Articles and Recent News
Intuitive Clinician Control Interface for a Powered Knee-Ankle Prosthesis: A Case Study
This paper presents a potential solution to the challenge of configuring powered knee-ankle prostheses in a clinical setting. Typically, powered prostheses use impedance-based control schemes that contain several independent controllers which correspond to consecutive periods along the gait cycle.
An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data
Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena.
Sleep Apnea Syndrome Sensing at C-Band
A non-intrusive sleep apnea detection system using a C-Band channel sensing technique is proposed to monitor sleep apnea syndrome in real time. The system utilizes perturbations of RF signals to differentiate between patient’s breathing under normal and sleep apnea conditions.
An AI-based Heart Failure Treatment Adviser System
Management of heart failure is a major health care challenge. Healthcare providers are expected to use best practices described in clinical practice guidelines, which typically consist of a long series of complex rules.
Virtual Neonatal Echocardiographic Training System (VNETS): An Echocardiographic Simulator for Training Basic Transthoracic Echocardiography Skills in Neonates and Infants
There is a great need for training in pediatric echocardiography. In addition to physicians being trained in pediatric cardiology and echocardiography technologists, neonatologist, pediatric intensivists, and other health care professionals may be interested in such training. Since, there is limited opportunity of training on live patients, echocardiographic simulators may be of help.
Reaction Time Predicts Brain-Computer Interface Aptitude
There is evidence that 15–30% of the general population cannot effectively operate brain–computer interfaces (BCIs). Thus the BCI performance predictors are critically required to pre-screen participants. Current neurophysiological and psychological tests either require complicated equipment or suffer from subjectivity.