The purpose of this study was to investigate the effects of virtual reality-based cognitive–motor rehabilitation (VRCMR) on the rehabilitation motivation and cognitive function in older adults. This study enrolled 40 older adults with mild cognitive impairment (MCI), living in the community. The subjects were randomly assigned to a VRCMR group (n = 20) or a conventional cognitive rehabilitation (CCR) group (n = 20). The VRCMR group underwent VRCMR using MOTOcog, a computer recognition program, whereas the CCR group underwent conventional cognitive rehabilitation, which included puzzles, wood blocks, card play, stick construction activity, and maze activity. Both interventions were performed 30 min per day, 5 days/week, for 6 weeks. This study performed a cognitive assessment using the Montreal Cognitive Assessment (MoCA) scale, Trail Making Test A and B (TMT-A/B), and Digit Span Test forward and backward (DST-forward/backward). In addition, a 0-to-10 numeric rating self-report scale was used to assess interest and motivation during the rehabilitation training. After the intervention, the VRCMR group showed a significantly greater improvement in the MoCA (p = 0.045), TMT-A (p = 0.039), TMT-B (p = 0.040), and DST-forward (p = 0.011) scores compared to the CCR group, but not in the DST-backward score (p = 0.424). In addition, subjects in the experimental group had significantly higher interest (p = 0.03) and motivation (p = 0.03) than those in the control group. Cohen’s d effect size was 0.4, 0.3, 0.35, 0.4, and 0.5 for the MoCA, TMT-A, TMT-B, DST-forward, and DST-backward tests, respectively. This study demonstrates that VRCMR enhances motivation for rehabilitation and cognitive function in older adults with MCI better than CCR.

Objective: To compare the effects of game-based chin-tuck against resistance exercise and head-lift ...

Background: Electromyographic biofeedback (EMG-BF) is known to be an effective therapy for stroke rehabilitation. However, because few studies have investigated the therapy in patients with dysphagia, its effectiveness is not yet clear. This study aimed to investigate the effect of neuromuscular electrical stimulation (NMES) using EMG-BF on swallowing function in stroke patients with oropharyngeal dysphagia. Methods: In this study, 10 patients with dysphagia were recruited. The 1-group, pre−post study design was adopted. All subjects received NMES combined with EMG-BF in the suprahyoid area. Electrical stimulation was provided as a reward when the electrical signal generated by effortful swallowing reached a preset threshold. The intervention was provided for 30 minutes a day, 5 times a week for 4 weeks. The videofluoroscopic dysphagia scale (VDS) and penetration–aspiration scale (PAS) based on the videofluoroscopic swallowing study were used to evaluate the swallowing function. Results: Pre-intervention showed no significant differences in all items of VDS (P > .05). However, there was a statistically significant change in VDS from 13.36 ± 5.94 to 9.36 ± 5.14 (P = .015) in the oral phase, and from 38.36 ± 7.42 to 20.71 ± 14.61 (P = .016) in the pharyngeal phase. The PAS scores showed significant change from 5.14 ± 2.27 to 3.00 ± 1.00 (P = .031). Conclusion: This study demonstrated that the use of NMES combined with EMG-BF had the potential to improve oropharyngeal swallowing in stroke patients with dysphagia.

BACKGROUND: Game-based exercise is effective for improving strength and motor function in stroke patients undergoing rehabilitation, and it creates fun and motivation for exercise. OBJECTIVE: We investigated the effect of game-based exercise on hand

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.