Remote healthcare monitoring is a crucial component in the field of medical Internet of Things (IoT), which effectively achieves remote monitoring, collection, and transmission of physiological data by combining AI algorithms with intelligent health monitoring systems to improve people’s quality of life and health. In this work, an AI-enabled scalable smartphone photonic sensing system is developed for remote healthcare monitoring using fiber optic sensors and a smartphone. The smartphone serves as both the light source and interrogator for the system, with the ability to connect to the network for integration with the IoT. Scalability is achieved through a multichannel framework, and by modifying the connector design, the system can incorporate more sensors to monitor multiple physiological parameters in real-time. In addition to acquiring basic respiratory and heartbeat signals and various gait parameters, the system successfully implemented the recognition of various gait patterns and fatigue monitoring using an adapted MobileNetV3 neural network structure. The accuracy of 98.5% for the gait pattern recognition task, and 94% and 95.8% for the mental and muscle fatigue monitoring tasks, respectively, demonstrates the system’s potential as a telemedicine tool. Additionally, the low cost, noninvasiveness, and portability of this innovative sensor system make it highly generalizable.

A risk aware UAS path planning methodology is proposed using monetary value as the sole cost metric. A third party ground risk model is used to generate a non-uniform costmap for a modified A* heuristic search. The Value of a Prevented Fatality provides a basis to convert fatality risk to monetary value terms as a Human Value at Risk (HVaR) measure. Additional operating and UAS Capital Value at Risk (CVaR) costs are modelled to provide a holistic monetary cost model for path cost minimisation. A number of future cost variants are investigated based upon prior work for a realistic urban-rural mix logistics case study in Southern England. Results show increasingly risk averse paths with decreasing future UAS operating costs.

Visual attention guides our gaze to relevant parts of the viewed scene, yet the moment-to-moment relocation of gaze can be different among observers even though the same locations are taken into account. Surprisingly, the variability of eye movements has been so far overlooked by the great majority of computational models of visual attention. In this paper we present the ecological sampling model, a stochastic model of eye guidance explaining such variability. The gaze shift mechanism is conceived as an active random sampling that the foraging eye carries out upon the visual landscape, under the constraints set by the observable features and the global complexity of the landscape. By drawing on results reported in the foraging literature, the actual gaze relocation is eventually driven by a stochastic differential equation whose noise source is sampled from a mixture of α-stable distributions. This way, the sampling strategy proposed here allows to mimic a fundamental property of the eye guidance mechanism: where we choose to look next at any given moment in time, it is not completely deterministic, but neither is it completely random To show that the model yields gaze shift motor behaviors that exhibit statistics similar to those displayed by human observers, we compare simulation outputs with those obtained from eye-tracked subjects while viewing complex dynamic scenes.

Human actions have been widely studied for their potential application in various areas such as sports, pervasive patient monitoring, and rehabilitation. However, challenges still persist pertaining to determining the most useful ways to describe human actions at the sensor, then limb and complete action levels of representation and deriving important relations between these levels each involving their own atomic components. In this paper, we report on a motion encoder developed for the sensor level based on the need to distinguish between the shape of the sensor's trajectory and its temporal characteristics during execution. This distinction is critical as it provides a different encoding scheme than the usual velocity and acceleration measures which confound these two attributes of any motion. At the same time, we eliminate noise from sensors by comparing temporal and spatial indexing schemes and a number of optimal filtering models for robust encoding. Results demonstrate the benefits of spatial indexing and separating the shape and dynamics of a motion, as well as its ability to decompose complex motions into several atomic ones. Finally, we discuss how this specific type of sensor encoder bears on the derivation of limb and complete action descriptions.

In this paper, we discuss how image segmentation can be handled by using Bayesian learning and inference. In particular variational techniques relying on free energy minimization will be introduced. It will be shown how to embed a spatial diffusion process on segmentation labels within the Variational Bayes learning procedure so as to enforce spatial constraints among labels.

Multiresolution diffused expectation maximisation performs segmentation on vector (e.g. color) images within a multiscale framework; segmentation is carried out via the expectation maximisation algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels.

A framework for diffusion of color images is presented. The method is based on the theory of thermodynamics of irreversible transformations which provides a suitable basis for designing correlations between the different color channels. More precisely, we derive an equation for color evolution which comprises a purely spatial diffusive term and a nonlinear term that depends on the interactions among color channels over space. We apply the proposed equation to images represented in several color spaces, such as RGB, CIELAB, Opponent colors, and IHS.

The evolution of information in images undergoing fine-to-coarse anisotropic transformations is analyzed by using an approach based on the theory of irreversible transformations. In particular, we show that, when an anisotropic diffusion model is used, local variation of entropy production over space and scale provides the basis for a general method to extract relevant image features.

Loss of information in images undergoing fine-to-coarse image transformations is analyzed by using an approach based on the theory of irreversible transformations. The method is applied to isotropic and anisotropic diffusion and an algorithm based on entropy variation is presented that extracts relevant features of the image and provides a method to discriminate between smooth, textured and edge-type regions.

We generalize to colour images a method to calculate the local information content in an image. The method is based on the theory of thermodynamics of irreversible transformations and relies on fine-to-coarse transformations, such as diffusion processes. To this end, we discuss an extension to multi-valued images of isotropic diffusion, where cross-interactions between the different colour channels are allowed. Results achieved in simulation are presented and discussed.

Employing a thermodynamical model applied to scale-space transformations, we derive a method to encode different types of image features, as regions of different information content. Information is locally defined in terms of activity of the density of entropy production along scales. An activity map is formed, which represents the conspicuity or saliency of each point of the image. According to the different levels of activity, labelled as high, medium or low, we can extract basic parts of the image by collecting points of similar saliency.

This paper deals with a novel way for representing and computing image features encapsulated within different regions of scale-space. Employing a thermodynamical model for scale-space generation, the method derives features as those corresponding to "entropy rich" image regions where, within a given range of spatial scales, the entropy gradient remains constant. Different types of image features, defining regions of different information content, are accordingly encoded by such regions within different bands of spatial scale.

The notion of entropy production /spl Pscr/, as defined by the thermodynamics of irreversible transformations, is used to quantify information loss during a transformation from fine-to-coarse image representations. To this aim we first show the existence of a precise relation between /spl Pscr/ and the Kullback-Leibler distance. This relation allows one to gauge local and global information losses which are associated with the global and local information content information of the images. Connections between the proposed approach, segmentation issues, and classical information theory are discussed.

We propose a variant of shape from shading which we call shape from image warping. The idea is that the three-dimensional shape of an object is estimated by determining how much the image of the object is warped with respect to the image of a known prototype shape. We demonstrate that, for a class of reflectance functions, there is a direct relationship between these image warps and geometric warps of the underlying three-dimensional shapes. Therefore detecting the image warp relative to a prototype of known shape allows us to reconstruct the shape of the imaged object. We derive properties of these shape warps and illustrate the results by recovering the shapes effaces. This relationship between image and shape warps helps us understand the relationship between image based models of object recognition and approaches based on three-dimensional object models.