scholarly journals Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 180
Author(s):  
Mario Budig ◽  
Michael Keiner ◽  
Riccardo Stoohs ◽  
Meike Hoffmeister ◽  
Volker Höltke
Keyword(s):  
Heart Rate ◽  
Field Study ◽  
Absolute Error ◽  
Distance Measurement ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Distance Measurements ◽  
Cross Sectional ◽  
Absolute Percentage Error ◽  
Activity Trackers

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports.

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

scholarly journals Validity of wrist-worn consumer products to measure heart rate and energy expenditure

2018 ◽  
Vol 4 ◽  
pp. 205520761877032 ◽  
Author(s):  
Robert S. Thiebaud ◽  
Merrill D. Funk ◽  
Jacelyn C. Patton ◽  
Brook L. Massey ◽  
Terri E. Shay ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Energy Expenditure ◽  
Consumer Products ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Metabolic Cart ◽  
General Exercise ◽  
Measure Heart Rate

Introduction The ability to monitor physical activity throughout the day and during various activities continues to improve with the development of wrist-worn monitors. However, the accuracy of wrist-worn monitors to measure both heart rate and energy expenditure during physical activity is still unclear. The purpose of this study was to determine the accuracy of several popular wrist-worn monitors at measuring heart rate and energy expenditure. Methods Participants wore the TomTom Cardio, Microsoft Band and Fitbit Surge on randomly assigned locations on each wrist. The maximum number of monitors per wrist was two. The criteria used for heart rate and energy expenditure were a three-lead electrocardiogram and indirect calorimetry using a metabolic cart. Participants exercised on a treadmill at 3.2, 4.8, 6.4, 8 and 9.7 km/h for 3 minutes at each speed, with no rest between speeds. Heart rate and energy expenditure were manually recorded every minute throughout the protocol. Results Mean absolute percentage error for heart rate varied from 2.17 to 8.06% for the Fitbit Surge, from 1.01 to 7.49% for the TomTom Cardio and from 1.31 to 7.37% for the Microsoft Band. The mean absolute percentage error for energy expenditure varied from 25.4 to 61.8% for the Fitbit Surge, from 0.4 to 26.6% for the TomTom Cardio and from 1.8 to 9.4% for the Microsoft Band. Conclusion Data from these devices may be useful in obtaining an estimate of heart rate for everyday activities and general exercise, but energy expenditure from these devices may be significantly over- or underestimated.

FRACTIONAL RESIDUAL PLOT FOR MODEL VALIDATION

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Nur Arina Bazilah Kamisan ◽  
Muhammad Hisyam Lee ◽  
Suhartono Suhartono ◽  
Abdul Ghapor Hussin ◽  
Yong Zulina Zubairi
Keyword(s):  
Model Validation ◽  
Goodness Of Fit ◽  
Mean Absolute Error ◽  
Pairwise Comparison ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Residual Plot

A pairwise comparison is important to measure the goodness-of-fit of models. Error measurements are used for this purpose but it only limit to the value, thus a graph is used to help show the precision of the models. These two should show a tally result in order to defense the hypothesis correctly. In this study, a fractional residual plot is proposed to help showing the precision of forecasts. This plot improvises the scale of the graph by changing the scale into decimal ranging from -1 to 1. The closer the point to 0 will indicate that forecast is robust and value closer to -1 or 1 will indicate that the forecast is poor. Two error measurements which are mean absolute error (MAE) and mean absolute percentage error (MAPE) and residual plot are used to justify the results and make comparison with the proposed fractional residual plot. Three difference data are used for this purpose and the results have shown that the fractional residual plot could give as much information as the residual plot but in an easier and meaningful way. In conclusion, the error plot is important in visualize the accurateness of the forecast.  

scholarly journals Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study (Preprint)

2020 ◽  
Author(s):  
David Joseph Muggeridge ◽  
Kirsty Hickson ◽  
Aimie Victoria Davies ◽  
Oonagh M Giggins ◽  
Ian L Megson ◽  
...  
Keyword(s):  
Heart Rate ◽  
Wearable Devices ◽  
Bicycle Ergometer ◽  
Cycle Ergometer ◽  
Primary Objective ◽  
Healthy Adults ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Poor Correlation ◽  
Absolute Percentage Error

BACKGROUND Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. OBJECTIVE The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. METHODS A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. RESULTS Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (<i>r</i>=0.95), with a mean bias of −1 beats·min<sup>-1</sup> and limits of agreement of −20 to 19 beats·min<sup>-1</sup>. The Fitbit Charge 3 device underestimated heart rate by 7 beats·min<sup>-1</sup> compared with Polar H10, with a limit of agreement of −46 to 33 beats·min<sup>-1</sup> and poor correlation (<i>r</i>=0.8). The mean absolute percentage error for both devices was deemed acceptable (&lt;5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. CONCLUSIONS Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises. CLINICALTRIAL

scholarly journals Evaluation of Several Error Measures Applied to the Sales Forecast System of Chemicals Supply Enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 39
Author(s):  
Ma. del Rocío Castillo Estrada ◽  
Marco Edgar Gómez Camarillo ◽  
María Eva Sánchez Parraguirre ◽  
Marco Edgar Gómez Castillo ◽  
Efraín Meneses Juárez ◽  
...  
Keyword(s):  
Forecast Model ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Error Evaluation ◽  
Sales Forecast ◽  
Absolute Percentage Error ◽  
Sales Prediction ◽  
Prediction Techniques ◽  
Evaluation Techniques

The objective of the industry in general, and of the chemical industry in particular, is to satisfy consumer demand for products and the best way to satisfy it is to forecast future sales and plan its operations.Considering that the choice of the best sales forecast model will largely depend on the accuracy of the selected indicator (Tofallis, 2015), in this work, seven techniques are compared, in order to select the most appropriate, for quantifying the error presented by the sales forecast models. These error evaluation techniques are: Mean Percentage Error (MPE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE) and Mean Absolute Arctangent Percentage Error (MAAPE). Forecasts for chemical product sales, to which error evaluation techniques are applied, are those obtained and reported by Castillo, et. al. (2016 & 2020).The error measuring techniques whose calculation yields adequate and convenient results, for the six prediction techniques handled in this article, as long as its interpretation is intuitive, are SMAPE and MAAPE. In this case, the most adequate technique to measure the error presented by the sales prediction system turned out to be SMAPE.

scholarly journals Accuracy and reliability of ultrasound estimation of fetal weight in women with a singleton term pregnancy

2019 ◽  
Vol 9 (1) ◽  
pp. 323
Author(s):  
Reena Sharma ◽  
Rohit Bhoil ◽  
Poojan Dogra ◽  
Sushruti Kaushal ◽  
Ajay Sharma
Keyword(s):  
Birth Weight ◽  
Mode Of Delivery ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Fetal Weight ◽  
Percentage Error ◽  
Strong Positive Correlation ◽  
Positive Correlation ◽  
Absolute Percentage Error ◽  
Estimated Fetal Weight

Background: Prenatal estimation of birth-weight is of utmost importance to predict the mode of delivery. This is also an important parameter of antenatal care. This study was conducted to evaluate the accuracy of estimated fetal weight by ultrasound, compared with actual birth weight.Methods: This was a prospective and comparative study comprising 110 pregnant women at term. Patients who had their sonography done within 7 days from date of delivery were included. Fetal weight was estimated by Hadlock 2 formula, the software of which was preinstalled in ultrasound-machine. The estimated fetal weight was compared to the post-delivery birth-weight. The Pearson's correlation coefficient was used and the accuracy of sonographic fetal weight estimation was evaluated using mean error, mean absolute error, mean percentage error, mean absolute percentage error and proportion of estimates within 10% of actual birth weight.Results: Mean estimated and actual birth weights were 3120.8±349.4 gm and 3088.2±404.5 g respectively. There was strong positive correlation between estimated fetal weight and actual birth weight (r = 0.58, p<0.001). The mean percentage error and mean absolute percentage error of ultrasound fetal weight estimations were 1.96±11.8% and 8.7±8.2% respectively. The percentage of estimates within ±10% of the actual birth weight was found to be 67.3%. In 23% of the cases, ultrasound overestimated the birth weight. In 13% of the cases, ultrasound underestimated the birth weight.Conclusions: There was strong positive correlation between actual and sonographically estimated fetal weight. So, ultrasonography can be considered as useful tool for estimating the fetal weight for improving the perinatal outcome.

scholarly journals Observer Influence with Other Variables on the Accuracy of Ultrasound Estimation of Fetal Weight at Term

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 216
Author(s):  
Mariola Sánchez-Fernández ◽  
Maria E. Corral ◽  
Longinos Aceituno ◽  
Marina Mazheika ◽  
Nicolás Mendoza ◽  
...  
Keyword(s):  
Absolute Error ◽  
Cross Sectional Study ◽  
Fetal Weight ◽  
Percentage Error ◽  
Time Interval ◽  
Cross Sectional ◽  
Absolute Percentage Error ◽  
Maternal Morbidity And Mortality ◽  
Simple Error ◽  
Error Percentage

Background and Objectives: The accuracy with which the estimation of fetal weight (EFW) at term is determined is useful in order to address obstetric complications, since it is a parameter that represents an important prognostic factor for perinatal and maternal morbidity and mortality. The aim of this study was to determine the role of the experienced observers with other variables that could influence the accuracy of the ultrasound used to calculate EFW at term, carried out within a period of seven days prior to delivery, in order to assess interobserver variability. Materials and Methods: A cross-sectional study was performed including 1144 pregnancies at term. The validity of the ultrasound used to calculate EFW at term was analyzed using simple error, absolute error, percentage error and absolute percentage error, as well as the percentage of predictions with an error less than 10 and 15% in relation to maternal, obstetric and ultrasound variables. Results: Valid predictions with an error less than 10 and 15% were 74.7 and 89.7% respectively, with such precision decreasing according to the observer as well as in extreme fetal weights. The remaining variables were not significant in ultrasound EFW at term. The simple error, absolute error, percentage error and absolute percentage error were greater in cases of extreme fetal weights, with a tendency to overestimate the low weights and underestimate the high weights. Conclusions: The accuracy of EFW with ultrasound carried out within seven days prior to birth is not affected by maternal or obstetric variables, or by the time interval between the ultrasound and delivery. However, accuracy was reduced by the observers and in extreme fetal weights.

scholarly journals PERAMALAN JUMLAH MAHASISWA MENGGUNAKAN MOVING AVERAGE

Telematika ◽  
2018 ◽  
Vol 15 (1) ◽  
pp. 67
Author(s):  
Hari Prapcoyo
Keyword(s):  
Mean Square Error ◽  
Mean Absolute Error ◽  
Average Length ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Square ◽  
Absolute Percentage Error ◽  
Weighted Moving Average

AbstractThe Process of using resources in higher education is influenced by the up and down of the number students. The purpose of this study is to predict the number of students who study in the department of informatics engineering UPN Veteran Yogyakarta for the next periods. This research, data is taken from forlap dikti for Informatics Engineering fom 2009 until 2016 at UPN Veteran Yogyakarta. The method that used to forecast the number of students is a Moving Average method consisting of: Single Moving Average (SMA), Weighted Moving Average (WMA) and Exponential Moving Average (EMA). This study will use the forecasting accuracy namely Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to select the best model to be used for forecasting. The best model that used for forecasting is Weighted Moving Average (WMA) with weighted 1/3 and average length (n) used for 2. The smallest value for MSE of 5807.96; the smallest MAE value of 55.89 and the smallest value for MAPE of 5.24%. Forecasting of the number of students for four semesters in the future after the even semester of 2016 are respectively: 902; 901,33; 901,56 and 901,48. Keywords : Forecasting, UPN Veteran Yogyakarta, Single moving average(SMA) AbstrakProses penggunaan sumber daya perguruan tinggi setiap tahun dipengaruhi oleh naik turunnya jumlah mahasiswa. Tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa yang kuliah di jurusan teknik informatika UPN Veteran Yogyakarta untuk periode yang akan datang. Data penelitian ini diambil dari forlap dikti untuk Teknik Informatika dari tahun 2009 sampai 2016 UPN Veteran Yogyakarta. Metode yang digunakan untuk melakukan peramalan jumlah mahasiswa adalah metode Moving Average yang tediri dari : Single Moving Average (SMA), Weighted Moving Average (WMA) dan Exponential Moving Average (EMA). Penelitian ini akan menggunkan akurasi peramalan Mean Square Error (MSE), Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE) untuk memilih model terbaik yang akan digunakan untuk peramalan. Model terbaik yang digunakan untuk peramalan yaitu Weighted Moving Average (WMA) dengan pembobot 1/3 dan panjang rata-rata (n) yang dipakai sebesar 2. Nilai terkecil untuk MSE sebesar 5807,96; nilai terkecil MAE sebesar 55,89 dan nilai terkecil untuk MAPE sebesar 5,24 %. Peramalan untuk jumlah mahasiswa empat semester kedepan setelah semester genap 2016 masing-masing adalah : 902; 901,33; 901,56 dan 901,48. Kata Kunci : Peramalan, UPN Veteran Yogyakarta, Single Moving Average(SMA).

scholarly journals Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study

10.2196/25313 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e25313
Author(s):  
David Joseph Muggeridge ◽  
Kirsty Hickson ◽  
Aimie Victoria Davies ◽  
Oonagh M Giggins ◽  
Ian L Megson ◽  
...  
Keyword(s):  
Heart Rate ◽  
Wearable Devices ◽  
Bicycle Ergometer ◽  
Cycle Ergometer ◽  
Primary Objective ◽  
Healthy Adults ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Poor Correlation ◽  
Absolute Percentage Error

Background Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. Objective The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. Methods A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. Results Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of −1 beats·min-1 and limits of agreement of −20 to 19 beats·min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats·min-1 compared with Polar H10, with a limit of agreement of −46 to 33 beats·min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. Conclusions Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises.

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