Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data.

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly-Supervised Detector of Relevant Anomalies from time series data.

The development of a manual-based digital memory notebook intervention with case study illustrations.

There is currently a need to identify feasible and effective interventions to help older individuals suffering from memory loss maintain functional independence and quality of life. To improve upon paper and pencil memory notebook interventions, the Digital Memory Notebook (DMN) application (app) was developed iteratively with persons with cognitive impairment. In this paper we detail a manual-based intervention for training use of the DMN app.

Creating a digital memory notebook application for individuals with mild cognitive impairment to support everyday functioning.

Memory impairment can necessitate use of external memory aids to preserve functional independence. As external aids can be difficult to learn and remember to use, technology may improve the efficacy of current rehabilitation strategies. We detail the iterative development of a digital application of a paper-and-pencil memory notebook.

SynSys: A Synthetic Data Generation System for Healthcare Applications.

Creation of realistic synthetic behavior-based sensor data is an important aspect of testing machine learning techniques for healthcare applications. Many of the existing approaches for generating synthetic data are often limited in terms of complexity and realism. We introduce SynSys, a machine learning-based synthetic data generation method, to improve upon these limitations.

Smart Secure Homes: A Survey of Smart Home Technologies that Sense, Assess, and Respond to Security Threats.

Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings.

Activity Learning as a Foundation for Security Monitoring in Smart Homes.

Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes.

An analysis of a digital variant of the Trail Making Test using machine learning techniques.

Background: The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility.

Objective: This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities.

Methods: Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features.

Multicomponent analysis of a digital Trail Making Test.

Objective: The purpose of the current study was to use a newly developed digital tablet-based variant of the TMT to isolate component cognitive processes underlying TMT performance.

Method: Similar to the paper-based trail making test, this digital variant consists of two conditions, Part A and Part B. However, this digital version automatically collects additional data to create component subtest scores to isolate cognitive abilities.