scholarly journals Skin Cooling and Force Replication at the Ankle in Healthy Individuals: A Crossover Randomized Controlled Trial

2015 ◽  
Vol 50 (6) ◽  
pp. 621-628 ◽  
Author(s):  
Daniela Pacheco dos Santos Haupenthal ◽  
Marcos de Noronha ◽  
Alessandro Haupenthal ◽  
Caroline Ruschel ◽  
Guilherme S. Nunes
Keyword(s):  
Data Collection ◽  
Root Mean Square ◽  
Isometric Force ◽  
Absolute Error ◽  
Mean Square ◽  
Healthy Individuals ◽  
Randomized Controlled ◽  
Skin Cooling ◽  
Target Force ◽  
Ankle Dorsiflexor

Context Proprioception of the ankle is determined by the ability to perceive the sense of position of the ankle structures, as well as the speed and direction of movement. Few researchers have investigated proprioception by force-replication ability and particularly after skin cooling. Objective To analyze the ability of the ankle-dorsiflexor muscles to replicate isometric force after a period of skin cooling. Design Randomized controlled clinical trial. Setting Laboratory. Patients or Other Participants Twenty healthy individuals (10 men, 10 women; age = 26.8 ± 5.2 years, height = 171 ± 7 cm, mass = 66.8 ± 10.5 kg). Intervention(s) Skin cooling was carried out using 2 ice applications: (1) after maximal voluntary isometric contraction (MVIC) performance and before data collection for the first target force, maintained for 20 minutes; and (2) before data collection for the second target force, maintained for 10 minutes. We measured skin temperature before and after ice applications to ensure skin cooling. Main Outcome Measure(s) A load cell was placed under an inclined board for data collection, and 10 attempts of force replication were carried out for 2 values of MVIC (20%, 50%) in each condition (ice, no ice). We assessed force sense with absolute and root mean square errors (the difference between the force developed by the dorsiflexors and the target force measured with the raw data and after root mean square analysis, respectively) and variable error (the variance around the mean absolute error score). A repeated-measures multivariate analysis of variance was used for statistical analysis. Results The absolute error was greater for the ice than for the no-ice condition (F1,19 = 9.05, P = .007) and for the target force at 50% of MVIC than at 20% of MVIC (F1,19 = 26.01, P < .001). Conclusions The error was greater in the ice condition and at 50% of MVIC. Skin cooling reduced the proprioceptive ability of the ankle-dorsiflexor muscles to replicate isometric force.

Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models

2020 ◽  
Vol 30 (4) ◽  
pp. 249-257
Author(s):  
Reid J. Reale ◽  
Timothy J. Roberts ◽  
Khalil A. Lee ◽  
Justina L. Bonsignore ◽  
Melissa L. Anderson
Keyword(s):  
Root Mean Square Error ◽  
Metabolic Rate ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Compartment Model ◽  
Absolute Error ◽  
Mean Square ◽  
Adolescent Athletes ◽  
Two Compartment Model

We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models’ performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) − (340 male or 537 female).

scholarly journals New Method for Forest Resource Data Collection Based on Smartphone Fusion with Multiple Sensors

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guangpeng Fan ◽  
Yanqi Dong ◽  
Danyu Chen ◽  
Feixiang Chen
Keyword(s):  
Data Collection ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Forest Stand ◽  
Mean Square ◽  
Forest Resource ◽  
Smart Space ◽  
Resource Monitoring ◽  
Resource Data

Tree parameter measurement is an important part of forest resource monitoring. Smartphones play an important role in forest resource surveys. Although sensors inside smartphones, such as gyroscopes and angle sensors, can meet the needs of the public for entertainment or games, the measurement accuracy in professional forest resource monitoring is slightly insufficient. In this paper, a method of collecting tree measurement factors based on personal smart space fusion with a variety of high-precision sensors is proposed. First of all, a high-precision attitude sensor measurement module and a laser ranging module are organically integrated and packaged in a black box. The smartphone is then connected to the sensor box using a magnet sheet, and the working personnel can determine key parameters in the forest stand by holding it. Finally, in order to verify the accuracy of the method, the measured values in this paper are compared with the reference values. The root mean square error (RMSE) of the tree position in the X and Y directions was 0.114 m and 0.147 m, the relative deviations (rBias) were 0.95% and 0.39%, and the average RMSE was 0.186 m. The RMSEs measured by tree height and diameter at breast height (DBH) were 0.98 m and 2.24 cm, the relative root mean square error (rRMSE) was 5.87% and 13.46%, and the relative deviations (rBias) were −1.40% and −1.06%, respectively. Therefore, the method of forest stand parameter measurement based on personal smart space fusion multitype sensors proposed in this paper can be effectively applied to forest resource data collection.

scholarly journals Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

2020 ◽  
Vol 12 (3) ◽  
pp. 356 ◽  
Author(s):  
Hui Qiu ◽  
Shuanggen Jin
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Absolute Error ◽  
Satellite System ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Mean Square ◽  
Mean Sea Surface

Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error ( RMSE ) of 3.56 m and, a correlation coefficient ( R ) of 0.95.

scholarly journals Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

2014 ◽  
Vol 7 (3) ◽  
pp. 1247-1250 ◽  
Author(s):  
T. Chai ◽  
R. R. Draxler
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Evaluation ◽  
Mean Absolute Error ◽  
Evaluation Studies ◽  
Model Performance ◽  
Absolute Error ◽  
Average Error ◽  
Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

The Acute Effect of Talocrural Joint Mobilization on Dorsiflexor Muscle Strength in Healthy Individuals: A Randomized Controlled Single-Blind Study

2019 ◽  
Vol 28 (6) ◽  
pp. 601-605 ◽  
Author(s):  
Ufuk Ersoy ◽  
Umut Ziya Kocak ◽  
Ezgi Unuvar ◽  
Bayram Unver
Keyword(s):  
Muscle Strength ◽  
Acute Effect ◽  
Control Group ◽  
Study Group ◽  
Healthy Individuals ◽  
Talocrural Joint ◽  
Joint Mobilization ◽  
Randomized Controlled ◽  
Single Blind ◽  
Ankle Dorsiflexor

Context: Mobilization has been used for enhancing muscle strength. Objective: The aim of this study was to investigate the acute effect of talocrural joint mobilization on ankle dorsiflexor muscle strength in healthy individuals, which has not yet been studied. Design: Randomized controlled single-blind study. Setting: University laboratory. Participants: Forty-eight healthy individuals. Interventions: Maitland grade III (study group) versus Maitland grade I (control group) mobilizations. Main Outcome Measures: Muscle strength measurements were performed using a handheld dynamometer at baseline, immediately after the mobilization, and 30 minutes after mobilization. Results: At baseline, the physical characteristics and muscular strength were similar in both groups (P > .05). According to Friedman analysis, a significant difference was detected following the mobilization in the study group (P < .001), and while the muscle strength at immediately after the mobilization and at 30 minutes after mobilization was significantly higher than baseline (P < .001), no significant differences were observed between 30 minutes after mobilization and immediately after the mobilization (P = .17). However, no significant changes were detected in the control group. The study group was found superior to the control group in terms of muscle strength differences following the mobilization (P < .001). Conclusion: The ankle dorsiflexor muscle strength might be increased by performing Maitland grade III mobilization, and this increase might be preserved for 30 minutes, while Maitland grade I mobilization did not lead to such an improvement in healthy individuals.

scholarly journals AZƏRBAYCANDA COVID-19 KORONAVİRUSUNA YOLUXMANIN PROQNOZLAŞDIRILMASI ÜÇÜN ARIMA MODELLƏRİNİN TƏTBİQİ

2021 ◽  
Vol 12 (1) ◽  
pp. 95-104
Author(s):  
Firəngiz Sadıyeva ◽  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

Məqalədə COVID-19 pandemiyasını proqnozlaşdırmaq üçün avtoreqressiv inteqrasiya edilmiş hərəkətli ortalama (ing. ARIMA. Autoregressive İntegrated Moving Average) modeli təklif edilmişdir. COVID-19 dünyada sürətlə yayılan və hazırda davam edən yeni növ pandemiyadır. Son dövrlərdə pandemiyaya yoluxanların sayı Azərbaycanda rekord həddə çatmışdır. Məhz bu səbəbdən COVID-19 pandemiyasının proqnozu məsələsinə baxılmışdır və bir neçə ayı əhatə edən real verilənlərlə eksperimentlərdə təklif edilmiş ARIMA modelinin COVID-19 zaman sıralarının proqnozlaşdırılması üçün müxtəlif parametrlərlə tətbiq edilmişdir. Verilənlər dedikdə, 22.01.2020 – 22.10.2020 tarixləri arasında Azərbaycan Respublikasının Səhiyyə Nazirliyi (www.sehiyye.gov.az) tərəfindən rəsmi olaraq qeydiyyata alınan gündəlik yoluxma hallarının sayı nəzərdə tutulur. Bu verilənlərdən istifadə etməklə, növbəti zaman aralığında ölkəmizdə baş verəcək yoluxma halları proqnoz edilmişdir. Bunun üçün ARIMA modelinə müxtəlif parametrlər verilmiş və uyğun olaraq hər bir modelin səhv dərəcəsi qiymətləndirilmişdir. Səhvin qiymətləndirilməsi üçün MAPE (Mean Absolute Persentace Error), MAE (Mean Absolute Error) və RMSE (Root Mean Square Error) funksiyaları istifadə edilib. Müqayisələr nəticəsində ən uyğun model seçilmişdir. Alınmış nəticələr ölkəmizdə pandemiya dövründə həm səhiyyə sistemi, həm də adi vətəndaşlar üçün vacib amildir. Əldə edilmiş nəticələr statistik metodların koronavirusa aid qeyri-stasionar zaman sıralarının proqnozlaşdırılmasının digər məsələlərə tətbiqində də məhsuldar ola biləcəyini təsdiqləyir.

scholarly journals Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

2021 ◽  
Vol 20 (2) ◽  
pp. 113-119
Author(s):  
Khaled Ferkous ◽  
Farouk Chellali ◽  
Abdalah Kouzou ◽  
Belgacem Bekkar
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Horizontal Surface ◽  
Total Solar Radiation ◽  
Mean Square

Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

scholarly journals Root mean square error (RMSE) or mean absolute error (MAE)?

2014 ◽  
Vol 7 (1) ◽  
pp. 1525-1534 ◽  
Author(s):  
T. Chai ◽  
R. R. Draxler
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Evaluation ◽  
Mean Absolute Error ◽  
Evaluation Studies ◽  
Model Performance ◽  
Absolute Error ◽  
Average Error ◽  
Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.

A validation scheme for homogenization techniques on a Swedish temperature network using artificial inhomogeneities (1950-2005)

2021 ◽  
Author(s):  
Giulio Nils Caroletti ◽  
Tommaso Caloiero ◽  
Magnus Joelsson ◽  
Roberto Coscarelli
Keyword(s):  
Missing Data ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Temperature Data ◽  
Physical Parameters ◽  
Mean Square

&lt;p&gt;Homogenization techniques and missing value reconstruction have grown in importance in climatology given their relevance in establishing coherent data records over which climate signals can be correctly attributed, discarding apparent changes depending on instrument inhomogeneities, e.g., change in instrumentation, location, time of measurement.&lt;/p&gt;&lt;p&gt;However, it is not generally possible to assess homogenized results directly, as true data values are not known. Thus, to validate homogenization techniques, artificially inhomogeneous datasets, also called benchmark datasets, are created from known homogeneous datasets. Results from their homogenization can be assessed and used to rank, evaluate and/or validate techniques used.&lt;/p&gt;&lt;p&gt;Considering temperature data, the aims of this work are: i) to determine which metrics (bias, absolute error, factor of exceedance, root mean squared error, and Pearson&amp;#8217;s correlation coefficient) can be meaningfully used to validate the best-performing homogenization technique in a region; ii) to evaluate through a Pearson correlation analysis if homogenization techniques&amp;#8217; performance depends on physical features of a station (i.e., latitude, altitude and distance from the sea) or on the nature of the inhomogeneities (i.e., the number of break points and missing data).&lt;/p&gt;&lt;p&gt;With this aims, a southern Sweden temperature database with homogeneous, maximum and minimum temperature data from 100 ground stations over the period 1950-2005 has been used. Starting from these data, inhomogeneous datasets were created introducing up to 7 artificial breaks for each ground station and an average of 107 missing data. Then, 3 homogenization techniques were applied, ACMANT (Adapted Caussinus-Mestre Algorithm for Networks of Temperature series), and two versions of HOMER (HOMogenization software in R): the standard, automated setup mode (Standard-HOMER) and a manual setup developed and performed at the Swedish Meteorological and Hydrological Institute (SMHI-HOMER).&lt;/p&gt;&lt;p&gt;Results showed that root mean square error, absolute bias and factor of exceedance were the most useful metrics to evaluate improvements in the homogenized datasets: for instance, RMSE for both variables was reduced from an average of 0.71-0.89K (corrupted dataset) to 0.50-0.60K (Standard-HOMER), 0.51-0.61K (SMHI-HOMER) and 0.46-0.53K (ACMANT), respectively.&lt;/p&gt;&lt;p&gt;Globally, HOMER performed better regarding the factor of exceedance, while ACMANT outperformed it with regard to root mean square error and absolute error. Regardless of the technique used, the homogenization quality anti-correlated meaningfully to the number of breaks. Missing data did not seem to have an impact on HOMER, while it negatively affected ACMANT, because this method does not fill-in missing data in the same drastic way.&lt;/p&gt;&lt;p&gt;In general, the nature of the datasets had a more important role in yielding good homogenization results than associated physical parameters: only for minimum temperature, distance from the sea and altitude showed a weak but significant correlation with the factor of exceedance and the root mean square error.&lt;/p&gt;&lt;p&gt;This study has been performed within the INDECIS Project, that is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).&lt;/p&gt;

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