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 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 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 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 Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

Comparison of Energy Expenditure and Step Count Measured by ActiGraph Accelerometers Among Dominant and Nondominant Wrist and Hip Sites

2020 ◽  
Vol 3 (4) ◽  
pp. 315-322
Author(s):  
Kayla J. Nuss ◽  
Nicholas A. Hulett ◽  
Alden Erickson ◽  
Eric Burton ◽  
Kyle Carr ◽  
...  
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Mean Absolute Percentage Error ◽  
Step Count ◽  
Percentage Error ◽  
Test Procedures ◽  
Men And Women ◽  
Step Counts ◽  
Absolute Percentage Error

Objective: To validate and compare the accuracy of energy expenditure (EE) and step counts measured by ActiGraph accelerometers (ACT) at dominant and nondominant wrist and hip sites. Methods: Thirty young adults (15 females, age 22.93 ± 3.30 years) wore four ActiGraph wGT3X accelerometers while walking and running on a treadmill for 7 min at seven different speeds (1.7, 2.5, 3.4, 4.2, 5.0, 5.5, and 6.0 mph). The EE from each ACT was calculated using the Freedson Adult equation, and the “worn on the wrist” option was selected for the wrist data. Indirect calorimetry and manually counted steps were used as criterion measures. Mean absolute percentage error and two one-sided test procedures for equivalence were used for the analyses. Results: All ACTs underestimated the EE with mean absolute percentage errors over 30% for wrist placement and over 20% for hip placement. The wrist-worn ACTs underestimated the step count with mean absolute percentage errors above 30% for both dominant and nondominant placements. The hip-worn ACTs accurately assessed steps for the whole sample and for women and men (p < .001 to .05 for two one-sided tests procedures), but not at speeds slower than 2.0 mph. Conclusion: Neither hip nor wrist placements assess EE accurately. More algorithms and methods to derive EE estimates from wrist-worn ACTs must be developed and validated. For step counts, both dominant and nondominant hip placements, but not wrist placements, lead to accurate results for both men and women.

Energy Expenditure in Playground Games in Primary School Children Measured by Accelerometer and Heart Rate Monitors

2017 ◽  
Vol 27 (5) ◽  
pp. 467-474 ◽  
Author(s):  
Jorge Cañete García-Prieto ◽  
Vicente Martinez-Vizcaino ◽  
Antonio García-Hermoso ◽  
Mairena Sánchez-López ◽  
Natalia Arias-Palencia ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Energy Expenditure ◽  
Primary School ◽  
Correlation Coefficient ◽  
School Children ◽  
Indirect Calorimetry ◽  
Coefficient Of Variation ◽  
Primary School Children ◽  
Heart Rate Monitors

The aim of this study was to examine the energy expenditure (EE) measured using indirect calorimetry (IC) during playground games and to assess the validity of heart rate (HR) and accelerometry counts as indirect indicators of EE in children´s physical activity games. 32 primary school children (9.9 ± 0.6 years old, 19.8 ± 4.9 kg · m-2 BMI and 37.6 ± 7.2 ml · kg-1 · min-1 VO2max). Indirect calorimetry (IC), accelerometry and HR data were simultaneously collected for each child during a 90 min session of 30 playground games. Thirty-eight sessions were recorded in 32 different children. Each game was recorded at least in three occasions in other three children. The intersubject coefficient of variation within a game was 27% for IC, 37% for accelerometry and 13% for HR. The overall mean EE in the games was 4.2 ± 1.4 kcals · min-1 per game, totaling to 375 ± 122 kcals/per 90 min/session. The correlation coefficient between indirect calorimetry and accelerometer counts was 0.48 (p = .026) for endurance games and 0.21 (p = .574) for strength games. The correlation coefficient between indirect calorimetry and HR was 0.71 (p = .032) for endurance games and 0.48 (p = .026) for strength games. Our data indicate that both accelerometer and HR monitors are useful devices for estimating EE during endurance games, but only HR monitors estimates are accurate for endurance games.

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%. 

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