scholarly journals Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded or Decoded in Single-Trial Oscillatory Brain Waves

2018 ◽  
Author(s):  
Takaaki Sato ◽  
Riichi Kajiwara ◽  
Ichiro Takashima ◽  
Toshio Iijima
Keyword(s):  
Correlation Analysis ◽  
Real Time ◽  
Single Trial ◽  
Brain Waves ◽  
Time Estimates

scholarly journals Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1804
Author(s):  
Dimitar Stanev ◽  
Konstantinos Filip ◽  
Dimitrios Bitzas ◽  
Sokratis Zouras ◽  
Georgios Giarmatzis ◽  
...  
Keyword(s):  
Real Time ◽  
Small Error ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Time Estimates ◽  
Musculoskeletal Models ◽  
Open Source Framework ◽  
Measurement Units ◽  
Technical Details ◽  
Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

Estimation of Truck Traffic Volume from Single Loop Detectors with Lane-to-Lane Speed Correlation

2003 ◽  
Vol 1856 (1) ◽  
pp. 106-117 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Pravin Varaiya ◽  
Alexander Skabardonis
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
The United States ◽  
Truck Traffic ◽  
Single Loop ◽  
Loop Detectors ◽  
Time Estimates ◽  
Surveillance Technology ◽  
Traffic Volumes ◽  
Urban Freeway

An algorithm for real-time estimation of truck traffic in multilane freeways was proposed. The algorithm used data from single loop detectors—the most widely installed surveillance technology for urban freeways in the United States. The algorithm worked for those freeway locations that have a truck-free lane and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produced real-time estimates of the truck traffic volumes at the location. It also can be used to produce alternative estimates of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm was tested with real freeway data and produced estimates of truck traffic volumes with only 5.7% error. It also captured the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on Interstate 710 near Long Beach, California, during the dockworkers’ lockout October 1 to 9, 2002, the algorithm found a 32% reduction in five-axle truck volume.

Adapting Collision Warnings to Real-Time Estimates of Driver Distraction

2008 ◽  
pp. 501-518 ◽  
Author(s):  
Matthew Smith ◽  
Gerald Witt ◽  
Debbie Bakowski ◽  
Dave Leblanc ◽  
John Lee
Keyword(s):  
Real Time ◽  
Driver Distraction ◽  
Time Estimates

scholarly journals Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-based Solutions in Rehabilitation

2021 ◽  
Author(s):  
Dimitar Stanev ◽  
Konstantinos Filip ◽  
Dimitrios Bitzas ◽  
Sokratis Zouras ◽  
Georgios Giarmatzis ◽  
...  
Keyword(s):  
Real Time ◽  
Small Error ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Time Estimates ◽  
Musculoskeletal Models ◽  
Open Source Framework ◽  
Measurement Units ◽  
Technical Details ◽  
Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim's offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

scholarly journals Learning In Spike Trains: Estimating Within-Session Changes In Firing Rate Using Weighted Interpolation

2016 ◽  
Author(s):  
Greg Jensen ◽  
Fabian Muñoz ◽  
Vincent P. Ferrera
Keyword(s):  
Real Time ◽  
Electrophysiological Study ◽  
Monte Carlo Algorithm ◽  
Small Samples ◽  
Weighted Interpolation ◽  
Time Dynamics ◽  
Firing Rates ◽  
The Past ◽  
Time Estimates ◽  
Quantitative Procedure

AbstractThe electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.

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