Abstract
In this paper we present a management model to deal with the problem of tracking a large number of features during long image sequences. Some usual difficulties are related to this problem: features may be temporarily occluded or might even have disappeared definitively; the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This methodology may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.

Abstract

Abstract
In this paper we address the problem of tracking feature points along image sequences. To analyze the undergoing movement we use a common approach based on Kalman filtering which performs the estimation and correction of the feature point's movement in every image frame. The criterion proposed to establish correspondences, between the group of estimates in each image and the new data to include, minimizes the global matching cost based on the Mahalanobis distance. In this paper, along with the movement tracking, we use a management model which is able to deal with the occlusion and appearance of feature points and allows objects tracking in long sequences. We also present some experimental results obtained that validate our approach.

Abstract

Abstract
A new wearable data capture system for gait analysis is being developed. It consists of a pantyhose with embedded conductive yarns interconnecting customized sensing electronic devices that capture inertial and electromyographic signals and sends aggregated information to a personal computer through a wireless link. The use of conductive yarns to build the myoelectric electrodes and the interconnections of the wired sensors network, as well as the topology and functionality of the sensor modules are presented.

Abstract
BackgroundCognitive impairment is a critical aspect of our aging society. Yet, it receives inadequate intervention due to delayed or missed detection. Dual-task gait analysis is currently considered a solution to improve the early detection of cognitive impairment in clinical settings. Recently, our group proposed a new approach for the gait analysis resorting to inertial sensors placed on the shoes. This pilot study aimed to investigate the potential of this system to capture and differentiate gait performance in the presence of cognitive impairment based on single- and dual-task gait assessments.MethodsWe analyzed demographic and medical data, cognitive tests scores, physical tests scores, and gait metrics acquired from 29 older adults with mobility limitations. Gait metrics were extracted using the newly developed gait analysis approach and recorded in single- and dual-task conditions. Participants were stratified into two groups based on their Montreal Cognitive Assessment (MoCA) global cognitive scores. Statistical analysis was performed to assess differences between groups, discrimination ability, and association of gait metrics with cognitive performance.ResultsThe addition of the cognitive task influenced gait performance of both groups, but the effect was higher in the group with cognitive impairment. Multiple dual-task costs, dual-task variability, and dual-task asymmetry metrics presented significant differences between groups. Also, several of these metrics provided acceptable discrimination ability and had a significant association with MoCA scores. The dual-task effect on gait speed explained the highest percentage of the variance in MoCA scores. None of the single-task gait metrics presented significant differences between groups.ConclusionsOur preliminary results show that the newly developed gait analysis solution based on foot-worn inertial sensors is a pertinent tool to evaluate gait metrics affected by the cognitive status of older adults relying on single- and dual-task gait assessments. Further evaluation with a larger and more diverse group is required to establish system feasibility and reliability in clinical practice.