Optimizing medication reminders using a decision-theoretic framework.

We discuss a new approach to patients' adherence to enhance to their medication-taking regimen by developing a context-aware alerting system that would optimize the expected utility of alerts. Each patient's instantaneous context is assessed using a real-time sensor network deploying a variety of sensors. The alerts are generated to optimize the expected value to the patient. This paper is focused on the initial assessment of the utility of alerts, including the tradeoff between effectiveness and annoyance.

A study of medication-taking and unobtrusive, intelligent reminding.

Poor medication adherence is one of the major causes of illness and of treatment failure in the United States. The objective of this study was to conduct an initial evaluation of a context-aware reminder system, which generated reminders at an opportune time to take the medication. Ten participants aged 65 or older, living alone and managing their own medications, participated in the study. Participants took a low-dose vitamin C tablet twice daily at times that they specified. Participants were considered adherent if they took the vitamin within 90 minutes (before or after) of the prescribed time. Adherence and activity in the home was measured using a system of sensors, including an instrumented pillbox. There were three phases of the study: baseline, in which there was no prompting; time-based, in which there was prompting at the prescribed times for pill-taking; and context-aware, in which participants were only prompted if they forgot to take their pills and were likely able to take their pills. The context-based prompting resulted in significantly better adherence (92.3%) as compared to time-based (73.5%) or no prompting (68.1%) conditions (p < 0.0002, chi(2) = 17.0). In addition, subjects had better adherence in the morning than in the evening. We have shown in this study that a system that generates reminders at an opportune time to take the medication significantly improves adherence. This study indicates that context-aware prompting may provide improved adherence over standard time-based reminders.

RKHS Bayes discriminant: a subspace constrained nonlinear feature projection for signal detection.

Given the knowledge of class probability densities, a priori probabilities, and relative risk levels, Bayes classifier provides the optimal minimum-risk decision rule. Specifically, focusing on the two-class (detection) scenario, under certain symmetry assumptions, matched filters provide optimal results for the detection problem. Noticing that the Bayes classifier is in fact a nonlinear projection of the feature vector to a single-dimensional statistic, in this paper, we develop a smooth nonlinear projection filter constrained to the estimated span of class conditional distributions as does the Bayes classifier. The nonlinear projection filter is designed in a reproducing kernel Hilbert space leading to an analytical solution both for the filter and the optimal threshold. The proposed approach is tested on typical detection problems, such as neural spike detection or automatic target detection in synthetic aperture radar (SAR) imagery. Results are compared with linear and kernel discriminant analysis, as well as classification algorithms such as support vector machine, AdaBoost and LogitBoost.

Continuously differentiable sample-spacing entropy estimation.

The insufficiency of using only second-order statistics and premise of exploiting higher order statistics of the data has been well understood, and more advanced objectives including higher order statistics, especially those stemming from information theory, such as error entropy minimization, are now being studied and applied in many contexts of machine learning and signal processing. In the adaptive system training context, the main drawback of utilizing output error entropy as compared to correlation-estimation-based second-order statistics is the computational load of the entropy estimation, which is usually obtained via a plug-in kernel estimator. Sample-spacing estimates offer computationally inexpensive entropy estimators; however, resulting estimates are not differentiable, hence, not suitable for gradient-based adaptation. In this brief paper, we propose a nonparametric entropy estimator that captures the desirable properties of both approaches. The resulting estimator yields continuously differentiable estimates with a computational complexity at the order of those of the sample-spacing techniques. The proposed estimator is compared with the kernel density estimation (KDE)-based entropy estimator in the supervised neural network training framework with computation time and performance comparisons.

Towards fish-eye camera based in-home activity assessment.

Indoors localization, activity classification, and behavioral modeling are increasingly important for surveillance applications including independent living and remote health monitoring. In this paper, we study the suitability of fish-eye cameras (high-resolution CCD sensors with very-wide-angle lenses) for the purpose of monitoring people in indoors environments. The results indicate that these sensors are very useful for automatic activity monitoring and people tracking. We identify practical and mathematical problems related to information extraction from these video sequences and identify future directions to solve these issues.

Continuous activity monitoring and intelligent contextual prompting to improve medication adherence.

Poor medication adherence is a serious medical problem, particularly in older adults. Various solutions have been developed to remind people to take their medications, but these systems are usually simple time-based alarm systems that are not particularly effective. We describe a system that is context aware, and that utilizes information about past patterns of behavior plus the current context to provide prompts at the appropriate time and place. A case study from our initial deployment of the system to eleven older adults illustrates the possibilities and advantages of context aware prompting systems.

Nonparametric snakes.

Active contours, or so-called snakes, require some parameters to determine the form of the external force or to adjust the tradeoff between the internal forces and the external forces acting on the active contour. However, the optimal values of these parameters cannot be easily identified in a general sense. The usual way to find these required parameters is to run the algorithm several times for a different set of parameters, until a satisfactory performance is obtained. Our nonparametric formulation translates the problem of seeking these unknown parameters into the problem of seeking a good edge probability density estimate. Density estimation is a well-researched field, and our nonparametric formulation allows using well-known concepts of density estimation to get rid of the exhaustive parameter search. Indeed, with the use of kernel density estimation these parameters can be defined locally, whereas, in the original snake approach, all the shape parameters are defined globally. We tested the proposed method on synthetic and real images and obtained comparatively better results.