The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

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Objective To determine whether a fully immersive virtual reality (VR) intervention combined with conventional rehabilitation (CR) can improve upper limb function more than CR alone in patients with spinal cord injury (SCI), we conducted a prospective, randomized, controlled clinical trial.Methods Participants were randomly assigned to either the control group (CG; n=10) or experimental group (EG; n=10). The participants in the CG received 60 minutes of conventional therapy per day, 4 days per week for 4 weeks, whereas those in the EG received 30 minutes of VR training and 30 minutes of conventional therapy per day, 4 days per week for 4 weeks. The clinical outcome measures included Medical Research Council grade, the American Spinal Injury Association upper extremity motor score (ASIA-UEMS), and scores in the Hand Strength Test, Box and Block Test, Nine-Hole Peg Test, Action Research Arm Test, and Korean version of the Spinal Cord Independence Measure (K-SCIM). The assessments were performed at the beginning (T0) and end of the intervention (T1).Results Grip power and K-SCIM score significantly improved in the EG after the intervention. When comparing differences between the groups, elbow extensor, wrist extensor, ASIA-UEMS, grip power, lateral pinch power, and palmar pinch power were all significantly improved.Conclusion VR training of upper limb function after SCI can provide an acceptable adjunctive rehabilitation method without significant adverse effects.