The Emergent Role of Virtual Reality in the Treatment of Neuropsychiatric Disease

Background/Objectives: This study explores the integration of wearable sensors and artificial intelligence (AI) for Human Activity Recognition (HAR) in the diagnosis and rehabilitation of Parkinson’s disease (PD). The objective was to develop a proof-of-concept model based on internal reproducibility, without external generalization, that is capable of distinguishing pathological movements from healthy ones while ensuring clinical relevance and patient safety. Methods: Nine subjects, including eight patients with Parkinson’s disease and one healthy control, were included. Motion data were collected using the Motigravity platform, which integrates inertial sensors in a controlled environment. The signals were automatically segmented into fixed-length windows, with poor-quality segments excluded through preprocessing. A hybrid CNN-LSTM (Convolutional Neural Networks—Long Short-Term Memory) model was trained to classify motion patterns, leveraging convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. The Motigravity system provided a controlled hypogravity environment for data collection and rehabilitation exercises. Results: The proposed CNN-LSTM model achieved a validation accuracy of 100%, demonstrating classification potential. The Motigravity system contributed to improved data reliability and ensured patient safety. Despite increasing class imbalance in extended experiments, the model consistently maintained perfect accuracy, suggesting strong generalizability after external validation to overcome the limitations. Conclusions: Integrating AI and wearable sensors has significant potential to improve the HAR-based classification of movement impairments and guide rehabilitation strategies in PD. While challenges such as dataset size remain, expanding real-world validation and enhancing automated segmentation could further improve clinical impact. Future research should explore larger cohorts, extend the model to other neurodegenerative diseases, and evaluate its integration into clinical rehabilitation workflows.

(1) Background: Cardiopulmonary resuscitation (CPR), as a form of basic life support, is critical for maintaining cardiac and cerebral perfusion during cardiac arrest, a medical condition with high expected mortality. Current guidelines emphasize the importance of rapid recognition and prompt initiation of high-quality CPR, including appropriate cardiac compression depth and rate. As space agencies plan missions to the Moon or even to explore Mars, the duration of missions will increase and with it the chance of life-threatening conditions requiring CPR. The objective of this review was to examine the effectiveness and feasibility of chest compressions as part of CPR following current terrestrial guidelines under hypogravity conditions such as those encountered on planetary or lunar surfaces; (2) Methods: A systematic literature search was conducted by two independent reviewers (PubMed, Cochrane Register of Controlled Trials, ResearchGate, National Aeronautics and Space Administration (NASA)). Only controlled trials conducting CPR following guidelines from 2010 and after with advised compression depths of 50 mm and above were included; (3) Results: Four different publications were identified. All studies examined CPR feasibility in 0.38 G simulating the gravitational force on Mars. Two studies also simulated hypogravity on the Moon with a force of 0.17 G/0,16 G. All CPR protocols consisted of chest compressions only without ventilation. A compression rate above 100/s could be maintained in all studies and hypogravity conditions. Two studies showed a significant reduction of compression depth in 0.38 G (−7.2 mm/−8.71 mm) and 0.17 G (−12.6 mm/−9.85 mm), respectively, with nearly similar heart rates, compared to 1 G conditions. In the other two studies, participants with higher body weight could maintain a nearly adequate mean depth while effort measured by heart rate (+23/+13.85 bpm) and VO_2max (+5.4 mL·kg⁻¹·min⁻¹) increased significantly; (4) Conclusions: Adequate CPR quality in hypogravity can only be achieved under increased physical stress to compensate for functional weight loss. Without this extra effort, the depth of compression quickly falls below the guideline level, especially for light-weight rescuers. This means faster fatigue during resuscitation and the need for more frequent changes of the resuscitator than advised in terrestrial guidelines. Alternative techniques in the straddling position should be further investigated in hypogravity.

Background/Objectives: Parkinsonian gait is characterized by impaired motor adaptability, altered temporal organization, and reduced movement variability. While data augmentation is commonly used to mitigate class imbalance in gait-based machine learning models, conventional strategies often ignore physiological differences between healthy and pathological movements, potentially distorting meaningful motor dynamics. This study explores whether preserving healthy motor variability while selectively augmenting pathological gait signals can improve the robustness and physiological coherence of gait pattern classification models. Methods: Eight patients with Parkinsonian gait patterns and forty-eight healthy participants performed walking tasks on the Motigravity platform under hypogravity conditions. Full-body kinematic data were acquired using wearable inertial sensors. A selective augmentation strategy based on smooth time-warping was applied exclusively to pathological gait segments (×5, σ = 0.2), while healthy gait signals were left unaltered to preserve natural motor variability. Model performance was evaluated using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture across multiple augmentation configurations. Results: Selective augmentation of pathological gait signals achieved the highest classification performance (94.1% accuracy, AUC = 0.97), with balanced sensitivity (93.8%) and specificity (94.3%). Performance decreased when augmentation exceeded an optimal range of variability, suggesting that beneficial augmentation is constrained by physiologically plausible temporal dynamics. Conclusions: These findings demonstrate that physiology-informed, selective data augmentation can improve gait pattern classification under constrained data conditions. Rather than supporting disease-specific diagnosis, this proof-of-concept study highlights the importance of respecting intrinsic differences in motor variability when designing augmentation strategies for clinical gait analysis. Future studies incorporating disease-control cohorts and subject-independent validation are required to assess specificity and clinical generalizability.