Fully portable low-cost motion capture system with real-time feedback for rehabilitation treatment

Kritikos, Jacob; Mehmeti, Anxhelino; Nikolaou, George; Koutsouris, Dimitris

doi:10.1109/icvr46560.2019.8994561

Cited by 6 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our goal has been to create virtual environments that are as realistic and immersive as possible to optimize the therapeutic effects of this type of treatment. Initially, we began by exploring ways to replace traditional VR controllers with motion-tracking cameras in order to enhance the immersive qualities [56][57][58]. Following that, we created a system that incorporates doctors in VR simulations during treatment [28].…”

Section: Discussionmentioning

confidence: 99%

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Kritikos,

Makrypidis,

Alevizopoulos

et al. 2023

Virtual Worlds

Self Cite

View full text Add to dashboard Cite

Brain–Machine Interfaces (BMIs) have made significant progress in recent years; however, there are still several application areas in which improvement is needed, including the accurate prediction of body movement during Virtual Reality (VR) simulations. To achieve a high level of immersion in VR sessions, it is important to have bidirectional interaction, which is typically achieved through the use of movement-tracking devices, such as controllers and body sensors. However, it may be possible to eliminate the need for these external tracking devices by directly acquiring movement information from the motor cortex via electroencephalography (EEG) recordings. This could potentially lead to more seamless and immersive VR experiences. There have been numerous studies that have investigated EEG recordings during movement. While the majority of these studies have focused on movement prediction based on brain signals, a smaller number of them have focused on how to utilize them during VR simulations. This suggests that there is still a need for further research in this area in order to fully understand the potential for using EEG to predict movement in VR simulations. We propose two neural network decoders designed to predict pre-arm-movement and during-arm-movement behavior based on brain activity recorded during the execution of VR simulation tasks in this research. For both decoders, we employ a Long Short-Term Memory model. The study’s findings are highly encouraging, lending credence to the premise that this technology has the ability to replace external tracking devices.

show abstract

Section: Discussionmentioning

confidence: 99%

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Kritikos,

Makrypidis,

Alevizopoulos

et al. 2023

Virtual Worlds

Self Cite

View full text Add to dashboard Cite

show abstract

“…One signifi cant advantage of VR is the ability to tailor the virtual environment to the personal needs of the patient. Therapists can adjust the intensity and nature of the stimuli in real time [11,12], allowing for personalized treatment plans. This customization enhances the therapy's relevance and effectiveness, ensuring that patients are exposed to appropriate levels of challenge and support.…”

Section: Introductionmentioning

confidence: 99%

Virtual reality, electrophysiology & motion tracking technologies in mental illnesses

Konstantina,

Iakovos

2024

Ann Psychiatry Treatm

View full text Add to dashboard Cite

The following mini-review paper explores the integration of Virtual Reality (VR), motion tracking, and electrophysiological sensors in the context of exposure therapy and mental health treatment. These technologies collectively offer innovative approaches for enhancing therapeutic goals, providing immersive environments, and enabling precise monitoring of physiological responses. The paper discusses their applications, benefits, and potential challenges, underscoring their transformative impact on mental health care.

show abstract

“…VR rehabilitation provides a gamified experience by creating assorted virtual environments during rehabilitation sessions for patients through monitors or VR tools [7]. To provide a proper level of challenge to individual users, the task difficulty of the VR game exercises must be modifiable to optimize motor learning according to the findings from assessments [3].…”

Section: Introductionmentioning

confidence: 99%

Correlation Between Knee Muscle Strength and Maximal Cycling Speed Measured Using 3D Depth Camera in Virtual Reality Environment

Kim

Jeon

Park

et al. 2022

Physical Therapy Korea

View full text Add to dashboard Cite

Background: Virtual reality (VR) programs based on motion capture camera are the most convenient and cost-effective approaches for remote rehabilitation. Assessment of physical function is critical for providing optimal VR rehabilitation training; however, direct muscle strength measurement using camera-based kinematic data is impracticable. Therefore, it is necessary to develop a method to indirectly estimate the muscle strength of users from the value obtained using a motion capture camera. Objects:The purpose of this study was to determine whether the pedaling speed converted using the VR engine from the captured foot position data in the VR environment can be used as an indirect way to evaluate knee muscle strength, and to investigate the validity and reliability of a camera-based VR program.Methods: Thirty healthy adults were included in this study. Each subject performed a 15-second maximum pedaling test in the VR and built-in speedometer modes. In the VR speedometer mode, a motion capture camera was used to detect the position of the ankle joints and automatically calculate the pedaling speed. An isokinetic dynamometer was used to assess the isometric and isokinetic peak torques of knee flexion and extension. Results:The pedaling speeds in VR and built-in speedometer modes revealed a significantly high positive correlation (r = 0.922). In addition, the intra-rater reliability of the pedaling speed in the VR speedometer mode was good (ICC [intraclass correlation coefficient] = 0.685). The results of the Pearson correlation analysis revealed a significant moderate positive correlation between the pedaling speed of the VR speedometer and the peak torque of knee isokinetic flexion (r = 0.639) and extension (r = 0.598). Conclusion:This study suggests the potential benefits of measuring the maximum pedaling speed using 3D depth camera in a VR environment as an indirect assessment of muscle strength. However, technological improvements must be followed to obtain more accurate estimation of muscle strength from the VR cycling test.

show abstract

Fully portable low-cost motion capture system with real-time feedback for rehabilitation treatment

Cited by 6 publications

References 31 publications

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Virtual reality, electrophysiology & motion tracking technologies in mental illnesses

Correlation Between Knee Muscle Strength and Maximal Cycling Speed Measured Using 3D Depth Camera in Virtual Reality Environment

Contact Info

Product

Resources

About