A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention.

Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly.

Scalp EEG-Based Pain Detection Using Convolutional Neural Network.

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN).

Wireless Multi-sensor Physio-Motion Measurement and Synchronization System and Method for HRI Research.

There is a strong demand for acquisition, processing and understanding of a variety of physiological and behavioral signals from the measurements in human-robot interface (HRI). However, multiple data streams from these measurements bring considerable challenges for their synchronizations, either for offline analysis or for online HRI applications, especially when the sensors are wirelessly connected, without synchronization mechanisms, such as a network-time-protocol. In this paper, we presented a full wireless multi-modality sensor system comprising biopotential measurements such as EEG, EMG and inertial parameter data of articulated body-limb motions.

Towards precise tracking of electric-mechanical cardiac time intervals through joint ECG and BCG sensing and signal processing.

Automatic tracking of intra-beat cardiac activities in ballistocardiogram (BCG) is a highly interesting yet technically challenging topic for cardiac monitoring, due to the signal's high susceptibility to various forms of distortions. In this paper, we aim to further investigate the BCG waveform detection from a signal processing and analysis viewpoint. We collect synchronized electrocardiography(ECG) and BCG recordings from four healthy human subjects using an in-house built multi-physiological monitoring device.

Monitoring respiration and cardiac activity during sleep using microbend fiber sensor: A clinical study and new algorithm.

We report results from a clinical trial for monitoring respiration and cardiac activity of patients during sleep using microbend fiber sensor. This sensor is used to acquire respiratory and heart beat information. We have collected reference data from standard Polysomnography and data from microbend fiber sensor on 22 patients.

Simultaneous measurement of breathing rate and heart rate using a microbend multimode fiber optic sensor.

We propose and demonstrate the feasibility of using a highly sensitive microbend multimode fiber optic sensor for simultaneous measurement of breathing rate (BR) and heart rate (HR). The sensing system consists of a transceiver, microbend multimode fiber, and a computer. The transceiver is comprised of an optical transmitter, an optical receiver, and circuits for data communication with the computer via Bluetooth.

Noninvasive monitoring of blood pressure using optical Ballistocardiography and Photoplethysmograph approaches.

A new all optical method for long term and continuous blood pressure measurement and monitoring without using cuffs is proposed by using Ballistocardiography (BCG) and Photoplethysmograph (PPG). Based on BCG signal and PPG signal, a time delay between these two signals is obtained to calculate both systolic blood pressure and diastolic blood pressure via linear regression analysis. The fabricated noninvasive blood pressure monitoring device consists of a fiber sensor mat to measure BCG signal and a SpO2 sensor to measure PPG signal.

Intensity-modulated microbend fiber optic sensor for respiratory monitoring and gating during MRI.

This paper describes a novel microbend fiber optic sensor system for respiratory monitoring and respiratory gating in the MRI environment. The system enables the noninvasive real-time monitoring and measurement of breathing rate and respiratory/body movement pattern of healthy subjects inside the MRI gantry, and has potential application in respiratory-gated image acquisition based on respiratory cues. The working principle behind this sensor is based on the microbending effect of an optical fiber on light transmission.