Tracking Performance with Visual, Auditory, or Electrocutaneous Displays

1972 ◽  
Vol 14 (2) ◽  
pp. 131-138 ◽  
Author(s):  
Mark A. Hofmann ◽  
Norman W. Heimstra
Keyword(s):  
Root Mean Square ◽  
Constant Error ◽  
Visual Display ◽  
Absolute Error ◽  
The Body ◽  
Information Feedback ◽  
Mean Square ◽  
Discriminate Analysis ◽  
Error Analyses ◽  
Speed Of Response

Three groups of ten subjects each performed a unidimensional compensatory tracking task for 30 minutes. Each group received continuous error information feedback either visually, auditorally, or cutaneously. Direction of error was signaled by the side of the body stimulated, magnitude of error was indicated by signal intensity. Intensities of the signals were subjectively matched. Performance was measured by time on target, constant error, absolute error, and root-mean-square error. Analyses of variance for these measures yielded significant differences between display types on all but the root-mean-square measure. A multiple discriminate analysis was performed on these measures and provided two significantly independent dimensions of discrimination. The first dimension was defined as “speed of response,” the second “goodness of performance.” It was concluded that for the particular task used, the auditory and cutaneous displays proved more effective than the visual display.

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 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.

scholarly journals An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3108 ◽  
Author(s):  
Shing-Hong Liu ◽  
Chuan-Bi Lin ◽  
Ying Chen ◽  
Wenxi Chen ◽  
Tai-Shen Huang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Root Mean Square ◽  
The Body ◽  
Median Frequency ◽  
Mean Square ◽  
Sport Injuries ◽  
Physiological Measurement ◽  
The Real ◽  
Emg Signal

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

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.

Study on Intelligent Control Strategy for Semi-Active Suspension System of Tracked Vehicle

2011 ◽  
Vol 48-49 ◽  
pp. 1162-1171 ◽  
Author(s):  
Yi Hui Zeng ◽  
Shao Jun Liu ◽  
Wei Cheng
Keyword(s):  
Root Mean Square ◽  
Intelligent Control ◽  
Pitch Angle ◽  
Control Method ◽  
Active Suspension ◽  
Suspension System ◽  
The Body ◽  
Mean Square ◽  
Tracked Vehicle ◽  
Magneto Rheological

Considering the multiple and complicated driving conditions for tracked vehicles and their structural features, a comprehensive intelligent control method to deal with semi-active suspension was proposed based on the principle of magneto rheological damper. One half of the tracked vehicle suspension system is taken as the research object, where analysis is directed to the vertical amplitude, pitch angle and vertical body acceleration response. And the magneto rheological damper was taken as an actuator, the fuzzy control was taken as feedforward and PID control was taken as feedback. The control system model has been established by using of the complex random road output to simulink due to the condition of MATLAB/Simulink. The simulation results show that it is of good real-time control competence, good robustness and high accuracy, etc. Contrasting to passive suspension, some capability parameters such as the body vertical amplitude, pitch angle and the body vertical acceleration of the semi-active suspension system can had been well controlled by using of the intelligent hybrid control method, for exmple, the root mean square value of vertical amplitude decreased by 37.2%，the root mean square value of pitch angle decreased by 45.2% and root mean square value of the vertical vibration acceleration decreased by 38.6%.

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.

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