Whole body vibration (WBV) exposure in elderly adults is found to increase physical activity and so the overall health status. For analyzing effects of WBV on muscle power, balance and overall mobility among elderly adults of age more than 60 years, comprehensive article search was performed from year 2013 till January 2017 on electronic databases of Medley, Google scholar and IEEE (institute of electrical and electronic engineering), search strategy and inclusion criteria was specified initially and then articles were recruited accordingly. Randomized controlled trails targeting WBV effects, compared to control group with some moderate exercise or no intervention at all, on muscle power, balance and mobility were studied and data extracted about author name, publication year, age and number of participants, WBV parameters, protocols of study, vibrating platform, description and comparison among interventions. Initially 656 records were identified in preliminary search through the databases, four studies finally were considered as eligible. Lower body muscle strength (14.8 ± 3.3 to 16.5 ± 3.6) and upper body strength (17.9 ± 4.5 to 20.3 ± 3.6) have shown significant results in all the studies. Improvement in balance and mobility was also significant with P<0.005 in comparison with control groups with no interventions. WBV alone or combined with exercise training program seems to improve muscle strength, overall balance and increased mobility among elder adults. Direct comparison among studies was not possible because of differences among parameters and study protocols. More extensive and well-designed research is still needed to establish efficacy and to understand the effects and influences more clearly.
The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which is a motor recovery system with the intent of rehabilitation, focuses on the hand organs and utilizes a brain-computer interface (BCI) technology. The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%. Moreover, the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks (ANN). The overall model performance reveals an accuracy up to 96.56% with sensitivity of 94.22% and specificity of 98.8%. Therefore, the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.
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