scholarly journals Statistical Parametric Mapping Reveals Subtle Gender Differences in Angular Movements in Table Tennis Topspin Backhand

2020 ◽  
Vol 17 (19) ◽  
pp. 6996
Author(s):  
Ziemowit Bańkosz ◽  
Sławomir Winiarski
Keyword(s):  
Gender Differences ◽  
Time Series Data ◽  
Statistical Parametric Mapping ◽  
Study Groups ◽  
Series Data ◽  
Data Series ◽  
Table Tennis ◽  
Parametric Mapping ◽  
Analysis System ◽  
Dorsal Flexion

Background: Statistical parametric mapping (SPM) is an innovative method based on the analysis of time series (data series) and is equivalent to statistical methods for numerical (discrete) data series. This study aimed to analyze the patterns of movement in the topspin backhand stroke in table tennis and to use SPM to compare these patterns between advanced female and male players. Methods: The research involved seven advanced male and six advanced female players. The kinematic parameters were measured using an inertial motion analysis system. The SPM was computed using the SPM1D Python package. Results: Our study made it possible to reproduce the pattern of movement in the joints during topspin backhand strokes in the studied athletes. During multiple comparisons, the analysis of variance (ANOVA) SPM test revealed many areas in the studied parameter series with statistically significant differences (p ≤ 0.01). Conclusions: The study presents the movement patterns in the topspin backhand shot and describes the proximal-to-distal sequencing principle during this shot. The SPM study revealed differences between men and women in the contribution of thoracic rotation, external shoulder rotation, dorsal flexion, and supination in the wrist during the hitting phase. These differences may result from the anatomical gender differences or variations in other functionalities of individual body segments between the study groups. Another possible source for these discrepancies may reside in tactical requirements, especially the need for a more vigorous attack in men. The gender differences presented in this study can help in the individualization of the training process in table tennis.

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 Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

2020 ◽  
Vol 12 (17) ◽  
pp. 2735 ◽  
Author(s):  
Carlos M. Souza ◽  
Julia Z. Shimbo ◽  
Marcos R. Rosa ◽  
Leandro L. Parente ◽  
Ane A. Alencar ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Data Series ◽  
Forest Conversion ◽  
Pixel Resolution ◽  
Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

scholarly journals Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

2011 ◽  
Vol 2 (2) ◽  
pp. 636 ◽  
Author(s):  
Iwa Sungkawa ◽  
Ries Tri Megasari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Data Series ◽  
Past Data ◽  
The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape. 

scholarly journals Postural Analysis: Description of a Dedicated System

2021 ◽  
Vol 4 (3) ◽  
pp. 01-10
Author(s):  
Sergio Palandri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Formal Analysis ◽  
Series Data ◽  
Good Balance ◽  
Analysis Process ◽  
Analysis System ◽  
Postural Analysis ◽  
Initial Results ◽  
Analysis Protocol

Background Dispite of postural analysis is a fundamental tool for evaluating the neuromusculoskeletal structure of a subject a unique formal analysis process has not yet been determined. The aim of this work is to propose a postural analysis protocol t promote discussion with other practitioners so as to make continuous improvements in the proposed protocol to provide a tool that can serve the needs of specialists even more efficiently. Materials and Methods The proposed system consists of four subsystems: medical history data, photographic references, overall observations, and tests. Results Even though the initial results are encouraging, additional use of the described analysis system is considered essential to improve the technique and the skills of the operator. In this light, specific requests and observations from other professionals are essential, and obtaining these requests and observations is one of the purposes of the present article, as initially declared. Conclusions The proposed postural analysis system represents a good balance of various, often opposing needs, being easily completed and reasonably fast and repeatable, making it possible to compare time series data.

scholarly journals Short-term results of gait analysis with the Heidelberg foot measurement method and functional outcome after operative treatment of ankle fractures

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Jessica C. Böpple ◽  
Michael Tanner ◽  
Sarah Campos ◽  
Christian Fischer ◽  
Sebastian Müller ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Ground Reaction Force ◽  
Measurement Method ◽  
Reaction Force ◽  
Statistical Parametric Mapping ◽  
Ankle Fractures ◽  
Parametric Mapping ◽  
Healthy Participants ◽  
Over Time ◽  
Dorsal Flexion

Abstract Background Ankle fractures are common fractures in trauma surgery. Several studies have compared gait patterns between affected patients and control groups. However, no one used the Heidelberg Foot Measurement Method in combination with statistical parametric mapping of the entire gait cycle in this patient cohort. We sought to identify possible mobility deficits in the tibio-talar joint and medial arch in patients after ankle fractures as a sign of stiffness and pain that could result in a pathological gait pattern. We focused on the tibio-talar flexion as it is the main movement in the tibio-talar joint. Moreover, we examined the healing progress over time. Methods Fourteen patients with isolated ankle fractures were included prospectively. A gait analysis using the Heidelberg Foot Measurement Method was performed 9 and 26 weeks after surgery to analyse the tibio-talar dorsal flexion, the foot tibia dorsal flexion, the subtalar inversion and the medial arch as well as the cadence, the walking speed and the ground reaction force. The American Orthopedic Foot & Ankle Society ankle hindfoot score was used to obtain clinical data. Results were compared to those from 20 healthy participants. Furthermore, correlations between the American Orthopedic Foot & Ankle Society hindfoot score and the results of the gait analysis were evaluated. Results Statistical parametric mapping showed significant differences for the Foot Tibia Dorsal Flexion for patients after 9 weeks (53–75%: p = 0.001) and patients after 26 weeks (58–70%: p = 0.011) compared to healthy participants, respectively. Furthermore, significant differences regarding the tibio-talar dorsal flexion for patients 9 weeks after surgery (15–40%: p < 0.001; 56,5–70%: p = 0.007; 82–88%: p = 0.033; 97–98,5%: p = 0.048) as well as patients after 26 weeks (62,5–65%: p = 0.049) compared to healthy participants, respectively. There were no significant differences looking at the medial arch and the subtalar inversion. Moreover, significant differences regarding the ground reaction force were found for patients after 9 weeks (0–17%: p < 0.001; 21–37%: p < 0.001; 41–54%: p < 0.001; 60–64%: p = 0.013) as well as patients after 26 weeks (0–1,5%: p = 0.046; 5–15%: p < 0.001; 27–33%: p = 0.001; 45–49%: p = 0.005; 57–59%: p = 0.049) compared to healthy participants, respectively. In total, the range of motion in the tibio-talar joint and the medial arch was reduced in affected patients compared to healthy participants. Patients showed significant increase of the range of motion between 9 and 26 weeks. Conclusions This study shows, that patients affected by ankle fractures show limited mobility in the tibio-talar joint and the medial arch when compared to healthy participants. Even though the limitation of motion remains at least over a period of 26 weeks, a significant increase can be recognized over time. Furthermore, if we look at the absolute values, the patients’ values tend to get closer to those of the control group. Trial registration This study is registered at the German Clinical Trials Register (DRKS00023379).

scholarly journals An ECM Approach for Long Run Relationship Between Real Exchange Rate and Output Growth: Evidence from Bangladesh

2015 ◽  
Vol 63 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Khnd Md Mostafa Kamal
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Real Exchange Rate ◽  
Time Series Data ◽  
Output Growth ◽  
Series Data ◽  
Data Series ◽  
Currency Exchange ◽  
Long Run ◽  
Short Run

Currency exchange rate is an important aspect in modern economy which indicates the strength of domestic currency with respect to international currency. This study uses 42 years’ (1972 to 2013) time series data for Bangladesh in order to empirically determine whether the real exchange rate has significant impact on output growth for Bangladesh by using error correction model (ECM).The time series econometrics properties of the data series have been thoroughly investigated to apply ECM approach. The empirical evidence suggests mixed results; in the short run low exchange rate has positive significant effect while in the long run output growth is positively affected high exchange rate pass through.Dhaka Univ. J. Sci. 63(2):105-110, 2015 (July)

scholarly journals Central Neurochemical Ultradian Variability in Depression

2006 ◽  
Vol 22 (1-2) ◽  
pp. 65-72 ◽  
Author(s):  
Ronald M. Salomon ◽  
Benjamin W. Johnson ◽  
Dennis E. Schmidt
Keyword(s):  
Repeated Measures ◽  
Time Series Data ◽  
Antidepressant Treatment ◽  
Series Data ◽  
Time Variability ◽  
Data Series ◽  
Monoamine Metabolites ◽  
Intrinsic Variability ◽  
Neuroendocrine Function ◽  
Depressed Patients

Depression is characterized by blunted behavior and neuroendocrine function that generally improve with antidepressant treatment. This study examined intrinsic variability in brain neurotransmitter function, since it may be a source of blunted behavior and neuroendocrine function in depression and a marker for the illness, and has not previously been analyzed using wavelet decomposition. To measure variability in monoamine metabolites, lumbar cerebrospinal fluid (CSF) was collected in serial samples in depressed patients before and after treatment. We hypothesized that changes in variability would be observed after treatment. Mechanisms that control such variability may be critical to the pathophysiology of depression.Method:Time series data was obtained from serial ten-min sampling over a 24-hr period (N= 144) from thirteen depressed patients, with a repeat collection after 5 weeks of antidepressant (sertraline or bupropion) treatment. Concentrations of tryptophan (TRP), the monoamine metabolites 5-HIAA (metabolite of serotonin) and HVA (metabolite of dopamine), and the HVA:5HIAA ratio were transformed to examine power in slowly (160 min/cycle) to rapidly (20 min/cycle) occurring events. Power, the sum of the squares of the coefficients in each d (detail) wavelet, reflects variability within a limited frequency bandwidth for that wavelet. Pre-treatment to post-treatment comparisons were conducted with repeated measures ANOVA.Results:Antidepressant treatment was associated with increased power in the d2 wavelet from the HVA (p= 0.03) and the HVA:5-HIAA ratio (p= 0.03) series. The d1 and d3 wavelets showed increased power following antidepressant treatment for the ratio series (d1,p= 0.01; d3,p= 0.05). Significant changes in power were not observed for the 5-HIAA data series. Power differences among analytes suggest that the findings are specific to each system.Conclusion:The wavelet transform analysis shows changes in neurochemical signal variability following antidepressant treatment. Patterns or degrees of variability may be as important as, or possibly more important than, the mean levels of monoamine transmitters. Studies of variability observed in healthy individuals and a larger depressed sample will be needed to verify a relationship with mood and treatment response. Neurochemical measures of time-variability may be a pivotal marker in depression.

scholarly journals Time Series Outlier Detection Based on Sliding Window Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yufeng Yu ◽  
Yuelong Zhu ◽  
Shijin Li ◽  
Dingsheng Wan
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Hydrologic Time Series ◽  
Prediction Confidence ◽  
Evaluation Of Data ◽  
Operation And Management

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

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