scholarly journals “Understanding Cortical Arousals during Sleep from Leg Movements: A Pilot Study.”

2021 ◽  
Author(s):  
Kanika Bansal ◽  
Javier Garcia ◽  
Cody Feltch ◽  
Christopher Earley ◽  
Ryan Robucci ◽  
...  
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Connectivity Pattern ◽  
Measurement Unit ◽  
Graph Theoretic ◽  
Displacement Sensors ◽  
Health And Disease ◽  
Leg Movement ◽  
Inertial Measurements ◽  
Leg Movements

Leg movements during sleep occur in patients with sleep pathology and healthy individuals.  Some (but not all) leg movements during sleep are related to cortical arousals which occur without conscious awareness of the patient but have a significant effect of sleep fragmentation.  Detecting leg movements during sleep that are associated with cortical arousals can provide unique insight into the nature and quality of sleep in both health and disease.  In this study, a novel leg movement monitor is used in conjunction with polysomnography to better understand the relationship between leg movement and electroencephalogram (EEG) defined cortical arousals.  In an approach that we call neuro-extremity analysis, graph theoretic, directed connectivity metrics are used to interrogate the causal links between neural activity measured by EEG and leg movements measured by the sensors within the leg movement monitor.  The leg movement monitor in this study utilizes novel capacitive displacement sensors, and a 9-axis inertial measurement unit to characterize leg and foot movements.  First, the capacitive displacement measures more closely related to EEG-defined cortical arousals than inertial measurements.  Second, the neuro-extremity analysis reveals a temporally evolving connectivity pattern that is consistent with a model of cortical arousals in which brainstem dysfunction leads to near-instantaneous leg movements and a delayed, filtered signal to the cortex.  Neuro-extremity analysis reveals causal relationships between EEG and leg movement sensor time-series data that may aid researchers to better understand the pathophysiology of cortical arousals associated with leg movements during sleep.

scholarly journals Coordination Pattern of the Thigh, Pelvic, and Lumbar Movements during the Gait of Patients with Hip Osteoarthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Takuya Ibara ◽  
Masaya Anan ◽  
Ryosuke Karashima ◽  
Kiyotaka Hada ◽  
Koichi Shinkoda ◽  
...  
Keyword(s):  
Time Series Data ◽  
Hip Osteoarthritis ◽  
Series Data ◽  
Measurement Unit ◽  
Control Group ◽  
Coordination Pattern ◽  
Rotational Movement ◽  
Vector Coding ◽  
Lateral Tilt ◽  
Angular Velocities

There are limited reports on segment movement and their coordination pattern during gait in patients with hip osteoarthritis. To avoid the excessive stress toward the hip and relevant joints, it is important to investigate the coordination pattern between these segment movements, focusing on the time series data. This study aimed to quantify the coordination pattern of lumbar, pelvic, and thigh movements during gait in patients with hip osteoarthritis and in a control group. An inertial measurement unit was used to measure the lumbar, pelvic, and thigh angular velocities during gait of 11 patients with hip osteoarthritis and 11 controls. The vector coding technique was applied, and the coupling angle and the appearance rate of coordination pattern in each direction were calculated and compared with the control group. Compared with the control group, with respect to the lumbar/pelvic segment movements, the patients with hip osteoarthritis spent more rates in anti-phase and lower rates in in-phase lateral tilt movement. With respect to the pelvic/thigh segment movements, the patients with hip osteoarthritis spent more rates within the proximal- and in-phases for lateral tilt movement. Furthermore, patients with osteoarthritis spent lower rates in the distal-phase for anterior/posterior tilt and rotational movement. Patients with hip osteoarthritis could not move their pelvic and thigh segments separately, which indicates the stiffness of the hip joint. The rotational movement and lateral tilt movements, especially, were limited, which is known as Duchenne limp. To maintain the gait ability, it seems important to pay attention to these directional movements.

scholarly journals Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7628
Author(s):  
Yeon-Wook Kim ◽  
Kyung-Lim Joa ◽  
Han-Young Jeong ◽  
Sangmin Lee
Keyword(s):  
Time Series Data ◽  
Learning Algorithm ◽  
Sampling Rate ◽  
Model Performance ◽  
Series Data ◽  
Measurement Unit ◽  
Clinical Test ◽  
Ensemble Model ◽  
Balance Assessment ◽  
Optimal Ensemble

In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

scholarly journals MDITRE: scalable and interpretable machine learning for predicting host status from temporal microbiome dynamics

2021 ◽  
Author(s):  
Venkata Suhas Maringanti ◽  
Vanni Bucci ◽  
Georg K Gerber
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Technologies ◽  
Series Data ◽  
Open Source Software Package ◽  
Host Status ◽  
Health And Disease ◽  
Almost All ◽  
Changes Over Time ◽  
Microbiome Data

Longitudinal microbiome datasets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. Yet, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, MDITRE, which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing datasets, we demonstrate that in almost all cases, MDITRE performs on par or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through use cases can readily derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes.

scholarly journals Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Hee-Un Kim ◽  
Tae-Suk Bae
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Time Series Data ◽  
Short Term Memory ◽  
Advanced Technology ◽  
Series Data ◽  
Measurement Unit ◽  
Vehicle Navigation ◽  
Real Time Kinematic ◽  
Autonomous Vehicle Navigation

Much navigation over the last several decades has been aided by the global navigation satellite system (GNSS). In addition, with the advent of the multi-GNSS era, more and more satellites are available for navigation purposes. However, the navigation is generally carried out by point positioning based on the pseudoranges. The real-time kinematic (RTK) and the advanced technology, namely, the network RTK (NRTK), were introduced for better positioning and navigation. Further improved navigation was also investigated by combining other sensors such as the inertial measurement unit (IMU). On the other hand, a deep learning technique has been recently evolving in many fields, including automatic navigation of the vehicles. This is because deep learning combines various sensors without complicated analytical modeling of each individual sensor. In this study, we structured the multilayer recurrent neural networks (RNN) to improve the accuracy and the stability of the GNSS absolute solutions for the autonomous vehicle navigation. Specifically, the long short-term memory (LSTM) is an especially useful algorithm for time series data such as navigation with moderate speed of platforms. From an experiment conducted in a testing area, the LSTM algorithm developed the positioning accuracy by about 40% compared to GNSS-only navigation without any external bias information. Once the bias is taken care of, the accuracy will significantly be improved up to 8 times better than the GNSS absolute positioning results. The bias terms of the solution need to be estimated within the model by optimizing the layers as well as the nodes each layer, which should be done in further research.

scholarly journals Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 151
Author(s):  
Harold R. Chamorro ◽  
Alvaro D. Orjuela-Cañón ◽  
David Ganger ◽  
Mattias Persson ◽  
Francisco Gonzalez-Longatt ◽  
...  
Keyword(s):  
Power Systems ◽  
Time Series Data ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Test System ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Measurement Unit ◽  
System Frequency

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

scholarly journals Drill bit state-oriented drilling process classification with time-series data for wheeled drilling rigs

2021 ◽  
Vol 942 (1) ◽  
pp. 012010
Author(s):  
Bartłomiej Ziętek ◽  
Jacek Wodecki ◽  
Anna Michalak ◽  
Pawel Śliwiński
Keyword(s):  
Time Series Data ◽  
Pressure Level ◽  
Series Data ◽  
Measurement Unit ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drill Bit ◽  
Feed Pressure ◽  
Drilling Rigs ◽  
Process Classification

Abstract This paper represents an analysis of the wheeled drilling rig’s drilling process. Thanks to data from the onboard measurement unit of the machine, the characteristics of the drilling process regarding state of the drill bit are identified and calculated. The aim of the work is to provide a comparison between different drill qualities and process classification using Threshold-based segmentation with feed pressure levels and duration of single hole drilling. Second methodology is hierarchical clustering to create cluster analysis. Thanks to these approaches, it is possible to detect the time when the drill bit should be changed. The obtained results state that the average drill time for a new drill bit is shorter approximately by 50% than for the worn-out bit in terms of average drilling duration. Moreover, these changes are visible in the subsystem pressure level of the machine under specific drilling regimes.

scholarly journals A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6541
Author(s):  
So-Hyeon Jo ◽  
Joo Woo ◽  
Gi-Sig Byun ◽  
Baek-Soon Kwon ◽  
Jae-Hoon Jeong
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Occurrence Rate ◽  
Series Data ◽  
Measurement Unit ◽  
Judgment Accuracy ◽  
Personal Mobility ◽  
Long Short Term Memory ◽  
Mobility Device

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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