Slow-Time Changes in Human EMG Muscle Fatigue States Are Fully Represented in Movement Kinematics

2008 ◽  
Vol 131 (2) ◽  
Author(s):  
Miao Song ◽  
David B. Segala ◽  
Jonathan B. Dingwell ◽  
David Chelidze
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Muscle Fatigue ◽  
Time Series Data ◽  
Series Data ◽  
Dimensional Manifold ◽  
Movement Kinematics ◽  
Individual Muscle ◽  
Time Changes ◽  
Low Dimensional

The ability to identify physiologic fatigue and related changes in kinematics can provide an important tool for diagnosing fatigue-related injuries. This study examined an exhaustive cycling task to demonstrate how changes in movement kinematics and variability reflect underlying changes in local muscle states. Motion kinematics data were used to construct fatigue features. Their multivariate analysis, based on smooth orthogonal decomposition, was used to reconstruct physiological fatigue. Two different features composed of (1) standard statistical metrics (SSM), which were a collection of standard long-time measures, and (2) phase space warping (PSW)–based metrics, which characterized short-time variations in the phase space trajectories, were considered. Movement kinematics and surface electromyography (EMG) signals were measured from the lower extremities of seven highly trained cyclists as they cycled to voluntary exhaustion on a stationary bicycle. Mean and median frequencies from the EMG time series were computed to measure the local fatigue dynamics of individual muscles independent of the SSM- and PSW-based features, which were extracted solely from the kinematics data. A nonlinear analysis of kinematic features was shown to be essential for capturing full multidimensional fatigue dynamics. A four-dimensional fatigue manifold identified using a nonlinear PSW-based analysis of kinematics data was shown to adequately predict all EMG-based individual muscle fatigue trends. While SSM-based analyses showed similar dominant global fatigue trends, they failed to capture individual muscle activities in a low-dimensional manifold. Therefore, the nonlinear PSW-based analysis of strictly kinematic time series data directly predicted all of the local muscle fatigue trends in a low-dimensional systemic fatigue trajectory. These results provide the first direct quantitative link between changes in muscle fatigue dynamics and resulting changes in movement kinematics.

Information-Theoretic Characterization of Bifurcations in Nonlinear Dynamical Systems With Noise

2013 ◽  
Author(s):  
Navendu S. Patil ◽  
Joseph P. Cusumano
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Nonlinear Dynamical Systems ◽  
Period Doubling ◽  
Entropy Rate ◽  
Series Data ◽  
High Dimensional ◽  
Dimensional System ◽  
Dimensional Manifold ◽  
Low Dimensional

Detecting bifurcations in noisy and/or high-dimensional physical systems is an important problem in nonlinear dynamics. Near bifurcations, the dynamics of even a high dimensional system is typically dominated by its behavior on a low dimensional manifold. Since the system is sensitive to perturbations near bifurcations, they can be detected by looking at the apparent deterministic structure generated by the interaction between the noise and low-dimensional dynamics. We use minimal hidden Markov models built from the noisy time series to quantify this deterministic structure at the period-doubling bifurcations in the two-well forced Duffing oscillator perturbed by noise. The apparent randomness in the system is characterized using the entropy rate of the discrete stochastic process generated by partitioning time series data. We show that as the bifurcation parameter is varied, sharp changes in the statistical complexity and the entropy rate can be used to locate incipient bifurcations.

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1343 ◽  
Author(s):  
Robin A. Choudhury ◽  
Neil McRoberts
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Complex Dynamics ◽  
Cost Effective ◽  
Series Data ◽  
Spore Trap ◽  
Abundance Data ◽  
Airborne Inoculum ◽  
Pathogen Dna ◽  
Low Dimensional

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.

scholarly journals Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

2022 ◽  
Vol 3 (1) ◽  
pp. 1-26
Author(s):  
Omid Hajihassani ◽  
Omid Ardakanian ◽  
Hamzeh Khazaei
Keyword(s):  
Time Series ◽  
Linear Transformation ◽  
Input Data ◽  
Time Series Data ◽  
Representation Learning ◽  
Sensor Data ◽  
Series Data ◽  
Sensitive Information ◽  
Latent Space ◽  
Low Dimensional

The abundance of data collected by sensors in Internet of Things devices and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this article, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.

scholarly journals Brief Communication: Breeding vectors in the phase space reconstructed from time series data

2015 ◽  
Vol 2 (4) ◽  
pp. 1301-1315
Author(s):  
E. Lynch ◽  
D. Kaufman ◽  
A. S. Sharma ◽  
E. Kalnay ◽  
K. Ide
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Evolution Equations ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Series Data ◽  
Nonlinear Instability ◽  
Bred Vectors ◽  
Dynamical Properties ◽  
A New Technique

Abstract. Bred vectors characterize the nonlinear instability of dynamical systems and so far have been computed only for systems with known evolution equations. In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the standard and nearest-neighbor breeding are shown to be similar, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

Phase space reconstruction using input-output time series data

1999 ◽  
Vol 60 (4) ◽  
pp. 4008-4013 ◽  
Author(s):  
David M. Walker ◽  
Nicholas B. Tufillaro
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Input Output ◽  
Output Time

scholarly journals A Case Study on Hospital Readmission Prediction Using Deep Learning Algorithms on EHRs

2021 ◽  
Vol 12 (3) ◽  
pp. 4267-4273
Author(s):  
B. Sushrith Et.al
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Risk ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Series Data ◽  
Critical Areas ◽  
Avoidable Readmission ◽  
Low Dimensional ◽  
Medical Concepts

In this paper, focus is made on predicting the patients who are going to be re-admitted back in the hospital before discharge using latest deep-learning algorithms is applied on the electronic health records of patients which is a time-series data. To begin with the study of the data and its analysis this project deployed the conventional supervised ML algorithms like the Logistic Regression, Naïve Bayes, Random Forest and SVM and compared their performances on different portion sizes of dataset. The final model built uses deep-learning architectures such as RNN and LSTM to improve the prediction results taking advantage of the time series data. Another feature added has been of low dimensional descriptions of medical concepts as the input to the model. Ultimately, this work tests, validates, and explains the developed system using the MIMIC-III dataset, which contains around 38000 patient’s information and about 61,155 patient’s data who admitted in ICU, duration of 10 years. The support from this exhaustive dataset is used to train the models that provide healthcare workers with proper information regarding their discharge and readmission in hospitals. These ML and deep learning models are used to know about the patient who is getting to be readmitted in the ICU before his discharge will help the hospital to allocate resources properly and also reduce the financial risk of patients. In order to reduce ICU readmission that can be avoided, hospitals have to be able to recognize patients who have a higher risk of ICU readmission. Those patients can then continue to stay in the ICU so that they will not have the risk of getting admit back to the hospital. Also, the resources of hospitals that were required for avoidable readmission can be re-allocated to more critical areas in the hospital that need them. A more effective model of predicting readmission system can play an important role in helping hospitals and ICU doctors to find the patients who are going to be readmitted before discharge. To build this system here we use different ML and deep-learning algorithms. Predictive models based on huge amounts of data are made to predict the patients who are going to be admitted back in the hospital after discharge.

scholarly journals Low-dimensional Convolutional Neural Network for Solar Flares GOES Time-series Classification

2022 ◽  
Vol 258 (1) ◽  
pp. 12
Author(s):  
Vlad Landa ◽  
Yuval Reuveni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Solar Flare ◽  
Convolutional Neural Network ◽  
Solar Flares ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Classification ◽  
X Ray ◽  
Low Dimensional

Abstract Space weather phenomena such as solar flares have a massive destructive power when they reach a certain magnitude. Here, we explore the deep-learning approach in order to build a solar flare-forecasting model, while examining its limitations and feature-extraction ability based on the available Geostationary Operational Environmental Satellite (GOES) X-ray time-series data. We present a multilayer 1D convolutional neural network to forecast the solar flare event probability occurrence of M- and X-class flares at 1, 3, 6, 12, 24, 48, 72, and 96 hr time frames. The forecasting models were trained and evaluated in two different scenarios: (1) random selection and (2) chronological selection, which were compared afterward in terms of common score metrics. Additionally, we also compared our results to state-of-the-art flare-forecasting models. The results indicates that (1) when X-ray time-series data are used alone, the suggested model achieves higher score results for X-class flares and similar scores for M-class as in previous studies. (2) The two different scenarios obtain opposite results for the X- and M-class flares. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M- and X-class magnitude solar flare events. Furthermore, based on the suggested method, the achieved scores, obtained solely from X-ray time-series measurements, indicate that substantial information regarding the solar activity and physical processes are encapsulated in the data, and augmenting additional data sets, both spatial and temporal, may lead to better predictions, while gaining a comprehensive physical interpretation regarding solar activity. All source codes are available at https://github.com/vladlanda.

Coexistence of Periodic Roll Motion and Chaotic One in a Forced Flooded Ship

1998 ◽  
Vol 08 (03) ◽  
pp. 619-626 ◽  
Author(s):  
Sunao Murashige ◽  
Kazuyuki Aihara
Keyword(s):  
Mathematical Model ◽  
Time Series ◽  
Periodic Solution ◽  
Time Series Data ◽  
Series Data ◽  
Roll Motion ◽  
Positive Maximum ◽  
Liapunov Exponent ◽  
Wave Condition ◽  
Low Dimensional

This letter describes the coexistence of periodic and chaotic roll motion of a flooded ship in waves. We found experimentally, both with a flooded ferry model and with a simplified box-shaped model, that the two types of roll motion can coexist under the same wave condition. A trajectory reconstructed in a delay-coordinate state space from the time series data of the measured roll angle looks like a low-dimensional strange attractor. Moreover, a mathematical model for the simplified box-shaped ship shows the coexistence of a periodic solution and a chaotic one with a positive maximum Liapunov exponent.

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