An interventional study of Active Comparator: Physical Training and Experimental: Virtual Reality with Force Feedback in Motor Skills and Healthy Volunteer, sponsored by University of Ontario Institute of Technology. Active, not recruiting at 1 site in Canada. Open to participants aged 18 Years to 35 Years. Per ClinicalTrials.gov, last updated 2026-10-29.
Sponsored by University of Ontario Institute of Technology · Not applicable, Interventional, and Other
The goal of this interventional study is to learn how force feedback in virtual reality training affects electroencephalography (EEG) biomarkers and/or real-world motor performance. The main question it aims to answer is:
● How does physical training compared to virtual reality training with force feedback compared with virtual reality training without force feedback affect EEG biomarkers and/or motor performance?
Participants will:
Background: Virtual reality (VR) is an immersive technology that allows users to experience and interact with digital environments. It is becoming increasingly popular in professional fields like medical training, sports, and rehabilitation (Rizzo \& Koenig, 2017). However, VR systems don't always provide the same physical feedback as the real world, which can impact how users learn and perform physical tasks. This is particularly relevant when it comes to force feedback, which involves using technology to simulate the resistance, pressure, and movement that your body feels when interacting with objects. For example, when picking up a water bottle, sensors in your hand allow your brain to understand the shape, size, weight, and location of the water bottle. In VR, this feedback is usually not provided, so participants may not feel the texture or resistance of objects the way that they would in the real world (Kilteni et al., 2012). These mismatches can impact the effectiveness of motor training in VR, making it harder for users to transfer skills learned in VR to real-world settings. This research aims to explore how force feedback differs in VR compared to the real world, and how these differences affect motor learning.
Purpose: First, we seek to understand how the differences in force feedback between VR and the real world impact motor learning. VR has been used in rehabilitation and motor training with some success, but the differences in sensory feedback have not been fully explored. For example, previous studies have shown that force feedback can improve motor learning in virtual environments, but force feedback is often lacking in current VR systems (Sigrist et al., 2013). Furthermore, research suggests that force feedback and visual accuracy can also play crucial roles in how well users learn and transfer skills (Riener \& Harders, 2012). This study will build on these findings to determine if and how the addition or lack of force feedback in VR affects motor learning.
Methodology: Upon providing informed consent, participants undergo demographic screening and pre-screening questionnaires to rule out confounding neck or upper limb pain. Participants are then randomly assigned to one of three parallel training arms:
Experiment Timeline:
University of Ontario Institute of Technology is the lead sponsor of 9 studies on the registry; 2 are open to participants now.
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Exclusion Criteria:
Participants use the physical simulator complete with an internal mechanism that is activated with resistance and coded with accurate trajectory.
Other: Active Comparator: Physical Training
Participants use the virtual reality simulator along with a mechanism that is activated with resistance and coded with accurate trajectory.
Other: Experimental: Virtual Reality with Force Feedback
Participants use the virtual reality simulator along with a mechanism that is limp and does not provide resistance.
Other: Sham Comparator: Virtual Reality without Force Feedback
The participant will extract a physical round object from a real world physical cave model through a tunnel using physical forceps and will be guided by resistance from an attached mechanism.
The participant will extract the round object from the cave in virtual reality using physical forceps and will be guided by resistance from the simulator.
The participant will extract a physical round object from the cave using physical forceps but will not receive any resistance from the simulator.
Resting State Electroencephalography
Participants will wear a 64-electrode whole-head electroencephalography (EEG) cap to measure their brain signals. Once the EEG cap has been prepared using conductive gel, a resting state EEG recording will be performed. Participants will be asked to remain sitting still and relaxed in a chair. While sitting they will be asked to stare at a cross symbol displayed on a white screen on a monitor in front of them. While doing this, their brain signals will be recorded through the EEG.
Time frame: 2 blocks of 5 minutes each before and after the training, 10 minutes total
Trajectory Measurement
The trajectory that the participants used to extract the round object from the cave will be measured using the simulator internal mechanism and compared to the expert trajectory that is coded within the simulator.
Time frame: The data will be collected over 30 to 60 minutes/
EEG biomarkers related to proprioception and error
Event-related EEG potentials and error related negativity will be collected in relation to specific physical stages of the forceps use.
Time frame: Over the 30 to 60 minutes of skill acquisition.
Plan to share: Undecided — TCPS2 in Canada is currently requiring a new layer of participant permission prior to sharing even de-identified data, so there is no guarantee that participants will consent to this.
Started Oct 2026 (actual). Primary completion is estimated for Jul 2027, 9 months after the start.
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From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is active, not recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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University of Ontario Institute of Technology