Feasibility of a Secure Wireless Sensing Smartwatch Application for the Self-Management of Pediatric Asthma.

To address the need for asthma self-management in pediatrics, the authors present the feasibility of a mobile health (mHealth) platform built on their prior work in an asthmatic adult and child. Real-time asthma attack risk was assessed through physiological and environmental sensors. Data were sent to a cloud via a smartwatch application (app) using Health Insurance Portability and Accountability Act (HIPAA)-compliant cryptography and combined with online source data.

Computation offloading for real-time health-monitoring devices.

Among the major challenges in the development of real-time wearable health monitoring systems is to optimize battery life. One of the major techniques with which this objective can be achieved is computation offloading, in which portions of computation can be partitioned between the device and other resources such as a server or cloud. In this paper, we describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data between the wearable device and mobile application as a function of desired classification accuracy.

Dynamic Computation Offloading for Low-Power Wearable Health Monitoring Systems.

Objective: The objective of this paper is to describe and evaluate an algorithm to reduce power usage and increase battery lifetime for wearable health-monitoring devices.

Methods: We describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy.

Results: By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%.

HIPAA Compliant Wireless Sensing Smartwatch Application for the Self-Management of Pediatric Asthma.

Asthma is the most prevalent chronic disease among pediatrics, as it is the leading cause of student absenteeism and hospitalization for those under the age of 15. To address the significant need to manage this disease in children, the authors present a mobile health (mHealth) system that determines the risk of an asthma attack through physiological and environmental wireless sensors and representational state transfer application program interfaces (RESTful APIs). The data is sent from wireless sensors to a smartwatch application (app) via a Health Insurance Portability and Accountability Act (HIPAA) compliant cryptography framework, which then sends data to a cloud for real-time analytics.

A flexible data-driven comorbidity feature extraction framework.

Disease and symptom diagnostic codes are a valuable resource for classifying and predicting patient outcomes. In this paper, we propose a novel methodology for utilizing disease diagnostic information in a predictive machine learning framework. Our methodology relies on a novel, clustering-based feature extraction framework using disease diagnostic information.

A Hierarchical Classification and Segmentation Scheme for Processing Sensor Data.

Detecting short-duration events from continuous sensor signals is a significant challenge in the domain of wearable devices and health monitoring systems. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which can be individually processed using statistical classifiers or other algorithms. In this paper, we propose an algorithm for segmenting time-series signals and detecting short-duration data in the domain of lightweight embedded systems with real-time constraints.

Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support.

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies.