the score of each step, is given through direct observation and interview, and with a score of 100, there are five evaluations where 0 to 24 points are completely dependent, 25 to 49 points are maximum dependency, 50 to 74 points are partial dependency, 75 to 90 points are slightly dependent, 91 to 99 points are minimum dependency, and 100 points is completely independent. The test-retest reliability is known as r = 0.89, and inter-rater reliability is known as r = 0.95. In the present study, MBI was used to assess daily activities and the total score of the evaluation points recorded before and after the experiment by an occupational therapist who had experience of nervous system occupational therapy for 3 years or more was used (Granger, Albrecht, & Hamilton, 1979).

2.5 Data analysis

This study used the PASW Statistics ver. 19.0 program (IBM Co., Armonk, NY, USA). A normality analysis was performed on the general characteristics of subjects and a paired t-test was performed to examine for changes pre and post-intervention for each group. A one-way ANOVA was used to determine for statistically significant differences in balance and walking ability between groups after six weeks, a post-hoc analysis was performed with the Duncan method, and the significance level was set at p < 0.05.

3 Results

3.1 General characteristics and medical characteristics of participants

The general characteristics and medical characteristics of all subjects in the VRGT, ARGT, and control were all homogenous (Table 1).

3.2 Changes in muscular strength and balance ability

The MRC, BBS, TUG, 10MWT and FMA significantly increased in the VRGT and ARGT (p < 0.05), and control group were significantly increased post intervention (p < 0.05). The MRC and FMA of the outcome measures showed a greater significant increase in VRGT compared to ARGT (except for MBI). However, there was a significant difference in the results when comparing VRGT and ARGT with the control group (p < 0.05) (Table 2 and Fig. 1).

Fig.1

Enrollment of stroke patients.

Table 2

Changes in balance ability and gait of the participants in this study (N = 40)

	VRGT (n = 12)		ARGT (n = 12)		Control (n = 16)		F	P
Pre-test	Post-test	Post-test	Post-test	Pre-test	Post-test
MRC (score)	15.83±0.68	20.33±1.02	15.08±0.90	18.50±0.67	15.81±0.75	18.12±0.71	28.629	< 0.001
Difference (post-pre)	4.50±0.76^*^†	3.41±0.90^*	2.31±0.60
t	–19.541	–13.146	–15.363
p	< 0.001	< 0.001	< 0.001
BBS (score)	40.50±1.65	50.16±1.72	40.41±5.21	48.16±1.80	40.62±1.99	45.75±1.91	10.474	< 0.001
Difference (post-pre)	9.66±1.97^*	7.75±4.00^*	5.12±1.50
t	–16.258	–6.707	–13.667
p	< 0.001	< 0.001	< 0.001
TUG (sec)	27.25±2.31	14.50±2.81	27.58±5.71	17.50±4.44	26.06±3.90	19.43±2.78	8.181	0.001
Difference (post-pre)	–12.75±3.19^*	–10.08±5.17^*	–6.62±3.44
t	13.249	6.746	7.698
p	< 0.001	< 0.001	< 0.001
10MWT (sec)	29.16±2.54	17.08±1.25	29.25±5.70	18.91±4.79	28.18±3.18	20.37±2.27	5.102	0.011
Difference (post-pre)	–12.08±2.89^*	–10.33±4.83	–7.81±2.71
t	13.819	7.410	11.517
p	< 0.001	< 0.001	< 0.001
FMA (score)	23.66±3.24	30.16±1.57	23.50±2.84	27.16±1.99	23.00±2.52	26.25±2.29	5.084	0.011
Difference (post-pre)	6.50±3.64^*^†	3.66±3.28	3.25±1.00
t	–5.922	–3.867	–13.000
p	< 0.001	0.003	< 0.001
MBI (score)	59.33±7.63	67.33±6.69	53.50±4.98	61.41±7.98	54.37±3.87	62.50±4.22	0.006	0.994
Difference (post-pre)	8.00±0.93	7.91±3.00	8.12±2.12
t	–3.778	–5.588	–15.292
p	0.003	< 0.001	< 0.001

MRC, Medical Research Council; BBS, Berg Balance Scale; TUG, Timed Up and Go; 10MWT, 10 meter walking test; FMA, Fugl-Myer Assessment; MBI, Modified Barthel Index. †: Significant difference between VRGT and ARGT (P < 0.05). *: Significant difference between VRGT and control, ARGT and control (P < 0.05).

4 Disscussion

The weakness of lower extremity muscles in stroke patients is one of the limiting factors for functional restoration, and it causes asymmetrical posture and physical imbalance, which greatly affects the balance ability. It also causes difficulty in walking ability by reducing functional low extremity movement (Canning & Sanchez, 2004; Yang et al., 2014). Anat et al. (2010) and Celine et al. (2014) suggested that robot-assisted gait training is a very effective method for restoring asymmetric walking ability due to muscle weakness. In addition, Dias et al. (2007) and Wong et al. (2012) stated that robot-assisted gait training gave a functional improvement to stroke patients with an effective change in balance ability. The results of this study also showed the improvement of balance ability and gait ability through robot-assisted gait training compared with general gait training (p < 0.05). This suggests that recovery of balance ability through improvement of leg strength may lead to changes in symmetrical gait, which may have affected gait ability and functional activities (Yang et al., 2008; Celine et al., 2014; Yang et al., 2014).

It is reported that the robot-assisted gait training using virtual reality program plays an important role in enhancing the active movement and participation of the patient by using the virtual reality program as a way of carrying out and interacting with the task through his or her representation on screen (Rizzo et al., 2005). In this study, a virtual reality program called ‘Augmented feedback’ provided a realistic environment, helping patients to participate enthusiastically and actively. In the study of Deutsch et al. (2004), active movement was induced by applying virtual reality and ankle rehabilitation system using robots, resulting in improvement of walking speed and muscle endurance. After that, Lee et al. (2013) and Ham et al. (2016) also demonstrated that robot-assisted gait training using virtual reality was very effective in restoring balance and gait ability of stroke patients. In this study, similar results were obtained as in previous studies. This result suggests that the robot-assisted gait training using virtual reality maximizes the active movement and gives a positive change of the balance ability and walking speed based on the improvement of the lower extremity strength (Anat et al., 2010; Karin et al., 2011).

Regularly auditory stimuli also provide intrinsic feedback on the purpose of the patient’s movements to improve the pattern of muscle activation and the internal time control of muscle activity (Thaut et al., 1997). This periodicity of auditory stimulation regularly regulates motor neuron activity leading to neuronal activation pattern, leading to consistent time control and more motor neuron mobilization (del Olmo et al., 2006; Thaut et al., 1997). Jeong et al. (2007) and Roerdink et al. (2007) reported that these regular auditory stimuli improved symmetrical gait ability and walking speed with improved coordination ability as well as improved joint angle and flexibility. It was reported that the gait training moderating the strength and difficulty of the exercise by using the progressive change of the speed of auditory stimulation positively influenced gait ability such as walking speed and stride length. This suggests that progressive changes in the stimulation rate may have influenced the recovery of balance ability as well as muscle strength by inducing active movement (Thaut et al., 2007). In addition, it is believed that repetitive tasks such as virtual reality and auditory stimuli stimulate long-term structural changes by promoting remapping of the sensory and motor cortex in functional changes immediately following brain injury, and by promoting neuro-plasticity, activation of the cerebral cortex and central nervous system areas leads to a positive effect on walking speed and balance ability by exercise re-learning in limbs (Johansson, 2000; Jang et al., 2003; Harvey, 2009).

Walking in patients with stroke is a type of asymmetric gait that has a very low walking speed and this change in gait has a great effect on functional activity (Kim et al., 2003). Celine et al. (2003) found that asymmetric hip and knee movements changed to symmetrical gait patterns after robot-assisted gait training, and recently, Yang et al. (2013) also showed symmetrical gait changes based on effective body weight shifting through changes in foot pressure after robot-assisted gait training. In the study of Anat et al. (2010), the positive effect of walking speed was derived on the basis of improvement of walking ability. Based on these evidences, the results of this study led to a positive change of FMA, which is an index of walking ability and functional performance ability (p < 0.05). This suggests the improvement of the overall function of the lower extremity, as well as muscle strength, and robot-assisted gait training can be said to have a very positive effect on the improvement of motor function as well as on balance and gait ability. However, ARGT did not show any significant change in walking ability compared to the control group performing general gait training. Thus, it is difficult to say that ARGT was effective on gait ability compared with general walking training. In addition, VRGT showed a significant difference between MRC and FMA compared to ARGT (p < 0.05). It can be said that robot-assisted gait training using virtual reality was more effective as a functional recovery method based on improvement of muscle strength by bringing active movement and participation of patients compared to auditory stimulation (Horlings et al., 2009). In addition, because visual task performance has more influence on posture stability than non-visual task performance, and since the visual signal is preferentially used to adjust the movement pattern for movement in the real environment, VRGT based on visual feedback is presumed to be more effective in muscle performance than ARGT (Palta et al., 2006 & Mendelson et al., 2009). However, robot-assisted gait training led to improvement of strength and balance ability and walking ability, but it was not enough to affect MBI result which is an indicator of daily life (p > 0.05). It is considered that the results of the robot-assisted gait training did not yield the expected results due to the longer training period required in order to change the activities of daily living.

There are some limitations in interpreting the results of this study. First, there is a limit to generalizing the results of the study because it does not involve a large number of subjects. Second, there is a limit to represent various characterized stroke patients with balance and gait abilities. Third, the study period is set at six weeks, and there is a limit to reflect the difference in effectiveness with short-term training. In the future, it is desired to complement the above limitations and make progress in the field of rehabilitation robots through various aspects of robot-assisted gait training for stroke patients.

Conflict of interest

The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Footnotes

Acknowledgments

This paper was supported by the Fund of the Sahmyook University in 2017.

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