Model- based filtering for artifact and noise suppression with state estimation for electrodermal activity measurements in real time

2015 ◽  
Author(s):  
Christian Tronstad ◽  
Odd M. Staal ◽  
Steinar Saelid ◽  
Orjan G. Martinsen
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Noise Suppression ◽  
Electrodermal Activity ◽  
Model Based ◽  
Activity Measurements

scholarly journals Autonomous Ground Vehicle Lane-Keeping LPV Model-Based Control: Dual-Rate State Estimation and Comparison of Different Real-Time Control Strategies

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1531
Author(s):  
Julián M. Salt Ducajú ◽  
Julián J. Salt Llobregat ◽  
Ángel Cuenca ◽  
Masayoshi Tomizuka
Keyword(s):  
Computational Complexity ◽  
State Estimation ◽  
Real Time ◽  
Control Strategy ◽  
Kalman Filters ◽  
Internal State ◽  
Model Based Control ◽  
Lateral Dynamics ◽  
Model Based ◽  
Lane Keeping

In this contribution, we suggest two proposals to achieve fast, real-time lane-keeping control for Autonomous Ground Vehicles (AGVs). The goal of lane-keeping is to orient and keep the vehicle within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While nonlinear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some control laws such as Model Predictive Control (MPC) in real time. Therefore, our first proposal is to use a Linear Parameter Varying (LPV) model to describe the AGV’s lateral dynamics, as a trade-off between computational complexity and model accuracy. Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KFs) are usually needed for sensor fusion. Our second proposal is to use a Dual-Rate Extended Kalman Filter (DREFKF) to alleviate the cost of updating the internal state of the filter. To check the validity of our proposals, an LPV model-based control strategy is compared in simulations over a circuit path to another reduced computational complexity control strategy, the Inverse Kinematic Bicycle model (IKIBI), in the presence of process and measurement Gaussian noise. The LPV-MPC controller is shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it is seen that Dual-Rate Extended Kalman Filters (DREKFs) constitute an interesting tool for providing fast vehicle state estimation in an AGV lane-keeping application.

scholarly journals USE OF ELECTRODERMAL WRISTBANDS TO MEASURE STUDENTS' COGNITIVE ENGAGEMENT IN THE CLASSROOM

2021 ◽  
Author(s):  
Patrick Terriault ◽  
Anastassis Kozanitis ◽  
Patrice Farand
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Cognitive Engagement ◽  
Pilot Project ◽  
External Factors ◽  
The Other ◽  
Activity Measurements ◽  
Activity Sensors ◽  
Skin Conductivity

A pilot project was conducted to study the feasibility of using electrodermal activity sensors embedded in a watch-like device to measure skin conductivity in real time. In the field of education, it may be interesting to use this technology to assess the students' cognitive engagement in the classroom. A few volunteer students as well as the professor were wearing an Empatica E4 wristband during some class periods where different activities were organized such as lectures, workshops and exams. Monitoring several individuals simultaneously makes possible to compare the collected data among students and between the students and the professor. Also, since the activities were weekly repeated, it was possible to assess to which extent the observed patterns were similar from one group to the other. In brief, the collected data is very difficult to interpret, since some external factors seem to have a significant effect on the measurements. Indeed, discrepancies are observed in the data curves representing the students’ electrodermal activity. Also, the data generated by the professor is quite different from one group to the other, even if he repeated the exact same activities at two different times of the week.  It is suggested to improve the understanding of all the phenomena that could affect the electrodermal activity measurements before trying to draw conclusions related to the students’ cognitive engagement in the classroom.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

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