The present paper presents the design of a remote monitoring system based on smartphone and mobile internet. The system can realize functions such as multi-physiological parameter collection, micromation of collecting equipment, real-time monitoring, remote data transmission, automatic alarm, physiological parameter analyze and Global Position System (GPS) location of patient's position. Besides acting as a receiver and transmission platform, smartphone can also process and analyze the physiological parameters, such as detection of the apnea from electrocardiogram (ECG). The system contains technologies of MCU, Bluetooth transmission, Android and Wed development, wavelet transform, mobile communication as a whole. It propels further developments of the remote mobile medical based on smartphone.
The development of muscle strength evaluating system based on Android system was developed in this research. The system consists of a lower unit and an intelligent mobile terminal. The pressure sensor of the lower unit was used to collect muscle strength parameters. And the parameters were sent to the Android device through the wireless Bluetooth serial port. Then the Android device would send the parameters to the doctor monitored platform through the Internet. The system realized analyzing the muscle strength parameters and real-time displaying them. After it ran on the Android mobile phones, it showed an effective result which proved that the system combined with mobile platform could make more convenient for the patients to assess their own muscle strength. It also provided reliable data references for doctors to know the patients' rehabilitation condition and to make the next rehabilitation plan.
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.
Neurosurgery navigation system, which is expensive and complicated to operate, has a low penetration rate, and is only found in some large medical institutions. In order to meet the needs of other small and medium-sized medical institutions for neurosurgical navigation systems, the scalp localization system of neurosurgery based on augmented reality (AR) theory was developed. AR technology is used to fuse virtual world images with real images. The system integrates computed tomography (CT) or magnetic resonance imaging (MRI) with the patient's head in real life to achieve the scalp positioning. This article focuses on the key points of Digital Imaging and Communications in Medicine (DICOM) standard, three-dimensional (3D) reconstruction, and AR image layer fusion in medical image visualization. This research shows that the system is suitable for a variety of mobile phones, can achieve two-dimensional (2D) image display, 3D rendering and clinical scalp positioning application, which has a certain significance for the auxiliary neurosurgical head surface positioning.
In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience’s emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.