LSTM-Guided Coaching Assistant for Table Tennis Practice

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.

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Quantifying non-communicable diseases’ burden in Egypt using State-Space model

PLoS ONE ◽

10.1371/journal.pone.0245642 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0245642

Author(s):

Somaya El-Saadani ◽

Mohamed Saleh ◽

Sarah A. Ibrahim

Keyword(s):

Time Series ◽

Health Services ◽

State Space ◽

Bayesian Methods ◽

Time Series Data ◽

State Space Model ◽

Series Data ◽

Non Communicable Diseases ◽

Space Model ◽

Health Burden

The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs’ health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, the Central Agency for Public Mobilization and Statistics (CAPMAS) Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease’s severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.

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Dynamic Analysis of Time Series Data Based on State Space Model

International Journal of u- and e- Service Science and Technology ◽

10.14257/ijunesst.2014.7.5.08 ◽

2014 ◽

Vol 7 (5) ◽

pp. 87-94

Author(s):

Yang Zhizhong ◽

Xi Bao

Keyword(s):

Time Series ◽

Dynamic Analysis ◽

State Space ◽

Time Series Data ◽

State Space Model ◽

Series Data ◽

Space Model ◽

Analysis Of Time Series

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Quantifying non-communicable diseases' burden in Egypt using State-Space model

10.1101/2021.01.07.21249383 ◽

2021 ◽

Author(s):

Somaya Mahmoud El-Saadani ◽

Mohamed Mostafa Saleh ◽

Sarah Assem Ibrahim

Keyword(s):

Time Series ◽

Health Services ◽

State Space ◽

Bayesian Methods ◽

Time Series Data ◽

State Space Model ◽

Series Data ◽

Non Communicable Diseases ◽

Space Model ◽

Health Burden

The study aimed to model and quantify the health burden induced by four non-communicable diseases in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs' health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, CAPMAS's Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease's severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.

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

Volume 7B: 9th International Conference on Multibody Systems, Nonlinear Dynamics, and Control ◽

10.1115/detc2013-13519 ◽

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.

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Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Entropy ◽

10.3390/e24010115 ◽

2022 ◽

Vol 24 (1) ◽

pp. 115

Author(s):

Hiroaki Inoue ◽

Koji Hukushima ◽

Toshiaki Omori

Keyword(s):

State Space ◽

Latent Variables ◽

Time Series Data ◽

State Space Model ◽

The State ◽

Series Data ◽

Replica Exchange ◽

Space Model ◽

Marginal Posterior Distribution ◽

Exchange Particle

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis–Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis–Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.

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Predicting Time Series from Short-Term High-Dimensional Data

International Journal of Bifurcation and Chaos ◽

10.1142/s021812741430033x ◽

2014 ◽

Vol 24 (12) ◽

pp. 1430033 ◽

Cited By ~ 10

Author(s):

Huanfei Ma ◽

Tianshou Zhou ◽

Kazuyuki Aihara ◽

Luonan Chen

Keyword(s):

Time Series ◽

Time Series Data ◽

High Dimensional Data ◽

Series Data ◽

High Dimensional ◽

Short Term ◽

Low Dimensional ◽

Low Dimensional Dynamics ◽

Term Data

The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.

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