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.

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 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.

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.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Penerapan metode fuzzy sugeno untuk prediksi persediaan bahan baku

2019 ◽  
Vol 9 (2) ◽  
pp. 12-20
Author(s):  
Julio Warmansyah ◽  
Dida Hilpiah
Keyword(s):  
Fuzzy Logic ◽  
Raw Materials ◽  
Mean Absolute Percentage Error ◽  
Raw Material ◽  
Percentage Error ◽  
Human Power ◽  
Absolute Percentage Error ◽  
Input Variables ◽  
A Company

 PT. Cahaya Boxindo Prasetya is a company engaged in the manufacture of carton boxes or boxes. The company's activities also include cutting and printing services using machinery and human power. The problem faced in this company is the difficulty of predicting the amount of inventory of raw materials that will be  included in the production. The remaining raw materials for production will be used as the final stock to get the minimum, the goal is to reduce excess stock Overcoming this problem, fuzzy logic is used to predict raw material inventories by focusing on the final stock. In this study using Fuzzy Sugeno, with three input variables, namely: initial inventory, purchase, production, while the output is the final stock. Determination of prediction results using defuzzification using the average concept of MAPE (Mean Absolute Percentage Error). The results obtained, using the Fuzzy Sugeno method can predict the inventory of raw materials with a MAPE value of 38%. 

scholarly journals PERBANDINGAN METODE EARNED VALUE, EARNED SCHEDULE, DAN KALMAN FILTER EARNED VALUE UNTUK PREDIKSI DURASI PROYEK

2020 ◽  
Vol 3 (1) ◽  
pp. 155
Author(s):  
Andree Sugiyanto ◽  
Onnyxiforus Gondokusumo
Keyword(s):  
Kalman Filter ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Project Schedule ◽  
Probabilistic Forecasting ◽  
Earned Value ◽  
Control Data ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Duration Prediction

In the world of construction, control is needed at the implementation stage, which is prediction or forecasting duration project schedule. Estimated project schedule is an important part for project management making decisions that affect the future of the project. Forecasting method commonly used by practitioners in this case the construction project contractor in evaluating prediction of duration is deterministic forecasting method Earned Value Method (EVM), Earned Schedule Method (ESM). Kalman Filter Earned Value Method (KEVM) as probabilistic forecasting method is carried out to produce more accurate predictive value. The purpose of this study to compare the accuracy of three methods. This research was conducted by calculating duration of the project from EVM, ESM, and KEVM on maintenance and reconstruction projects of Jakarta-Cikampek and Jakarta-Tangerang toll roads. The data used from the project control data S-curve. The control data is processed with EVM, ESM, KEVM to determine the comparison between three methods of predicting duration. Prediction results of three methods were tested with Mean Absolute Percentage Error (MAPE). The results of this study indicate that KEVM can reduce errors after Kalman Filter is performed on estimated duration using EVM. ESM duration prediction yields the smallest MAPE value of the three methods. AbstrakDalam dunia pembangunan konstruksi dibutuhkan pengendalian pada tahap pelaksanaan yaitu prediksi atau peramalan durasi jadwal proyek. Perkiraan jadwal proyek adalah bagian penting untuk manajemen proyek membuat keputusan yang mempengaruhi masa depan proyek. Metode peramalan yang umum digunakan para praktisi dalam hal ini kontraktor proyek konstruksi dalam mengevaluasi prediksi durasi adalah metode peramalan deterministik Earned Value Method (EVM), Earned Schedule Method (ESM). Kalman Filter Earned Value Method (KEVM) sebagai metode peramalan probabilistik dilakukan untuk menghasilkan nilai prediksi yang lebih akurat. Tujuan penelitian ini membandingkan akurasi dari ketiga metode. Penelitian ini dilakukan dengan menghitung durasi proyek dari EVM, ESM, dan KEVM pada proyek pemeliharaan dan rekonstruksi jalan tol Jakarta – Cikampek dan Jakarta – Tangerang. Data yang digunakan dari proyek tersebut adalah data-data pengendalian berupa kurva S. Data pengendalian tersebut diolah dengan EVM, ESM, KEVM untuk mengetahui perbandingan antara ketiga metode prediksi durasi tersebut. Hasil prediksi dari ketiga metode diuji dengan Mean Absolute Percentage Error (MAPE). Hasil dari penelitian ini menunjukkan bahwa KEVM dapat mengurangi kesalahan setelah dilakukan Kalman Filter pada perkiraan durasi menggunakan Earned Value Method. Prediksi durasi ESM menghasilkan nilai MAPE yang paling kecil dari ketiga metode.

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 Peramalan Harga Komoditas Dengan Menggunakan Metode Arima-Garch

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 113-124
Author(s):  
Ulil Azmi ◽  
Wawan Hafid Syaifudin
Keyword(s):  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Data History

Emas, Tembaga dan Minyak merupakan jenis komoditas yang banyak diincar oleh para investor untuk menanamkan modal dengan cara melakukan investasi pada jenis komoditas tersebut. Prediksi harga komoditas sangat bermanfaat bagi investor untuk melihat prospek investasi komoditas pada suatu perusahaan di masa yang akan datang. Harga komoditas memiliki karakteristik data yang tidak stabil atau sering disebut volatilitas. Untuk mengatasi permasalahan tersebut, dilakukan peramalan dengan metode ARIMA dan ARIMA-GARCH. Dipilih dua metode tersebut karena dua metode ini cocok untuk meramalkan sesuatu yang memiliki data history yang kuat. Metode ARIMA ARCH-GARCH lebih cocok digunakan untuk data-data yang memliki volatilitas yang tinggi atau terdapat heteroskedastisitas pada residual data, sehingga hasil prediksi lebih akurat. Hal ini dibuktikan dengan nilai AIC lebih kecil dari pada hanya menggunakan metode ARIMA. Model terbaik untuk komoditas Emas adalah ARIMA(0,1,1) – GARCH(1,1) sedangkan komoditas tembaga memiliki model terbaik yaitu ARIMA(2,1,2) – GARCH(1,1) dan komoditas minyak yaitu ARIMA(1,1,1) – GARCH(0,1). Nilai MAPE (Mean Absolute Percentage Error) untuk masing-masing komoditas berturut-turut adalah 1,113; 0,542 dan 1,158 untuk Emas, Tembaga dan Minyak.

scholarly journals Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 41
Author(s):  
Jaka Darma Jaya
Keyword(s):  
Trend Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Polynomial Trend ◽  
Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri.  Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional.  Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis.  Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error).  Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12;  MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

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