Home Healthcare Technologies and Services: Heart-Rate Fetus Monitoring System Using an MCU ESP8266 Node

Skrivanos, Anastasios G.; Chronopoulos, Spyridon K.; Kosma, Evangelia I.; Kouretas, Ioannis; Peppas, Kostas P.

doi:10.1109/pacet56979.2022.9976348

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Some research has focused on utilizing Artificial Intelligence (AI) techniques, although more work is required in this field. Furthermore, there is a need for ample data for testing and training the machine and deep learning algorithms [19], [20]. This study tackles these problems by employing an AI algorithm for FHR detection dependent on PCG signals and providing sufficient data for training and evaluating the proposed work.…”

Section: Introductionmentioning

confidence: 99%

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

ABBURI¹,

Hatai²,

Samanta³

2023

Preprint

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<p>This work utilizes a wearable phonocardiogram belt for acquiring signals from remote areas. Four sequential methodologies are undergone in the proposed work for resilience, and effectiveness. Intelligent, and accurate FHR monitoring. At first, the noise and artifacts are removed by performing two levels pre-processing methodology method. The low frequency noises are removed by Chebyshev II High Pass Filter and high frequency noises are removed by a hybrid of EC2EMDAN- PS-MODWT filters respectively. In the next step, the denoised signals are segmented for reducing the complexity in which the segmentation is performed using Multi Agent Deep Q-Learning (MA-DQL) algorithm based on several constraints. Further, the segmented signal is provided for reducing the redundancies in cardiac cycles using Artificial Humming Bird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm named Variational Auto Encoder-General Adversarial Networks (VAE-GAN) for analyzing the signals with better visual interpretation in 3D space. Finally, utilizing the 3D spectrogram analysis the feature extraction and classification is taken place by adopting a hybrid of Bidirectional Gated Recurrent Unit (BiGRU) and Multi Boosted Capsule Network (MBCapsNet) into three classes such as normal (110-160 BPM), abnormal (above 180 BPM), and Suspicious (fluctuates between normal and abnormal). The proposed work is implemented and simulated in the MATLAB R2020a tool and the performance is validated by adopting effective validation metrics. The outcomes demonstrate that the suggested work performs better than the current works. </p>

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Section: Introductionmentioning

confidence: 99%

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

ABBURI¹,

Hatai²,

Samanta³

2023

Preprint

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VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

Chamanthi,

et al. 2024

2024 International Conference on Inventive Computation Technologies (ICICT)

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Integration of Modbus Ethernet Communication for Real-Time Electrical Power Consumption, Temperature, and Humidity Monitoring System

Le,

Nguyen,

Bui

et al. 2024

Preprint

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Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, and power factor. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. In this paper, we propose a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by the FINECO, the KLEA 220P three-phase multifunction meter by the KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are firstly utilized. Finally, the collected data of voltage, current, electrical consumption, temperature, and humidity can be stored on the SD card and transmitted to the cloud for real-time monitoring on mobile devices facilitated by the ESP-WROOM-32 microcontroller from the Espressif system. The obtained results showed that the temperature and the humidity changed gradually with time and remained within the standard range of temperature and relative humidity.

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Home Healthcare Technologies and Services: Heart-Rate Fetus Monitoring System Using an MCU ESP8266 Node

Cited by 3 publications

References 39 publications

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

Integration of Modbus Ethernet Communication for Real-Time Electrical Power Consumption, Temperature, and Humidity Monitoring System

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