A Data-driven Functional PCA Filter for Compensating the Effect of Sensor Position Changes in Motion Data

2012 ◽  
Author(s):  
Roya Haratian ◽  
Chris Phillips ◽  
Tijana Timotijevic
Keyword(s):  
Data Driven ◽  
Motion Data ◽  
Sensor Position ◽  
Functional Pca

scholarly journals Motion-Data Driven Grasp/Assembly Planner

2019 ◽  
Vol 24 ◽  
pp. 1-4 ◽  
Author(s):  
Kensuke Harada ◽  
Natsuki Yamanobe ◽  
Weiwei Wan ◽  
Kazuyuki Nagata ◽  
Ixchel G. Ramirez-Alpizar ◽  
...  
Keyword(s):  
Data Driven ◽  
Motion Data

Exploring the Dimensionality of Ground-Motion Data by Applying Autoencoder Techniques

2021 ◽  
Author(s):  
Reza Dokht Dolatabadi Esfahani ◽  
Kristin Vogel ◽  
Fabrice Cotton ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
...  
Keyword(s):  
Ground Motion ◽  
Reconstruction Error ◽  
Variable Number ◽  
Data Driven ◽  
Data Reconstruction ◽  
Motion Model ◽  
Distance Information ◽  
Motion Data ◽  
Ground Motion Model ◽  
Minimum Number

ABSTRACT In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.

A Link between Machine Learning and Optimization in Ground-Motion Model Development: Weighted Mixed-Effects Regression with Data-Driven Probabilistic Earthquake Classification

2020 ◽  
Vol 110 (6) ◽  
pp. 2777-2800
Author(s):  
Sebastian von Specht ◽  
Fabrice Cotton
Keyword(s):  
Ground Motion ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Data Driven ◽  
Weighted Likelihood ◽  
Motion Data ◽  
Motion Models ◽  
Uncertainty Estimates ◽  
Data Weighting ◽  
Ground Motion Models

ABSTRACT The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation–maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastías and Montalva (2016) with ∼2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.

Geomorphological hazards assessment using machine learning and data fusion

2020 ◽  
Author(s):  
Mohammad Ahmed ◽  
Hamed Farhadi ◽  
Panagiotis Michalis ◽  
Manousos Valyrakis
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Turbulent Flow ◽  
Turbulent Flows ◽  
Fuzzy Inference ◽  
Data Driven ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Flume Experiments ◽  
Motion Data

<p>Turbulent flows may destabilise riverbeds and banks, transporting sediment or underscouring hydraulic infrastructure built near water bodies. For example, scour is a significant challenge that can affect the stability of bridge foundations as the transport of sediment around a bridge pier may cause structural instabilities and catastrophic failures. The aim of this study is to use machine learning techniques & data driven algorithms to predict how energetic turbulent flow events can result in the removal of individual sediment grains, resting on the bed surface or on the protective armour layer around built infrastructure. </p><p>The flume experiments involve flow and particle motion data gathering campaigns [1]. Turbulent flow data are collected upstream the exposed target particle using acoustic Doppler velocimetry. Particle's motion data are gathered using novel micro-electro-mechanical sensors embedded within its waterproof casing, for a range of flow conditions. The obtained data are fed into neural networks having distinct algorithmic complexity (inputs, levels and neutrons). A comparison of the performance of the various model architectures, as well as with past ones [2], is conducted to identify the optimal predictive algorithm for the configuration tested. Sensor data fusion combined with artificial intelligence techniques are shown to provide a unique tool for live and robust data-driven predictions to help tackle significant engineering problems, such as geomorphological activity and scouring of infrastructure (eg bridge piers and embankments) due to turbulent flows, which become increasingly more challenging, under the scope of climate change and intensifying extreme weather hazards.</p><p> </p><p>References</p><p>[1] Valyrakis, M., Farhadi, H. 2017. Investigating coarse sediment particles transport using PTV and “smart-pebbles” instrumented with inertial sensors, EGU General Assembly 2017, Vienna, Austria, 23-28 April 2017, id. 9980.</p><p>[2] Valyrakis, M., Diplas, P., Dancey, C.L. 2011b. Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25 (22). pp. 3513-3524. ISSN 0885-6087, doi:10.1002/hyp.8228.</p>

scholarly journals A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1499
Author(s):  
Yanran Jiang ◽  
Vincent Hernandez ◽  
Gentiane Venture ◽  
Dana Kulić ◽  
Bernard K. Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Center Of Pressure ◽  
Continuous Process ◽  
Force Plate ◽  
Human Motion ◽  
Data Driven ◽  
General Nature ◽  
Motion Data ◽  
Unseen Data

Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland–Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue.

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