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%.
OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.
METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.
RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.
DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.
CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.
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).
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