Patient health monitoring system using smart IoT devices for medical emergency services

Sreelakshmy, R.; Sruthy, R; Rajeshwari, R; Thyla, B.; M, Suresh Kumar

doi:10.1109/icses55317.2022.9914385

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Sreelakshmy and et al designs other monitoring system by information internet of thing (IoT) based health monitoring and medical information system that compromise technologies like wireless networks and mobile computing, aiming to deliver patients with remotely receivable sensing, sound, image, and video multimedia information, enabled medical diagnosis accuracy the quality of clinical services. The Information is transmitted using sensor network technologies [20]. The big issue to be considered in Smart Human Health Monitoring System by using information internet of thing (IoT) is privacy and security [21].…”

Section: Introductionmentioning

confidence: 99%

Low-cost detection, transmission, and central monitoring of patients’ vital signs in rural areas: digitizing the Ethiopian rural healthcare system

Berihun,

Kassaw,

Wosene

et al. 2024

Preprint

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Background monitoring vital signs is essential for identifying patient issues. However, there are deaths that could have been avoided due to insufficient clinical monitoring and diagnosis delay. In Ethiopia, patients in rural areas are facing challenges like limited system interoperability and overcrowded facilities. The objective of this project is to design a cost-effective system for recording, transmitting, and monitoring remotely located patients’ vital signs from the center. Methods radio transmitter and receiver, temperature, oxygen saturation and heart rate sensors, Arduino, keypad, alarm/light indicators and GSM module components were collected. Proteus and Arduino software were used to design and simulate the recording, transmitting and receiving modules individually. The individual modules were assembled. The design was implemented by connecting the components on breadboard. Finally, the assembled system was tested for its functionality. Results a device that can record and transmit patient’s room number, bed number, vital sign and other important parameters to the central vital sign monitoring location was designed. The designed system was simulated using proteus software. The simulated design was implemented practically using the mentioned components. The result of practical testing showed that the vital signs and other parameters were recorded successfully by the recorder module and displayed at monitoring location. Furthermore, the device was able to send notification to the physician’s mobile phone via means of messages. Therefore, it is possible to monitor the remotely located patient’s vital sign at the central location using cost effective central monitoring system which can be implemented in low resource setting areas like Ethiopia.

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Section: Introductionmentioning

confidence: 99%

Low-cost detection, transmission, and central monitoring of patients’ vital signs in rural areas: digitizing the Ethiopian rural healthcare system

Berihun,

Kassaw,

Wosene

et al. 2024

Preprint

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IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis

Alharbe,

Almalki

2024

Multimed Tools Appl

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Deep learning and the Internet of Things (IoT) are revolutionizing the healthcare industry. This study explores the potential commercial transformation resulting from IoT-enabled healthcare systems that use deep learning for patient monitoring and diagnosis. Wearables, smart sensors, and internet-connected medical devices allow doctors to monitor patients' vital signs, activities, and physiological traits in real time. However, these devices generate vast and complex data, making analysis and diagnosis challenging. Deep learning models are well-suited to analyze this growing volume of medical data. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can automatically recognize complex patterns and relationships in sensor data, electronic health records, and patient-reported information. This capability aids clinical professionals in diagnosing illnesses, identifying warning signs, and tailoring treatments. This paper describes a Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -based IoT-enabled healthcare system that performs feature extraction, classification, prediction, and data preparation. Additionally, it addresses interpretability issues, privacy concerns, and resource limitations of deep learning models in real-time healthcare settings. The study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -powered IoT-based healthcare solutions, such as real-time patient monitoring, disease detection, risk prediction, and therapy optimization. These techniques can improve the quality, cost, and outcomes of healthcare. Combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with IoT can significantly enhance healthcare by improving disease detection, personalized treatment, and patient monitoring through connected devices and powerful analytics.

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IoT based Patients Monitoring System in Healthcare Service

Hanumanthakari

Pullela

Bhukya³

et al. 2022

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)

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Patient health monitoring system using smart IoT devices for medical emergency services

Cited by 4 publications

References 8 publications

Low-cost detection, transmission, and central monitoring of patients’ vital signs in rural areas: digitizing the Ethiopian rural healthcare system

Low-cost detection, transmission, and central monitoring of patients’ vital signs in rural areas: digitizing the Ethiopian rural healthcare system

IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis

IoT based Patients Monitoring System in Healthcare Service

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