The Use of Immersive Virtual Reality During Robot-assisted Walking in Healthy Adults - Effect of Changing the Optic Flow Speed in Two Different Virtual Environments on Active Participation During Robot-assisted Walking.
Phase: NA
Conditions: Healthy
Sponsor: Vrije Universiteit Brussel

Corresponding author: emma.de.keersmaecker@vub.be  
 
 
The use of immersive virtual reality during 
robot-assisted walking in healthy adults: a study 
protocol 
Effect of changing the optic flow speed in two different virtual environments 
on active participation during robot-assisted walking. 
 
Synopsis 
The aim of this study will be to investigate the effect of changing the optic flow 
speed on active participation during robot-assisted walking with the Lokomat-
system in healthy adults. Second, the aim is to investigate the effect of two 
different virtual environments on active participation. The Lokomat is a gait 
exoskeleton that comprises two actuated leg orthoses and is used in conjunction 
with a body-weight support and treadmill. 28 participants, divided equally into 
2 groups, will be tested in the rehabilitation center TrainM (Antwerp). 
Participants will perform one virtual-reality (VR) enhanced robot-assisted gait 
training (RAGT) session under three conditions for 7 minutes each: 1) walking 
with slower optic flow speed, 2) walking with matched optic flow speed, and 3) 
walking with fast optic flow speed. The virtual environment will be different for 
both groups: the experimental group will walk in a more interactive virtual 
environment such as a park or city street whereas the control group will walk in 
a virtual environment representing an endless hallway. Active participation will 
be assessed with the use of electromyography (EMG) and the position and force 
sensors of the Lokomat. 
Key words: Robot-Assisted Gait Training, Lokomat, Virtual Reality, Optic Flow, 
Stroke  
 
 
 
 
 
 
 
Prof. Dr. Eric Kerckhofs, Brussel 7/12/2017 

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1. Introduction 
1.1 Rationale 
The recovery of functional gait is of great importance for neurological patients and is a common 
goal of their rehabilitation programs [1]. Treadmill training with or without body weight support 
(BWS) is often used in clinics as a therapeutic approach for improving patients’ gait function [2]. A 
more recent strategy to enhance gait training is robot-assisted gait training (RAGT), combining 
robotics with BWS treadmill training [1]. Both treadmill training and RAGT have demonstrated 
improvements in regaining the ability to walk in different neurological populations [3-6]. However, 
an important problem of using a treadmill for gait training is the incorrect visual information 
subjects receive during treadmill waking [2].  
Controlling our locomotion involves the integration of visual, vestibular and proprioceptive 
information [7]. An important source of visual information to guide locomotion is optic flow (OF), 
a pattern of visual motion projected onto the retina of the eye [2, 8]. Optic flow, induced by 
locomotion, provides us with information about the direction and speed of self-motion [2, 7-9]. 
However, during treadmill walking, the visual information about the direction and speed of walking 
is inconsistent with the proprioceptive input of the lower limbs [2]. Subjects are walking on the 
treadmill and yet, their environment remains static.  
It has already been shown that manipulating the OF can induce changes in humans’ locomotion. 
Studies in healthy volunteers and neurological patients have shown that walking speed can be 
altered by changing the OF speed while walking on a treadmill [7, 9-13]. A negative correlation 
between walking speed and OF speed has been found, indicating that subjects will walk faster at a 
slower optic flow speed and vice versa. By manipulating the OF speed, a mismatch will arise 
between the OF and the proprioceptive information of the lower extremities and gait parameters 
will be adjusted [2, 7].  
This manipulation of the OF can be done with the use of virtual reality (VR) where patients walk 
in a virtual environment (VE). VR devices and systems can be classified into two categories: (1) 
Semi-immersive or non-immersive VR systems, who let the user perceive both real world and a part 
of the VE (e.g. TV-screens, projection screens), and (2) Immersive VR systems, who fully integrate 
the user into the VE, by blocking out perception of the real world (e.g. head-mounted displays 
(HMD)) [14].  
Although various studies have already investigated the effects of manipulating the OF on gait 
parameters, all these studies used either overground training or treadmill training to obtain 
locomotion. None of these studies assessed the effect of OF speed alterations during RAGT. Even 
more, the majority of these studies used non-immersive VR systems while this study will provide 
subjects with an immersive VE with the use of a HMD. Since this will be the first study investigating 
the effect of manipulating the OF during RAGT by means of an exoskeleton, a healthy population 
will first be tested before performing this on neurological patients. Furthermore, this study is also 
going to investigate if the type of VE has an influence on the active participation. 

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1.2 Objective 
The aim of this study is to investigate the effect of different OF speeds on active participation in 
healthy participants during VR-enhanced RAGT by means of an exoskeleton. 
The second aim is to investigate the effect of two different VEs on active participation during RAGT.  
2. Methods 
2.1 Study design 
An experimental, 2-group, single-center trial will be conducted in which healthy participants will 
perform one VR-enhanced RAGT session in which 3 different optic flow (OF) speeds will be 
presented in a random order: 1) walking with slow OF speed, 2) walking with matched OF speed, 
and 3) walking with fast OF speed. The VE will be different for both groups. The experimental 
group will walk in a more interactive VE, whereas the VE of the control group will be a more static 
environment. 
2.2 Participants 
2.2.1 Number 
Based on a sample size calculation (G*Power 3.0.10) (F-tests, repeated measures ANOVA, within-
between subjects) with Cohen’s f of 0.25 (moderate effect size), type 1 error probability of 0.05, 
power of 0.80 for 2 groups and 3 conditions, a total of 28 participants, divided equally in 2 groups, 
need to be recruited. In case subjects drop out, additional participants will be tested till 14 
participants in each group completed all conditions. Participants will be tested in the rehabilitation 
center TrainM in Antwerp. 
2.2.2 Eligibility criteria 
Inclusion criteria will be: 1) ≥ 55 years of age, 2) normal or corrected-to-normal vision with glasses 
or contact lenses, 3) no locomotion impairments, 4) a maximal body weight of 135 kg, and 5) a 
maximal body height of 200 cm. Limitations regarding body weight and body length are due to the 
design of the Lokomat gait exoskeleton.  
Participants will be excluded if they: 1) have skin lesions that cannot be protected appropriately, 2) 
have had significant lower extremity injuries during the last two years that might affect their gait, 
and 3) have any type of vestibular/visual deficiency.    
2.3 Instrumentation  
2.3.1 Virtual reality 
The virtual reality will be provided by a HMD VR system ‘Oculus Rift’ (Oculus rift, LLC, US) and 
will assure an immersive virtual environment. The experimental group will walk in a more 
interactive VE such as a park or city streets, while the VE of the control group will represent an 
endless hallway where subjects will have to walk through.  
 

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2.3.2 Robot-assisted gait training 
The Lokomat (version LokomatPro V6) gait exoskeleton (Hocoma AG, Volketswil, Switzerland) 
comprises two actuated leg orthoses and is used in conjunction with a body-weight support and 
treadmill. It has four degrees of freedom, allowing to control the movements of the hip and knees 
in the sagittal plane. Foot lifters can be used to assist the dorsiflexion, necessary for foot clearance 
during the swing phase. Furthermore, the Lokomat has force and position sensors in each hip and 
knee joint to measure the interaction torques between orthosis and participant and the range of 
motion, respectively.  
2.4 Procedure 
Participants will be tested during one VR enhanced RAGT session at the rehabilitation center 
TrainM (Antwerp).  
Prior to the start of the experiment, surface electrodes will be placed bilateral on the Mm. rectus 
femoris and Mm. biceps femoris to measure muscle activity. Electrode placement will follow as 
closely as possible the guidelines of SENIAM [16]. The skin underlying the electrode will be shaved 
and cleaned to improve electrode–skin contact and reduce impedance.  
At the start of the session, participants will be habituated to walking in the Lokomat without the 
virtual reality. During this habituation trial, the appropriate settings to walk comfortable in the 
exoskeleton will be determined.  To make sure all participants walk comfortable in the Lokomat, 
subjects will be provided with a body weight support (BWS) of 30%. Guidance force (GF) will be 
set on 80% for all participants. With lower levels of GF, the biofeedback values will show smaller 
changes and can become negligible. Lastly, the walking speed (WS) will be set at 2.8 km/h for all 
participants (setting Lokomat: see Appendix). Preparing and installing the participant and 
determining the appropriate Lokomat settings will take no longer than 20 minutes. When the 
Lokomat settings are defined, the Lokomat will be stopped for a moment so that the virtual reality 
can be added. Participants will put on the HMD and will start walking again for one minute while 
watching the virtual environment scene expanding at a speed matching their walking speed to get 
used to the VR. After the habituation trial, participants will walk in the Lokomat with the three 
different OF speeds [10, 15] that will be presented in a random order for 7 minutes each:  
1) slower OF: 0.5 times the individual’s comfortable speed,  
2) matched OF: same as the individual’s comfortable speed,  
3) faster OF: 2 times the individual’s comfortable speed.  
In order to normalize the registered EMG signals, the maximum voluntary isometric contraction 
(MVIC) test will be used. Each subject carries out the MVIC test 3 times for each muscle. Subjects 
must reach their maximum strength as quickly as possible and must keep it for 6 seconds. Subjects 
will perform the MVIC test in the following testing positions: prone with the knees in 30° of ﬂexion 
(Mm. biceps femoris); seated with the knees in 60°of ﬂexion, and the hips in 90° of ﬂexion (Mm. 
rectus femoris). The average of the three tests will be taken for each muscle. 

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Example of a possible VR enhanced RAGT session 
Preparation and installation 
subject + determining settings 
of the Lokomat 
Getting 
used  
to the 
VR 
Walking with  
slower OF 
Walking with  
matched OF 
Walking with  
faster OF 
Uninstalling 
subject 
20 min 
1 min 
7 min 
7 min 
7 min 
3 min 
 
Total time: 45 min 
 
 
 
2.5 Randomization 
Subjects who agree to participate in this study will be equally randomized in a 1:1 ratio into 2 groups. 
For randomization, sealed envelopes will be prepared in advance and marked on the inside with a 
VE + or VE -. During the intervention, the speed of the OF (slow, matched or fast) will also be 
randomized. To participants, it will only be said that they will have to walk in the Lokomat with 
the VR for 21 minutes. They will not know that and when the OF speed will change.  
2.6 Materials  
Surface EMG electrodes will be used to measure the muscle activity of the Mm. rectus femoris and 
Mm. biceps femoris. Surface EMG data will be captured with the ME6000 EMG-system (Mega 
Electronics Ltd) using Conmed Cleartrace electrodes (Ag/AgCl) with an active diameter of 10mm.  
The Lokomat has a biofeedback system that measures and displays participants’ activity in real-time. 
Whit the use of force sensors, this biofeedback system measures the force of the Lokomat’s drive 
motors. If the participant actively moves with the programmed gait pattern, the power required 
from the drives is small. The less active the participant is, the greater the drive force required to 
move his limbs according to the preprogrammed pattern. With this approach, two values are 
calculated for each driven joint; one for the swing phase, the other for the stance phase. This 
provides 8 biofeedback values per step cycle to characterize the degree of activity of the patient. 
Besides force sensors, the Lokomat also comprises position sensors. These sensors will be used to 
measure the range of motion (ROM) of the hip- and knee joints. 
2.7 Outcome 
2.7.1 Biomechanical quantification of active participation 
The active participation during RAGT will be measured with the use of electromyography, the force 
sensors and the position sensors of the Lokomat.  
- 
Electromyography: with the use of surface electrodes, muscle activity of the Mm. rectus 
femoris and Mm. biceps femoris of both legs will be measured. Surface electromyograms of 
these muscles will be analyzed with the use of the ‘mean absolute value’ variable. It 
represents the area under the EMG signal (AUC) once it has been rectified, meaning that all 
of the negative voltage values have been made positive. The mean absolute value will be 
measured per minute to compare the amount of muscle activity in time within one OF 
speed, between different OF speeds and between the 2 VEs.  
Pause the Lokomat to put 
on the HMD 

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- 
Force sensors: with the use of the force sensors of the Lokomat, interaction torques between 
the participant and the orthosis will be measured of the 4 joints (bilateral hip and knee). 
These interaction torques will be used to quantify how much the participant contributes to 
the walking movement him or herself. The average of these interaction torques will be 
calculated per minute to compare the amount of force in time within one OF speed, between 
different OF speeds and between the 2 VEs.  
- 
Position sensors: with the use of the position sensors of the Lokomat, it is possible to measure 
the range of motion (°) of the 4 joints (bilateral hip and knee).  
 
2.8 Statistical analysis 
Statistics will be performed using SPSS (IBM, Chicago, IL). The significance level will be set on 5%. 
Descriptive statistics will be calculated for baseline participant characteristics. Means and standard 
deviations will be calculated for continues variables and frequencies and percentages for categorical 
variables. Active participation will be measured continuously during the session. Data analysis of 
the average of every minute will be conducted. To investigate the effect of time, the different OF 
speeds, and the different VEs, repeated-measures ANOVA (within-between subject design) will be 
done.   
3. Funding 
The author disclosed receipt of the following financial support for the research, authorship and/or 
publication of this study: fellowship of the Vrije Universiteit Brussel for one year.  
 
 

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Appendix 
Setting Lokomat 
 
Start walking at 100% GF, 30% BWS and 1.5 kmph WS 
↑ WS to 2.8 kmph 
Visual inspection of the gait pattern (mainly foot clearance, hip and knee angles) 
Adapt hip and knee angle, foot straps and/or patient coefficient if necessary 
Question participant with respect to pain and comfort 
Set GF at 80% 
Visual inspection of the gait pattern (mainly foot clearance, hip and knee angles) 
Adapt hip and knee angle, foot straps and/or patient coefficient if necessary 
Question participant with respect to pain and comfort 
If necessary re-adapt WS until comfortable walking in the Lokomat is achieved 
with visual “normal” gait pattern (e.g. sufficient foot clearance, no knee buckling) 
and no pain or discomfort