Design and Realization of Blood Oxygen and Heart Rate Sensor Nodes in Wireless Body Area Network

Cao, Tianao; Tao, Lin; Liú, Dan; Sun, Jinwei

doi:10.1109/icaica50127.2020.9182480

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…Using the classical theory of Beer-Lambert law for the estimation of the Oxygen saturation, 16 we compute for each pair of signals the Oxygen saturation, the PPG signal of 890 [nm] is chosen as infrared, and the PPG signal of 660 [nm] as the red signal, then we apply: R = ln Iout I0 P P G660nm…”

Section: Classical Estimation For Oxygen Saturation and Hemoglobin Le...mentioning

confidence: 99%

Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

Alvarez,

Arzuaga,

Sierra

2023

Emerging Topics in Artificial Intelligence (ETAI) 2023

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Measuring Hemoglobin (Hb) levels is required for the assessment of different health conditions, such as anemia, a condition where there are insufficient healthy red blood cells to carry enough oxygen to the body’s tissues. Measuring Hb levels requires the extraction of a blood sample, which is then sent to a laboratory for analysis. This is an invasive procedure that may add challenges to the continuous monitoring of Hb levels. Noninvasive techniques, including imaging and photoplethysmography (PPG) signals combined with machine learning techniques, are being investigated for continuous measurements of Hb. However, the availability of real data to train the algorithms is limited to establishing a generalization and implementation of such techniques in healthcare settings. In this work, we present a computational model based on Monte Carlo simulations that can generate multispectral PPG signals that cover a broad range of Hb levels. These signals are then used to train a Deep Learning (DL) model to estimate hemoglobin levels. Through this approach, valuable insights about the relationships between PPG signals, oxygen saturation, and Hb levels are learned by the DL model. The signals were generated by propagating a source in a volume that contains the skin tissue properties and the target physiological parameters. The source consisted of plane waves using the 660 nm and 890 nm wavelengths. A range of 6 g/dL to 18 dL Hb values was used to generate 468 PPGs to train a Convolutional Neural Network (CNN). The initial results show high accuracy in detecting low levels of Hb. To the best of our knowledge, the complexity of biological interactions involved in measuring hemoglobin levels has yet to be fully modeled. The presented model offers an alternative approach to studying the effects of changes in Hb levels on the PPGs signal morphology and its interaction with other physiological parameters that are present in the optical path of the measured signals.

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Section: Classical Estimation For Oxygen Saturation and Hemoglobin Le...mentioning

confidence: 99%

Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

Alvarez,

Arzuaga,

Sierra

2023

Emerging Topics in Artificial Intelligence (ETAI) 2023

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“…Such a threshold also determines the acquired voltage value to decide whether it is a waveform. When a valid waveform is received, half of the amplitude (i.e., 50%) of the signal is regarded as a feature point [44], as displayed in Figure 7. Consequently, the IBI is calculated by the time of two adjacent feature points, and the BPM is obtained accordingly.…”

Section: Design Of Intelligent Walking Stickmentioning

confidence: 99%

An AIoT-Based Assistance System for Visually Impaired People

Li,

Xie,

Chen

et al. 2023

Electronics

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In this work, an assistance system based on the Artificial Intelligence of Things (AIoT) framework was designed and implemented to provide convenience for visually impaired people. This system aims to be low-cost and multi-functional with object detection, obstacle distance measurement, and text recognition achieved by wearable smart glasses, heart rate detection, fall detection, body temperature measurement, and humidity-temperature monitoring offered by an intelligent walking stick. The total hardware cost is approximately $66.8, as diverse low-cost sensors and modules are embedded. Meanwhile, a voice assistant is adopted, which helps to convey detection results to users. As for the performance evaluation, the accuracies of object detection and text recognition in the wearable smart glasses experiments are 92.16% and 99.91%, respectively, and the maximum deviation rate compared to the mobile app on obstacle distance measurement is 6.32%. In addition, the intelligent walking stick experiments indicate that the maximum deviation rates compared to the commercial devices on heart rate detection, body temperature measurement, and humidity-temperature monitoring are 3.52%, 0.19%, and 3.13%, respectively, and the fall detection accuracy is 87.33%. Such results demonstrate that the proposed assistance system yields reliable performances similar to commercial devices and is impressive when considering the total cost as a primary concern. Consequently, it satisfies the fundamental requirements of daily life, benefiting the safety and well-being of visually impaired people.

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“…LP-WAN networks are used to enable the long range transmission with low transmit power and higher energy efficiency. This technology is especially useful when there is a need to provide long battery lifetime of the sensing devices such as thermometer [ 38 ], HR meter, and oxymeter [ 39 ] that send the measurements with lower data rate over longer ranges. LP-WAN networks that operate in licensed bands have the advantage of the dedicated spectrum which lead to lower interference levels and more reliable communications, which is particularly suitable for critical healthcare applications.…”

Section: Communication Standards In Medical Applicationsmentioning

confidence: 99%

Breaking Barriers in Emerging Biomedical Applications

Katzis

Berbakov

Gardasevic

et al. 2022

Entropy

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The recent global COVID-19 pandemic has revealed that the current healthcare system in modern society can hardly cope with the increased number of patients. Part of the load can be alleviated by incorporating smart healthcare infrastructure in the current system to enable patient’s remote monitoring and personalized treatment. Technological advances in communications and sensing devices have enabled the development of new, portable, and more power-efficient biomedical sensors, as well as innovative healthcare applications. Nevertheless, such applications require reliable, resilient, and secure networks. This paper aims to identify the communication requirements for mass deployment of such smart healthcare sensors by providing the overview of underlying Internet of Things (IoT) technologies. Moreover, it highlights the importance of information theory in understanding the limits and barriers in this emerging field. With this motivation, the paper indicates how data compression and entropy used in security algorithms may pave the way towards mass deployment of such IoT healthcare devices. Future medical practices and paradigms are also discussed.

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Design and Realization of Blood Oxygen and Heart Rate Sensor Nodes in Wireless Body Area Network

Cited by 10 publications

References 3 publications

Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

An AIoT-Based Assistance System for Visually Impaired People

Breaking Barriers in Emerging Biomedical Applications

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