Topic of Special Issue: Distributed Diagnosis and Home Healthcare

Abstract

High-performance computing played a pivotal role in providing convincing solutions to area of healthcare. Signal and Image processing, computer modeling, medical instrumentation, healthcare informatics, communication, biocomputing, and data mining tools have been developed to aid the clinicians in effective diagnosis and treatment. Distributed Diagnosis and Home Healthcare (D2H2) is new area dealing with miniaturization, integration of instrumentation (wearable and implanted), electronic medicine, inexpensive point-of-care diagnostic systems, biosensors, biomaterials, data acquisition, medical imaging and computing. D2H2 continuously strives to improve the quality of patient care by transforming the delivery of healthcare from a hospital-oriented system to more distributed and home-based. This new approach will help to deliver healthcare facilities to patients anywhere in the world by integrating the information technology with engineering and medical sciences disciplines. This special issue has two major sections: Section one covers first nine papers and deals with biomedical devices and diagnosis. Section 2 covers the last seven papers and are dedicated to biomedical imaging and diagnosis. The efficacy of reflected-type green light photoplethysmography (green light PPG) was studied in Paper 1. Transmitted infrared light was used for PPG and the arterial pulse was monitored transcutaneously. The reflected PPG signal contains AC components based on the heartbeatrelated signal from the arterial blood flow and DC components, which include reflectance and scattering from tissue. Generally, changes in AC components are monitored, but the DC components play an important role during heat stress. In this study, authors have compared the signal of green light PPG to infrared PPG and ECG during heat stress. Their results show that, the pulse rates obtained from green light PPG were strongly correlated with the R-R interval of an electrocardiogram in all environments, but those obtained from infrared light PPG displayed a weaker correlation with cold exposure. Paper 2 deals with an algorithm to extract information from human walking, using Bayesian Network (BN) . Their results show that the BN extracted from different types of gait measurement data, muscle activities and joint trajectories, could reflect the difference between different walking: the normal walking, simulated hemiplegic walking and paralyzed walking. This approach can be useful for diagnostic, therapeutic, and rehabilitative applications. E. Y. K. Ng (*) School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50, Nanyang Avenue, Singapore, Singapore 639798 e-mail: mykng@ntu.edu.sg