Smart multi-level tool for remote patient monitoring based on a wireless sensor network and mobile augmented reality.

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.

Driving Maximal Frequency Content and Natural Slopes Sharpening for Image Amplification with High Scale Factor.

BACKGROUND: In this paper, a method for adaptive Pure Interpolation (PI) in the frequency domain, with gradient auto-regularization, is proposed. METHODS: The input image is transformed into the frequency domain and convolved with the Fourier Transform (FT) of a 2D sampling array (interpolation kernel) of initial size L × M. The Inverse Fourier Transform (IFT) is applied to the output coefficients and the edges are detected and counted. To get a denser kernel, the sampling array is interpolated in the frequency domain and convolved again with the transform coefficients of the original image of low resolution and transformed back into the spatial domain. The process is repeated until a maximum number of edges is reached in the output image, indicating that a locally optimal magnification factor has been attained. Finally, a maximum ascend-descend gradient auto-regularization method is designed and the edges are sharpened. RESULTS: For the gradient management, a new strategy is proposed, referred to as the Natural bi- Directional Gradient Field (NBGF). It uses a natural following of a pair of directional and orthogonal gradient fields. CONCLUSION: The proposed procedure is comparable to novel algorithms reported in the state of the art with good results for high scales of amplification.