Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Validation of Wireless Sensors for Psychophysiological Studies.

ABSTRACT: James One (MindProber Labs) is a wireless psychophysiological device comprising two sensors: one measuring electrodermal activity (EDA), the other photoplethysmography (PPG). This paper reports the validation of James One's EDA sensor by comparing its signal against a research grade polygraph. Twenty participants were instructed to perform breathing exercises to elicit the modulation of EDA and heart rate, while the physiological signal was captured simultaneously on James One and a Biopac MP36. The resulting EDA and PPG records collected from both systems were comprehensively compared. Results suggest that James One captures EDA signal with a quality comparable to a research grade equipment, this constituting a reliable means of capturing data while minimizing setup time and intrusiveness.

SUBMITTER: Silva Moreira P

PROVIDER: S-EPMC6891453 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Validation of Wireless Sensors for Psychophysiological Studies.

Silva Moreira Pedro P Chaves Pedro P Dias Ruben R Dias Nuno N Almeida Pedro R PR

Sensors (Basel, Switzerland) 20191106 22

James One (MindProber Labs) is a wireless psychophysiological device comprising two sensors: one measuring electrodermal activity (EDA), the other photoplethysmography (PPG). This paper reports the validation of James One's EDA sensor by comparing its signal against a research grade polygraph. Twenty participants were instructed to perform breathing exercises to elicit the modulation of EDA and heart rate, while the physiological signal was captured simultaneously on James One and a Biopac MP36. ...[more]

PMID: 31698694

Similar Datasets

Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing.

Project description:Knowledge of the mental workload induced by a Web page is essential for improving users' browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of mental workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%.

| S-EPMC5855035 | biostudies-literature

Microscopic sensors using optical wireless integrated circuits.

Project description:We present a platform for parallel production of standalone, untethered electronic sensors that are truly microscopic, i.e., smaller than the resolution of the naked eye. This platform heterogeneously integrates silicon electronics and inorganic microlight emitting diodes (LEDs) into a 100-μm-scale package that is powered by and communicates with light. The devices are fabricated, packaged, and released in parallel using photolithographic techniques, resulting in ∼10,000 individual sensors per square inch. To illustrate their use, we show proof-of-concept measurements recording voltage, temperature, pressure, and conductivity in a variety of environments.

| S-EPMC7196798 | biostudies-literature

Wireless, skin-interfaced sensors for compression therapy.

Project description:Therapeutic compression garments (TCGs) are key tools for the management of a wide range of vascular lower extremity conditions. Proper use of TCGs involves application of a minimum and consistent pressure across the lower extremities for extended periods of time. Slight changes in the characteristics of the fabric and the mechanical properties of the tissues lead to requirements for frequent measurements and corresponding adjustments of the applied pressure. Existing sensors are not sufficiently small, thin, or flexible for practical use in this context, and they also demand cumbersome, hard-wired interfaces for data acquisition. Here, we introduce a flexible, wireless monitoring system for tracking both temperature and pressure at the interface between the skin and the TCGs. Detailed studies of the materials and engineering aspects of these devices, together with clinical pilot trials on a range of patients with different pathologies, establish the technical foundations and measurement capabilities.

| S-EPMC7821894 | biostudies-literature

Automating sleep stage classification using wireless, wearable sensors.

Project description:Polysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.

| S-EPMC6925191 | biostudies-literature

Soft Radio-Frequency Identification Sensors: Wireless Long-Range Strain Sensors Using Radio-Frequency Identification.

Project description:Increasing amounts of attention are being paid to the study of Soft Sensors and Soft Systems. Soft Robotic Systems require input from advances in the field of Soft Sensors. Soft sensors can help a soft robot to perceive and to act upon its immediate environment. The concept of integrating sensing capabilities into soft robotic systems is becoming increasingly important. One challenge is that most of the existing soft sensors have a requirement to be hardwired to power supplies or external data processing equipment. This requirement hinders the ability of a system designer to integrate soft sensors into soft robotic systems. In this article, we design, fabricate, and characterize a new soft sensor, which benefits from a combination of radio-frequency identification (RFID) tag design and microfluidic sensor fabrication technologies. We designed this sensor using the working principle of an RFID transporter antenna, but one whose resonant frequency changes in response to an applied strain. This new microfluidic sensor is intrinsically stretchable and can be reversibly strained. This sensor is a passive and wireless device, and as such, it does not require a power supply and is capable of transporting data without a wired connection. This strain sensor is best understood as an RFID tag antenna; it shows a resonant frequency change from approximately 860 to 800 MHz upon an applied strain change from 0% to 50%. Within the operating frequency, the sensor shows a standoff reading range of >7.5 m (at the resonant frequency). We characterize, experimentally, the electrical performance and the reliability of the fabrication process. We demonstrate a pneumatic soft robot that has four microfluidic sensors embedded in four of its legs, and we describe the implementation circuit to show that we can obtain movement information from the soft robot using our wireless soft sensors.

| S-EPMC6386780 | biostudies-literature

In Vivo Wireless Sensors for Gut Microbiome Redox Monitoring.

Project description:A perturbed gut microbiome has recently been linked with multiple disease processes, yet researchers currently lack tools that can provide in vivo, quantitative, and real-time insight into these processes and associated host-microbe interactions. We propose an in vivo wireless implant for monitoring gastrointestinal tract redox states using oxidation-reduction potentials (ORP). The implant is powered and conveniently interrogated via ultrasonic waves. We engineer the sensor electronics, electrodes, and encapsulation materials for robustness in vivo, and integrate them into an implant that endures autoclave sterilization and measures ORP for 12 days implanted in the cecum of a live rat. The presented implant platform paves the way for long-term experimental testing of biological hypotheses, offering new opportunities for understanding gut redox pathophysiology mechanisms, and facilitating translation to disease diagnosis and treatment applications.

| S-EPMC7170758 | biostudies-literature

A wireless "Janus" soft gripper with multiple tactile sensors.

Project description:Biomimetic properties allow soft robots to complexly interact with the environment. As the bridge between the robot and the operating object, the gripping hand is an important organ for its connection with the outside world, which requires the ability to provide feedback from the grasped object, similar to the human sensory and nervous system. In this work, to cope with the difficulty of integrating complex sensing and communication systems into flexible soft grippers, we propose a GO/PI composite bilayer film-based gripper with two types of tactile sensors and a LC passive wireless transmission module to obtain the grip information and transmit it to the processor. The bilayer film structure demonstrates good photothermal driving performance. Pressure and material sensors are located at the tips of the gripper's fingers to acquire tactile information which is wirelessly transmitted to the processor for analysis via the LC circuit. The grasping and feedback of the gripper are presented through an intelligent display system, realizing the wireless interconnection between the robot terminal and processing system, exhibiting broad application potential.

| S-EPMC9642356 | biostudies-literature

Conjunction of triboelectric nanogenerator with induction coils as wireless power sources and self-powered wireless sensors.

Project description:Here we demonstrate a magnetic resonance coupling based wireless triboelectric nanogenerator (TENG) and fully self-powered wireless sensors. By integrating a microswitch and an inductor with the TENG, the pulsed voltage output is converted into a sinusoidal voltage signal with a fixed frequency. This can be transmitted wirelessly from the transmit coil to the resonant-coupled receiver coil with an efficiency of 73% for a 5 cm distance between the two coils (10 cm diameter). Analytic models of the oscillating and coupled voltage signals for the wireless energy transfer are developed, showing excellent agreement with the experimental results. A TENG of 40 × 50 mm2 can wirelessly light up 70 LEDs or charge up a 15 μF capacitor to 12.5 V in ~90 s. The system is further utilized for two types of fully self-powered wireless chipless sensors with no microelectronic components. The technologies demonstrate an innovative strategy for a wireless 'green' power source and sensing.

| S-EPMC6940365 | biostudies-literature

Wireless, closed-loop, smart bandage with integrated sensors and stimulators for advanced wound care and accelerated healing

Project description:We quantify the effects of electrical stimulation on murine cells at the cellular and transcriptomic level using single cell RNA-sequencing. We characterize circulating cells and compare them to unstimulated circulating cells.

2022-10-14 | GSE215246 | GEO

Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors.

Project description:This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios and evaluate the results. Our study indicates that near perfect classification accuracies are achievable for small gestures and that the speed of classification is sufficient to allow interactivity. However, such accuracies are more difficult to obtain when a participant does not participate in training, indicating that more work needs to be done in this area to create a system that can be used by the general population.

| S-EPMC4883296 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data