Exercises Intensity Estimation Based on the Physical Activities Healthcare System

Li, Zhi

doi:10.1109/cmc.2009.68

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…This underlines the need to monitor exercise intensity in real time. HR can be used as an indicator of exercise intensity [7], according to the formula outlined by Fox and Haskell [8].…”

Section: Introductionmentioning

confidence: 99%

Integrated Wearable System for Monitoring Heart Rate and Step during Physical Activity

Prawiro

Yeh

Chou

et al. 2016

Mobile Information Systems

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This paper integrates a heart rate (HR) monitoring system with step counter for use during physical activities. Novel step counter algorithm has been developed to enable the highly accurate detection of step. The proposed system comprises a wireless wearable device, a smartphone, and a remote server. Data transmission between a wearable device and a smartphone is conducted via Bluetooth low energy (BLE). An indirect contact measurement method has also been devised to eliminate the need for direct contact electrodes and likelihood of skin irritation. The proposed system is compact, lightweight, and comfortable to wear. A smartphone application provides the interface for the display of data related to HR, step count (SC), exercise intensity, speed, distance, and calories burned, as well as waveforms related to ECG and step cycle. ECG peak detection algorithm achieved accuracy of 99.7% using the MIT-BIH ST Change Database. Accuracy of 98.89% was achieved for HR and 98.96% for SC at treadmill speeds of 1.8 to 9.0 km/h.

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“…This underlines the need to monitor exercise intensity in real time. HR can be used as an indicator of exercise intensity [7], according to the formula outlined by Fox and Haskell [8].…”

Section: Introductionmentioning

confidence: 99%

Integrated Wearable System for Monitoring Heart Rate and Step during Physical Activity

Prawiro

Yeh

Chou

et al. 2016

Mobile Information Systems

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“…For example, walking at a certain speed may result in acceleration outputs similar to that of walking at the same speed while carrying a load or going uphill, although the energy expenditure is different. Under such circumstances, sensors that measure human physiological responses, such as heart-rate (10, 15, 16, 18, 21, 38, 41, 58, 59, 68), respiration (32, 43), or an armband (2, 4, 12, 25, 31, 35) consisting of heat flux, galvanic skin response, and skin temperature sensors, have been studied to determine if these additional sensors improve the estimation of the energy expenditure. For example, combining the measurement of heart-rate with acceleration estimates the PAEE more precisely (R 2 = 0.81, RMSE = 0.64 METs) than the PAEE estimate using a single hip accelerometer (R 2 = 0.41, RMSE =1.22 METs) (59).…”

Section: Body-worn Sensorsmentioning

confidence: 99%

“…As statistical measures, time-domain features provide insight into the following aspects of the measured signal: general description of the raw signal, such as the mean (6, 8, 32, 42, 43) and standard deviation (6, 8, 32, 43, 54);representation of the signal strength, such as accelerometer counts (13, 17, 44, 50, 56), signal power (52), log energy (41) and peak-to-peak amplitude (6, 8);distributions of the signal, such as 10th, 25th, 50th (median), 75th, 90th percentiles (32, 43, 57) and interquartile range (42, 52);measures of the signal probability distribution, such as kurtosis (52), skewness (52) and coefficient of variation (CV) (17, 52, 54);and others, such as peak intensity (52), zero crossings (54), autocorrelation (57), and cross-correlation (6, 43). …”

Section: Feature Generationmentioning

confidence: 99%

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Computational Methods for Estimating Energy Expenditure in Human Physical Activities

Liu

Gao

Freedson

2012

Medicine &Amp; Science in Sports &Amp; Exercise

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Accurate and reliable methods for assessing human physical activity energy expenditure (PAEE) are informative and essential for understanding individual behaviors and quantifying the impact of physical activity (PA) on disease, PA surveillance and for examining determinants of PA in different populations. This paper reviews recent advances in the estimation of PAEE, in three interrelated areas: 1) types of sensors worn by human subjects, 2) features extracted from the measured sensor signals, and 3) modeling techniques to estimate the PAEE using these features. The review illustrates three directions in the PAEE studies, and provides recommendations for future research, with the aim to produce valid, reliable, and accurate assessment of PAEE from wearable sensors.

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“…Analysis of heart rate or ECG morphology is useful to detect heart disease. Other information related to the person's health can also be estimated from ECG, such as respiration [4], stress level [5], energy expenditure [6], activity intensity [7], and activity classification [8]. Thus, daily monitoring of ECG at home is important to assess the person's health.…”

Section: Introductionmentioning

confidence: 99%

Improvement of human identification accuracy by wavelet of peak-aligned ECG

Fernando

Morikawa

2015

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)

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In this paper, a novel method of human identification using electrocardiogram (ECG) is proposed. In the method, while normalizing RR interval, in addition to normalized signal where time interval of P wave, Q wave, R wave, S wave relatively to R wave is unaligned, normalized signal where time interval of those peaks is aligned is also generated. Wavelet transform is then applied to both normalized signals and feature vector is extracted from their wavelet coefficients. ECG data are collected from 10 subjects using a pair of dry electrodes which are held by two fingers. Experiment results show that adding wavelet of peak-aligned ECG improves the classification accuracy, where the maximum accuracy is 100%, 97%, and 90% for data measured in more than 20 seconds, 5 seconds, and 3 seconds respectively.

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Exercises Intensity Estimation Based on the Physical Activities Healthcare System

Cited by 5 publications

References 3 publications

Integrated Wearable System for Monitoring Heart Rate and Step during Physical Activity

Integrated Wearable System for Monitoring Heart Rate and Step during Physical Activity

Computational Methods for Estimating Energy Expenditure in Human Physical Activities

Improvement of human identification accuracy by wavelet of peak-aligned ECG

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