Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types

Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

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A Study on the Effect of Contact Pressure during Physical Activity on Photoplethysmographic Heart Rate Measurements

Sensors ◽

10.3390/s20185052 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5052

Author(s):

Francesco Scardulla ◽

Leonardo D’Acquisto ◽

Raffaele Colombarini ◽

Sijung Hu ◽

Salvatore Pasta ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Contact Pressure ◽

Pearson Correlation ◽

Wearable Sensors ◽

Percentage Error ◽

Average Percentage ◽

The Individual ◽

Average Percentage Error ◽

Contact Pressures

Heart rate (HR) as an important physiological indicator could properly describe global subject’s physical status. Photoplethysmographic (PPG) sensors are catching on in field of wearable sensors, combining the advantages in costs, weight and size. Nevertheless, accuracy in HR readings is unreliable specifically during physical activity. Among several identified sources that affect PPG recording, contact pressure (CP) between the PPG sensor and skin greatly influences the signals. Methods: In this study, the accuracy of HR measurements of a PPG sensor at different CP was investigated when compared with a commercial ECG-based chest strap used as a test control, with the aim of determining the optimal CP to produce a reliable signal during physical activity. Seventeen subjects were enrolled for the study to perform a physical activity at three different rates repeated at three different contact pressures of the PPG-based wristband. Results: The results show that the CP of 54 mmHg provides the most accurate outcome with a Pearson correlation coefficient ranging from 0.81 to 0.95 and a mean average percentage error ranging from 3.8% to 2.4%, based on the physical activity rate. Conclusion: Authors found that changes in the CP have greater effects on PPG-HR signal quality than those deriving from the intensity of the physical activity and specifically, the individual best CP for each subject provided reliable HR measurements even for a high intensity of physical exercise with a Bland–Altman plot within ±11 bpm. Although future studies on a larger cohort of subjects are still needed, this study could contribute a profitable indication to enhance accuracy of PPG-based wearable devices.

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Actuation and dynamic mechanical characteristics of a core free flat dielectric electro-active polymer soft actuator

JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES ◽

10.15282/jmes.14.4.2020.08.0582 ◽

2020 ◽

Vol 14 (4) ◽

pp. 7396-7404

Author(s):

Abdul Malek Abdul Wahab ◽

Emiliano Rustighi ◽

Zainudin A.

Keyword(s):

Theoretical Model ◽

Resonance Frequency ◽

Dynamic Testing ◽

Experimental Results ◽

Percentage Error ◽

Initial Tension ◽

Average Percentage ◽

Soft Actuator ◽

Average Percentage Error ◽

Mechanical Dynamics

Various complex shapes of dielectric electro-active polymer (DEAP) actuator have been promoted for several types of applications. In this study, the actuation and mechanical dynamics characteristics of a new core free flat DEAP soft actuator were investigated. This actuator was developed by Danfoss PolyPower. DC voltage of up to 2000 V was supplied for identifying the actuation characteristics of the actuator and compare with the existing formula. The operational frequency of the actuator was determined by dynamic testing. Then, the soft actuator has been modelled as a uniform bar rigidly fixed at one end and attached to mass at another end. Results from the theoretical model were compared with the experimental results. It was found that the deformation of the current actuator was quadratic proportional to the voltage supplied. It was found that experimental results and theory were not in good agreement for low and high voltage with average percentage error are 104% and 20.7%, respectively. The resonance frequency of the actuator was near 14 Hz. Mass of load added, inhomogeneity and initial tension significantly affected the resonance frequency of the soft actuator. The experimental results were consistent with the theoretical model at zero load. However, due to inhomogeneity, the frequency response function’s plot underlines a poor prediction where the theoretical calculation was far from experimental results as values of load increasing with the average percentage error 15.7%. Hence, it shows the proposed analytical procedure not suitable to provide accurate natural frequency for the DEAP soft actuator.

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PP15 Predicting variations of calls to an ambulance service in the UK caused by circulating infections using-deep learning methods

Emergency Medicine Journal ◽

10.1136/emermed-2019-999abs.15 ◽

2019 ◽

Vol 36 (10) ◽

pp. e7.2-e7

Author(s):

Thilo Reich ◽

Marcin Budka

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Prehospital Care ◽

Machine Learning Algorithms ◽

Ambulance Service ◽

Percentage Error ◽

Learning Methods ◽

Average Percentage ◽

Average Percentage Error ◽

The Uk

BackgroundDigital patient records in the ambulance service have opened up new opportunities for prehospital care. Previously it was demonstrated that prehospital pyrexia numbers are linked to an increase in overall calls to the ambulance service. This study aims to predict the future number of calls using deep-learning methods.MethodsTemperature readings for 280,447 patients were generously provided by the South Western Ambulance Service Trust. The data covered the time between 05/01/2016 and 30/04/2017 with overall 44,472 patients being pyretic. A rolling window of 10 days was applied to daily sums for both pyretic and apyretic patients. These windows were used as input features to train machine-learning algorithms predicting the number of calls 10 days ahead. Algorithms tested include Linear Regression (LR), basic Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. A genetic approach was used to optimise the architecture, in which parameters were randomly modified and over several generations the best performing algorithm will be selected to be further manipulated. To assess performance the Mean Average Percentage Error (MAPE) was used.ResultsThe initial analysis showed that the total patient number and pyretic patient numbers are correlated. The best performing algorithms with varying numbers of hidden units had the following MAPE in comparison to simple LR: LR=19.4%, LSTM (104 units) = 6.1%, RNN (79 units)=6.01%, GRU (80 units)=5.97%.ConclusionsThese preliminary results suggest that deep-learning methods allow to predict the variations in total number of calls caused by circulating infections. Further investigations will aim to confirm these findings. Once fully verified these algorithms could play a major role in operational planning of any ambulance service by predicting increases in demand.

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Comparison of Otolith Readability and Reproducibility of Counts of Translucent Zones Using Different Otolith Preparation Methods for Four Endemic Labeobarbus Species in Lake Tana, Ethiopia

Water ◽

10.3390/w11071336 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1336 ◽

Cited By ~ 2

Author(s):

Gebremedhin ◽

Bekaert ◽

Getahun ◽

Bruneel ◽

Anteneh ◽

...

Keyword(s):

Management Strategies ◽

Tropical Fish ◽

Percentage Error ◽

Systematic Bias ◽

Preparation Technique ◽

Lake Tana ◽

Preparation Methods ◽

Average Percentage ◽

Average Percentage Error ◽

Preparation Techniques

The analysis of fish age data is vital for the successful conservation of fish. Attempts to develop optimal management strategies for effective conservation of the endemic Labeobarbus species are strongly affected by the lack of accurate age estimates. Although methodological studies are key to acquiring a good insight into the age of fishes, up to now, there have not been any studies comparing different methods for these species. Thus, this study aimed at determining the best method for the endemic Labeobarbus species. Samples were collected from May 2016 to April 2017. Asteriscus otoliths from 150 specimens each of L. intermedius, L. tsanensis, L. platydorsus, and L. megastoma were examined. Six methods were evaluated; however, only three methods resulted in readable images. The procedure in which whole otoliths were first submerged in water, and subsequently placed in glycerol to take the image (MO1), was generally best. Except for L. megastoma, this method produced the clearest image as both the coefficient of variation and average percentage error between readers were lowest. Furthermore, except for L. megastoma, MO1 had high otolith readability and no systematic bias. Therefore, we suggest that MO1 should be used as the standard otolith preparation technique for the first three species, while for L. megastoma, other preparation techniques should be evaluated. This study provides a reference for researchers from Africa, particularly Ethiopia, to develop a suitable otolith preparation method for the different tropical fish species.

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Physical Activity Monitor Accuracy for Overground Walking and Free-Living Conditions Among Pregnant Women

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2019-0059 ◽

2020 ◽

Vol 3 (2) ◽

pp. 100-109

Author(s):

Christopher P. Connolly ◽

Jordana Dahmen ◽

Robert D. Catena ◽

Nigel Campbell ◽

Alexander H.K. Montoye

Keyword(s):

Physical Activity ◽

Pregnant Women ◽

Living Conditions ◽

Overground Walking ◽

Third Trimester ◽

Free Living ◽

Activity Monitors ◽

Step Counting ◽

Physical Activity Monitors ◽

Free Living Conditions

Purpose: We aimed to determine the step-count validity of commonly used physical activity monitors for pregnancy overground walking and during free-living conditions. Methods: Participants (n = 39, 12–38 weeks gestational age) completed six 100-step overground walking trials (three self-selected “normal pace”, three “brisk pace”) while wearing five physical activity monitors: Omron HJ-720 (OM), New Lifestyles 2000 (NL), Fitbit Flex (FF), ActiGraph Link (AG), and Modus StepWatch (SW). For each walking trial, monitor-recorded steps and criterion-measured steps were assessed. Participants also wore all activity monitors for an extended free-living period (72 hours), with the SW used as the criterion device. Mean absolute percent error (MAPE) was calculated for overground walking and free-living protocols and compared across monitors. Results: For overground walking, the OM, NL, and SW performed well (<5% MAPE) for normal and brisk pace walking trials, and also when trials were analyzed by actual speeds. The AG and FF had significantly greater MAPE for overground walking trials (11.9–14.7%). Trimester did affect device accuracy to some degree for the AG, FF, and SW, with error being lower in the third trimester compared to the second. For the free-living period, the OM, NL, AG, and FF significantly underestimated (>32% MAPE) actual steps taken per day as measured by the criterion SW (M [SD] = 9,350 [3,910]). MAPE for the OM was particularly high (45.3%). Conclusion: The OM, NL, and SW monitors are valid measures for overground step-counting during pregnancy walking. However, the OM and NL significantly underestimate steps by second and third trimester pregnant women in free-living conditions.

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Stock-specific assessment of precise age and growth in the long-whiskered catfish Sperata aor from the Ganges River

Marine and Freshwater Research ◽

10.1071/mf19315 ◽

2020 ◽

Vol 71 (12) ◽

pp. 1693

Author(s):

Aafaq Nazir ◽

M. Afzal Khan

Keyword(s):

Age And Growth ◽

Percentage Error ◽

Specific Information ◽

Ganges River ◽

Average Percentage ◽

Marginal Increment ◽

Hard Structures ◽

Average Percentage Error ◽

Anthropogenic Threats ◽

The Ganges

Sustainable management of the long-whiskered catfish Sperata aor (Hamilton, 1822) in the Ganges River justifies precise stock-specific information on age and growth. The aim of the present study was to estimate the age and growth of three stocks, namely Narora–Kanpur, Varanasi and Bhagalpur, of S. aor from the Ganges River. Among the hard structures chosen for analysis, vertebrae provided precise age estimates up to 9 years of age in all the three stocks of S. aor based on average percentage error. Edge analysis of vertebrae and marginal increment ratio analysis of sectioned otoliths showed annulus formation once per year during April–June. The von Bertalanffy growth rates showed significant differences between the sexes and among the stocks. The growth coefficient k (0.24–0.30 year–1) showed rapid growth relative to asymptotic length (L∞) in all three stocks. The growth performance index was nearly the same for all three stocks. The results of the present study can be used in formulating scientifically sound management policies in view of anthropogenic threats to the populations of S. aor from the Ganges River.

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A COMPARISON OF FORECASTING THE RESULTS OF ROAD TRANSPORTATION NEEDS

Transport ◽

10.3846/16484142.2012.666763 ◽

2012 ◽

Vol 27 (1) ◽

pp. 73-78 ◽

Cited By ~ 6

Author(s):

Bogna Mrówczyńska ◽

Karolina Łachacz ◽

Tomasz Haniszewski ◽

Aleksander Sładkowski

Keyword(s):

Harmonic Analysis ◽

Percentage Error ◽

Road Transportation ◽

Artificial Immune ◽

Mathematical Methods ◽

Average Percentage ◽

The Past ◽

Forecasting Methods ◽

Average Percentage Error

Determining the size and quality of transport needs would not be possible without adequate forecasting based on the sales volume or demand for this service from the past periods. Traditional forecasting methods use econometric models that may be subject to serious errors. The use of the methods taking into account the variability of the studied phenomena or more advanced mathematical methods enables to minimize the error. Various methods of artificial intelligence such as a neural network, fuzzy sets, genetic algorithms, etc., have been recently successfully applied. The aim of this paper is to compare three forecasting methods that can be used for predicting the volume of road freight. The article deals with the effectiveness of three prediction methods, namely Winter's method for seasonal problems – a multiplicative version, harmonic analysis and harmonic analysis aided by the artificial immune system. The effectiveness of prediction was counted using MAPE errors (main average percentage error). The results of calculations were compared and the best example was presented.

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The Design of Portable Battery Charging Devices Using Motorcycle Wheel Round

International Journal of Electrical, Energy and Power System Engineering ◽

10.31258/ijeepse.2.2.16-19 ◽

2019 ◽

Vol 2 (2) ◽

pp. 16-19

Author(s):

Arlenny

Keyword(s):

Power Consumption ◽

Electric Power ◽

Electrical Power ◽

Electrical Energy ◽

Threshold Value ◽

Electric Power Consumption ◽

Percentage Error ◽

Average Percentage ◽

Power Load ◽

Average Percentage Error

This research aims to the development of reader equipment as well as control the load limitation of electric power using Atmega 8535 microcontroller. In the development of equipment of reading and controlling electrical energy consumptions, the modified KWH (Kilo Watt Hour) meter was used by placing the optocoupler sensor as the enumerator indicator the electric power consumption on the disc. Atmega 8535 microcontroller was used to control and limitation of the electric power consumption. In this research, the measuring and control system was developed to record the amount of electrical power load used, and it can be used as an alternative to the current divider for the achievement of the efficiency of practical electrical energy consumption. The results of the measurement comparison between the measured load and the output load tended to be stable with an average percentage error of 6.3%, and it was still below the optimum threshold value of the error factor, which around 10%. Therefore, results of testing developed equipment KWH digital meter using Atmega 8535 microcontroller that was produced a good performance.

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An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i3.pp1245-1250 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1245

Author(s):

Adi Kurniawan ◽

Anisa Harumwidiah

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Global Solar Radiation ◽

Percentage Error ◽

Average Percentage ◽

Daily Average ◽

Artificial Neural ◽

Average Percentage Error ◽

The City

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

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SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Forecasting ◽

10.3390/forecast4010005 ◽

2022 ◽

Vol 4 (1) ◽

pp. 72-94

Author(s):

Roberto Vega ◽

Leonardo Flores ◽

Russell Greiner

Keyword(s):

Machine Learning ◽

The United States ◽

Sir Model ◽

Percentage Error ◽

Infected People ◽

Average Percentage ◽

Interpretable Model ◽

The Government ◽

Average Percentage Error ◽

Government Level

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.

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