To correlate existing evaluation tools with clinical information on Duchenne muscular dystrophy (DMD) patients following age and to investigate genetic mutation and its relationship with clinical function. The medical records of 121 children with DMD who had visited the pediatric rehabilitation clinic from 2006 to 2009 were reviewed. The mean patient age was 9.9±3.4 years and all subjects were male. Collected data included Brooke scale, Vignos scale, bilateral shoulder abductor and knee extensor muscles power, passive range of motion (PROM) of ankle dorsi-flexion, angle of scoliosis, peak cough flow (PCF), fractional shortening (FS), genetic abnormalities, and use of steroid. The Brooke and Vignos scales were linearly increased with age (Brooke (y(1)), Vignos (y(2)), age (x), y(1)=0.345x-1.221, R(Brooke) (2)=0.435, y(2)=0.813x-3.079, R(Vignos) (2)=0.558, p<0.001). In relation to the PROM of ankle dorsi-flexion, there was a linear decrease in both ankles (right and left R(2)=0.364, 0.372, p<0.001). Muscle power, Cobb angle, PCF, and FS showed diversity in their degrees, irrespective of age. The genetic test for dystrophin identified exon deletions in 58.0% (69/119), duplications in 9.2% (11/119), and no deletions or duplications in 32.8% (39/119). Statistically, the genetic abnormalities and use of steroid were not definitely associated with functional scale. The Brooke scale, Vignos scale and PROM of ankle dorsi-flexion were partially available to assess DMD patients. However, this study demonstrates the limitations of preexisting scales and clinical parameters incomprehensively reflecting functional changes of DMD patients.

The population of adults diagnosed with cerebral palsy (CP) is increasing along with the survival rate of children born with the disability. Adults with CP need health services for the continued monitoring and management of their condition. Moreover, the development of additional health problems in adulthood increases the need for ongoing access to health services. Adults with CP manifest a higher rate of chronic health conditions and eventual decline in strength and functional reserve, deterioration in physical activity, increased risk of musculoskeletal complications, and gradual changes in swallowing ability. They are also reported to exhibit difficulty engaging socially and have a low health-related quality of life (QOL). However, there are a large number of adults with CP who cannot access medical services adequately and are therefore not effectively treated. To overcome these apparent challenges, we need to fully comprehend the healthcare needs of adults with CP to develop adultfocused health services. Further research is needed regarding the impact of physical activity, nutrition, sarcopenia, myeloradiculopathy, and swallowing function on QOL.

A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified. The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories. The average correlation coefficient between the predicted and real trajectories in the MEG case was $$0.705 \pm 0.292$$ ( $${\text{p}} < 0.001$$ ). In the EEG case, it was $$0.684 \pm 0.309$$ ( $${\text{p}} < 0.001$$ ). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively. A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because the non-invasive neural signal is less accurate. However the success rate could be sufficiently improved for practical applications by using additional sensors. Robot arm control based on hand trajectories predicted from EEG would allow for portability, and the performance with EEG was comparable to that with MEG.