ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience

Esfahlani, Shabnam Sadeghi; Thompson, Tommy; Parsa, Ali Davod; Brown, Ian E.; Cirstea, Silvia

doi:10.1016/j.heliyon.2018.e00526

Cited by 22 publications

(26 citation statements)

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“…An expanding number of analysts focus on portraying the estimation of rehabilitation intercessions that can be utilized all through the disease, from the underlying side effects to the propelled stages [4]. Our previous research [5], [6] have also shown that integrating off-the-shelf devices with video game rehabilitation program could facilitate a platform to enable…”

Section: Introductionmentioning

confidence: 99%

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

2019

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In this paper, an intuitive neuro-rehabilitation video game has been developed employing the fusion of artificial neural networks (ANNs), inverse kinematics (IK), and fuzzy logic (FL) algorithms. The embedded algorithms automatically adjust the game difficulty level based on the player's interaction with the game. Moreover, it is manifested as an alternative approach for possible movements to improve incorrect positioning through real-time visual feedback on the screen; 52 participants volunteered to engage in the program. Motor assessment scale (MAS) was determined to assess the participants' functional ability preand post-treatments. The system input is received via the Microsoft Kinect, a foot Pedal (Saitek), and the Thalmic Myo armband. The ANN classifier integrates the limb joints orientation, angular velocity, lower arms' muscle activity, hand gestures, feet sole (plantar) pressure parameters, and the MAS scores to learn from data and predict the improvement following the intervention. The fuzzy input generates a crisp output and provides a personalized rehabilitation program with the potential to be integrated into clinical protocols. Experiments to obtain the input signals and desired outputs were conducted for the learning and validation of the network. The networks pattern recognition, self-organizing map, and non-linear auto-regression analysis performed using feed-forward and Levenberg-Marquardt backpropagation (LMBP) procedure. The results showed the effectiveness of the non-linear auto-regression using the optimized LMBP algorithm to classify and visualize the target categories. Furthermore, the state of the network demonstrates the prediction accuracy exceeding 94%. Clustering algorithm grouped the data based on the similarity. Self-organizing map trained the network to learn the topology of samples with high correlation, presented outputs with high achievement.

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Section: Introductionmentioning

confidence: 99%

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

2019

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“…Stets et al (2017) work with a point cloud from Microsoft Kinect. Esfahlani et al (2018) reported on non-immersive VR in rehab. Uppot et al (2019) describe VR and AR for radiology.…”

Section: Virtual Reality In Medical Researchmentioning

confidence: 99%

Inspection of Histological 3D Reconstructions in Virtual Reality

Lobachev¹,

Berthold²,

Pfeffer³

et al. 2021

Front. Virtual Real.

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3D reconstruction is a challenging current topic in medical research. We perform 3D reconstructions from serial sections stained by immunohistological methods. This paper presents an immersive visualization solution to quality control (QC), inspect, and analyze such reconstructions. QC is essential to establish correct digital processing methodologies. Visual analytics, such as annotation placement, mesh painting, and classification utility, facilitates medical research insights. We propose a visualization in virtual reality (VR) for these purposes. In this manner, we advance the microanatomical research of human bone marrow and spleen. Both 3D reconstructions and original data are available in VR. Data inspection is streamlined by subtle implementation details and general immersion in VR.

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“…Electromyography (EMG) can be used for analyzing the degree of muscular activity when performing different VR exercises [ 51 ] or the use of myoelectric signals for controlling prosthetics [ 52 ]. VR rehabilitation game examples include the use of Microsoft Kinect and Myo gesture control armband (with EMG integrated) for treating people with neurological disorders [ 53 ]. Regarding EEG monitoring, this helps studying the effects of VR training over neural reorganization and can be used as a facilitator for a brain–computer interface (BCI) [ 54 ].…”

Section: Ict Solutions For Neurological Disorders Treatmentmentioning

confidence: 99%

Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement

Stănică

Moldoveanu

Portelli

et al. 2020

Sensors

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As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system’s accuracy and investigate a wireless alternative based on neural networks.

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ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience

Cited by 22 publications

References 50 publications

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Inspection of Histological 3D Reconstructions in Virtual Reality

Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement

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