Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms

This article covers radar signal processing for sensing in the context of assisted living (AL). This is presented through three example applications: human activity recognition (HAR) for activities of daily living (ADL), respiratory disorders, and sleep stages (SSs) classification. The common challenge of classification is discussed within a framework of measurements/preprocessing, feature extraction, and classification algorithms for supervised learning. Then, the specific challenges of the three applications from a signal processing standpoint are detailed in their specific data processing and ad hoc classification strategies. Here, the focus is on recent trends in the field of activity recognition (multidomain, multimodal, and fusion), health-care applications based on vital signs (superresolution techniques), and comments related to outstanding challenges. Finally, this article explores challenges associated with the real-time implementation of signal processing/classification algorithms

The International Classification of Functioning, Disability and Health: Contemporary Literature Overview

This article reviews the literature from the 3 years since the International Classification of Functioning, Disability and Health\u27s (ICF\u27s) endorsement, focusing on those articles that discuss (a) what the ICF means and how it can be used; (b) the general utility of the ICF for specific fields, such as nursing, occupational therapy, speech-language pathology, and audiology; (c) examples of applications for classification in particular disorders, such as chronic health conditions, neuromusculoskeletal conditions, cognitive disorders, mental disorders, sensory disorders, and primary and secondary conditions in children; (d) uses of the ICF to recode prior work across multiple surveys and across country coding schemes on disability-related national survey items; and (e) governmental uses of the ICF in the United States and selected countries abroad. Future directions needed to effectively implement the ICF across rehabilitation policy, research, and practice are discussed. Our review suggests that the actual application of the ICF is as yet somewhat limited because the World Health Organization (WHO) endorsement is so recent; the earliest references using the ICF correspond with the WHO\u27s 2001 endorsement. Standardized application of the ICF in North America has yet to be realized in anticipation of the release of the clinical implementation manual (see Reed et al., 2005); thus, it is not surprising to find limited research on clinical implementation of the ICF. From our review of the literature and of unpublished reports, it seems clear that the ICF is being used in a preliminary fashion to inform conceptual frameworks in research and for recoding data from other health classifications. Recently completed and ongoing research has undoubtedly not yet been published

Real-time food intake classification and energy expenditure estimation on a mobile device

© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment

Finding Order in Complexity: A Typology of Local Public Health Delivery Systems

Public health decision-makers and researchers currently lack an evidence-based framework for describing, classifying, and comparing public health delivery systems based on their organizational components, operational characteristics, and division of responsibility. Related typologies developed in the health services sector have proven extremely valuable for policy and administrative decision-making as well as for ongoing research. Performance assessment, quality improvement, and accreditation activities are now blossoming in public health—adding urgency to the need for classification and comparison frameworks. This brief describes a newly-developed empirical typology for local public health systems and highlights its policy and managerial applications

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.Siirretty Doriast

Creating a Classification Module to Analysis the Usage of Mobile Health Apps

With an ageing society becoming a major issue for many countries, health-related concerns are growing and mobile health applications (MHAs) are rapidly gaining users. The applications available range from those that promote exercise to maintain health, those that help to manage physical condition by recording weight and activity, and those that allow users to consult doctors and pharmacists. On the other hand, there are still many mobile users who do not use MHAs. In this case study from Japan, the range of diverse MHAs were classified into five categories by K-means clustering analysis and the results of a questionnaire on the use of MHAs were analyzed using a scientific approach to find out which types of users mainly use these applications. Based on the results of this analysis, a classifier was created using a Random Forest algorithm to extract MHAs that meet the needs of users based on their attributes and thoughts. With this Random Forest classification model, this paper recommends appropriate models for potential users who are not yet using MHAs