Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review
This paper provides a systematic review of current techniques for quantitative gait analysis and proposes key metrics for for qualifying gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications
Special Issue: IEEE EMBC 2015
This special issue of the IEEE JOURNAL ON BIOMEDICAL AND HEALTH INFORMATICS (JBHI) presents full articles of the work presented at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), which was held in Milano, Italy, from August 25–29.
Special Issue: IEEE BSN 2015
This special issue includes four papers that expand on work presented at the 12th BSN Conference, held in Cambridge, MA, USA, in June 2015, which included 85 oral and poster presentations. All four papers went through the rigorous review process of the IEEE Journal of Biomedical and Health Informatics (J-BHI).
Special Section: MobiHealth 2014, IEEE HealthCom 2014, and IEEE BHI 2014
The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients,
security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.
Classification of Voluntary Cough Airflow Patterns for Prediction of Abnormal Spirometry
In this work, we investigate the feasibility of predicting abnormal spirometry using measurements of the partial flow-volume curve generated during a voluntary cough. A variety of features were extracted from the cough airflow signals and fed to a support vector machine (SVM) classifier. Airflow features and SVM parameters were selected using a genetic algorithm, and accuracy assessed using repeated double cross-validation. This technique is easily performed and does not require expensive equipment. Based on these characteristics, and the results of this study, it has potential for becoming especially useful for mass screening or for testing subjects who are not able to perform conventional pulmonary function testing.
DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis
Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process that abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model that integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease. More specifically, we demonstrate the derivation of TAs from data, which are used for building the network structure. We use machine learning algorithms to learn the parameters of the model through data. We apply the extended model to a longitudinal medical dataset and compare its performance to the performance of a DBN implemented without TAs. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.