We developed a rehabilitation system by using the virtual reality technique and the Kinect in this paper. The system combines rehabilitation training with HMI and serious game organically, and provides a game and motion database to meet different patients' demands. Extended interface of game database is provided in two ways: personalized games can be developed by Virtools and Flash games which are suitable for patients' rehabilitation can be download from the Internet directly. In addition, the system provides patients with flexible interaction and easy control mode, and also presents real time data recording. An objective and subjective evaluation method is proposed to review the effectiveness of the rehabilitation training. According to the results of short questionnaires and the evaluation results of patients' rehabilitation training, the system compared with traditional rehabilitation can record and analyze the training data, which is useful to make rehabilitation plans. More entertainment and lower cost will increase patients' motivation, which helps to increase the rehabilitation effectiveness.
The cognitive impairment of type 2 diabetes patients caused by long-term metabolic disorders has been the current focus of attention. In order to find the related electroencephalogram (EEG) characteristics to the mild cognitive impairment (MCI) of diabetes patients, this study analyses the EEG synchronization with the method of multi-channel synchronization analysis--S estimator based on phase synchronization. The results showed that the S estimator values in each frequency band of diabetes patients with MCI were almost lower than that of control group. Especially, the S estimator values decreased significantly in the delta and alpha band, which indicated the EEG synchronization decrease. The MoCA scores and S value had a significant positive correlation in alpha band.
The clinical electroencephalogram (EEG) monitoring systems based on personal computer system can not meet the requirements of portability and home usage. The epilepsy patients have to be monitored in hospital for an extended period of time, which imposes a heavy burden on hospitals. In the present study, we designed a portable 16-lead networked monitoring system based on the Android smart phone. The system uses some technologies including the active electrode, the WiFi wireless transmission, the multi-scale permutation entropy (MPE) algorithm, the back-propagation (BP) neural network algorithm, etc. Moreover, the software of Android mobile application can realize the processing and analysis of EEG data, the display of EEG waveform and the alarm of epileptic seizure. The system has been tested on the mobile phones with Android 2.3 operating system or higher version and the results showed that this software ran accurately and steadily in the detection of epileptic seizure. In conclusion, this paper provides a portable and reliable solution for epileptic seizure monitoring in clinical and home applications.