<span>Smart and connected health care is of specific significance in the spectrum of applications enabled the Internet of Things (IoT). Networked sensors, either embedded inside our living system or worn on the body, enable to gather rich information regarding our physical and mental health. In specific, the accessibility of information at previously unimagined scales and spatial longitudes combined with the new generation of smart processing algorithms can expedite an advancement in the medical field, from the current post-facto diagnosis and treatment of reactive framework, to an early-stage proactive paradigm for disease prognosis combined with prevention and cure as well as overall administration of well-being rather than ailment. This paper sheds some light on the current methods accessible in the Internet of Things (IoT) domain for healthcare applications. The proposed objective is to design and create a healthcare system centered on Mobile-IoT by collecting patient information from different sensors and alerting both the guardian and the doctor by sending emails and SMS in a timely manner. It remotely monitors the physiological parameters of the patient and diagnoses the illnesses swiftly. </span>
A n algorithm for computing 3D motion (velocity field and motion parameters) from a sequence of range images is presented. The algorithm is based on a new integral 3 0 rigid motion invariant feature which can be computed locally at eve y point of the moving surface.This feature is the trace of a 3 x 3 matrix (referred to as "feature matrix'> in the paper) which is closely related to the moment of inertia tensor in physics. This trace of the "feature matrix" at any point provides a quantitative measure of the local shape of the surface around that point. Since the trace feature is rigid motion invariant, it remains conserved along the trajectory of every moving point. Based on that, a 3D Flow Constraint Equation is formulated at every point on the surface, which can be solved for the velocity at the point using optical flow techniques.Since the proposed feature is integral in nature, it is robust in terms of noise and discontinuity in the moving surface. Theoretical analysis is provided t o justify these claims. Experimental results are also provided to justify the efficacy of the method.
An algorithm for computing three-dimensional (3-D) velocity field and motion parameters from a range image sequence is presented. It is based on a new integral 3-D rigid motion invariant feature-the trace of a 3x3 "feature matrix" related to the moment of inertia tensor. This trace can be computed at every point of a moving surface and provides a quantitative measure of the local shape of the surface. Based on the feature's conservation along the trajectory of every moving point, a 3-D Flow Constraint Equation is formulated and solved for the velocity field. The robustness of the feature in presence of noise and discontinuity is analyzed.
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