Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor

Shandhi, Md. Mobashir Hasan; Bartlett, William; Heller, J. Alex; Etemadi, Mozziyar; Young, Aaron; Plötz, Thomas; Inan, Omer T.

doi:10.1109/jbhi.2020.3009903

Cited by 29 publications

(9 citation statements)

References 48 publications

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“…In parallel, we also attached the wearable patch biosensor to the subject’s mid-sternum and collected single-lead ECG, two sets of multiwavelength PPGs (red, infrared [IR], and green), and triaxial seismocardiogram (SCG, not used in this study), sampled at 500, 67, and 1000 Hz, respectively. The hardware used in the biosensor is almost identical to that reported in our previous work [ 8 , 10 , 23 ] except for the addition of the PPG modules and the change in form factor. The ECG analog front-end (AFE) and the accelerometer AFE (for SCG) remain the same.…”

Section: Methodsmentioning

confidence: 99%

“…This device is also equipped with wireless capabilities (i.e., Bluetooth and Wi-Fi) for transmitting data. However, in this study, data were stored in the Secure Digital card and later retrieved by a custom-built software application as in previous work [ 8 , 10 , 23 ]. The battery life of the device at the full sample rates of all sensors is up to 60 h. The front and lateral views of the device are shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Thus, central pulse oximeters offer a promising approach for ambulatory outpatient monitoring and in detecting acute hypoxemia events. Furthermore, chest-based pulse oximeters might be advantageous for their synergistic incorporation with other cardiac monitoring methods—such as electrocardiography and seismocardiography—for a more holistic understanding of cardiac functions [ 7 , 8 , 9 , 10 ]. Nevertheless, a few major challenges such limitations to the reflectance design, the presence of respiratory artifacts, and the malperfusion of the sternum pose difficulties to the adoption of chest-based approaches.…”

Section: Introductionmentioning

confidence: 99%

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Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum

et al. 2021

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In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum

et al. 2021

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“…Very recently, a different type of experimentation was reported by Shandhi et al [24]. In this work, the authors used the cardio-electromechanical and environmental sensing measurements to predict the V O .…”

Section: Exercise-based Modelsmentioning

confidence: 99%

“…The results from two different sample sizes (n = 12,480 and n = 20,868) showed reasonable predictability using the bigger sample with R = 0.87. Very recently, a different type of experimentation was reported by Shandhi et al [24]. In this work, the authors used the cardio-electromechanical and environmental sensing measurements to predict the .…”

Section: Exercise-based Modelsmentioning

confidence: 99%

A Short Review on the Machine Learning-Guided Oxygen Uptake Prediction for Sport Science Applications

2021

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In recent years, the rapid improvement in computing facilities combined with that achieved in algorithms and the immense amount of available data led to a great interest in machine learning (ML), which is a subset of artificial intelligence. Nowadays, the ML technique is used mostly in all applications for various purposes, whereby ML will be possible to learn from data, predict, identify patterns, and make decisions. In this regard, the ML was successfully used to predict the oxygen uptake during physical activity without the need for complicated procedures used in the direct measurement. Accordingly, in the present work, the state-of-art and recent advances related to the oxygen uptake prediction using ML were presented. Various exercise and non-exercise predictive models also were discussed.

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