Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.
Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.
Methods: Post-stroke patients who attended the outpatient clinics in three hospitals of Peninsular Malaysia were enrolled in the study. The risk of malnutrition was assessed using the Malnutrition Risk Screening Tool-Hospital. Data including demographic characteristics, clinical profiles, dietary nutrients intake, body mass index (BMI) and hand grip strength were collected during the survey. The crude odds ratio (OR) and adjusted odds ratio (AOR) were reported for univariate and multivariate logistic regression analyses, respectively.
Results: Among 398 patients included in the study, 40% were classified as high-risk for malnutrition. In the multivariable logistic regression, tube feeding (AOR: 13.16, 95% confidence interval [CI]: 3.22-53.77), loss of appetite (AOR: 8.15, 95% CI: 4.71-14.12), unemployment (AOR: 4.26, 95% CI: 1.64-11.12), wheelchair-bound (AOR: 2.23, 95% CI: 1.22-4.09) and BMI (AOR: 0.87, 95% CI: 0.82-0.93) were found to be significant predictors of malnutrition risk among stroke patients.
Conclusion: The risk of malnutrition is highly prevalent among post-stroke patients. Routine nutritional screening, identification of risk factors, and continuous monitoring of dietary intake and nutritional status are highly recommended even after the stroke patient is discharged.
MATERIALS AND METHODS: A cross-sectional study was conducted to evaluate the perceptions of nurses (n = 45) and students (n = 6) when performing patient transfers from bed to wheelchair and vice versa using the NEAR-1 compared to an existing floor lift, walking belt, and manual transfer. Participants filled out surveys evaluating the perceived task demands and usability of the NEAR-1, as well as open-ended interviews.
RESULTS: The use of the NEAR-1 significantly reduced the mean of all NASA-TLX constructs (p
Purpose: To investigate the effects of 'graded exercise integrated with education' on physical fitness, exercise self-efficacy (ESE), and physical activity (PA) levels among subacute and chronic wheelchair-dependent paraplegia patients.
Overview of Literature: Most of the chronic spinal cord injury (SCI) patients had low physical fitness due to a sedentary lifestyle and lack of ESE after discharge from a rehabilitation program. Education may encourage them to engage with exercise to regain and maintain their physical fitness. However, there is a lack of research to support the effects of exercise integrated with education after an SCI.
Methods: A total of 44 participants will be assigned to either the experimental group (graded exercise integrated with education) or active control (conventional physical therapy). The experimental group will receive graded strength and aerobic exercise training according to their progression criteria. They will attend an education program during and after the rehabilitation program. The control group will only receive conventional physical therapy during their in-rehabilitation program. This study will be conducted during a period of 16 weeks, consisting of 8 weeks of in-rehabilitation and 8 weeks post-rehabilitation. Statistical analysis will be performed using the IBM SPSS ver. 21.0 (IBM Corp., Armonk, NY, USA) at a significance level of p≤0.05.
Results: The primary outcome measures will be upper-limb isokinetic strength, isometric grip strength, and cardiorespiratory fitness. The secondary outcomes will be ESE and PA levels.
Conclusions: An intervention that combines exercise training and education may be warranted to enhance the physical fitness, ESE, and PA levels in SCI patients. This trial was registered with ClinicalTrials.gov (NCT03420170).
FINDINGS AND CONCLUSIONS: Stroke recovery involves adapting to new limitations and discovering the support necessary to live life. These changes are influenced by a range of environmental factors. Healthcare professionals need to support stroke patients in identifying challenges and work to find innovative ways to address them. Stroke survivors may benefit from the use of an assistive device beyond its clinical function to participate purposefully in activities of daily living. Implications for Rehabilitation Stroke is a cause of disability that limits everyday activities and reduces social participation. Assistive devices help achieve independence, social inclusion and shape stroke recovery. Individuals with disabilities in low and middle income countries often do not have access to assistive devices and resort to innovative solutions that are purpose built. Stroke recovery involves adapting to new limitations and discovering the support necessary to live life as best as possible.