Health Monitoring and Predicting System using Internet of Things &amp; Machine Learning

Rahman, Jenifar; Sanshi, Shridhar; Ahamed, N. Nasurudeen

doi:10.1109/icaccs51430.2021.9441856

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…Currently, many tools for monitoring the patient's physiological state have been developed by conducting significant diagnostic tests with measurements of BPM, SpO2, and IoT-based patient body temperature. [6], [7], [8], [9], [10], [11], [12], [13], [14]. The implementation of the Internet of Things (IoT) in the design of monitoring tools for BPM, SpO2, and patient body temperature provides many benefits including facilitating the performance of Health Workers to monitor body conditions, diagnoses, and prevent patient emergencies remotely in realtime, continuously and requires low energy.…”

Section: Introductionmentioning

confidence: 99%

“…Antonio Iyda Paganelli et al, [7] Antonio Iyda Paganelli et al, designed an effective COVID-19 patient monitoring system using IoT and wireless sensors equipped with early warning with a comprehensive conceptual architecture covering scalability, interoperability, network dynamics, discovery context, reliability, and privacy. used for monitoring the patient's condition and the error value of the system.…”

Section: Introductionmentioning

confidence: 99%

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Design And Development Of Spo2, Bpm, And Body Temperature For Monitoring Patient Conditions In Iot-Based Special Isolation Rooms

Purwitosari,

Irianto,

Triwiyanto

et al. 2023

j.teknokes

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The use of batteries as the main power source in portable equipment systems has several drawbacks, including the percentage of battery power that must be monitored so that the system is always active. Analysis of battery power efficiency is needed to determine the resistance of portable systems. This study makes a portable system for monitoring the condition of patients with infectious diseases in a special isolation room that can measure heart rate, body temperature, and oxygen saturation. The design of this device uses a 2200mAH battery as a power source on the IC TTGO ESP32 to manage data and display measurement results, the MAX30102 sensor to measure oxygen saturation and heart rate, and the MCP9808 sensor to measure body temperature. The design of this device has been tested on respondents aged 25-40 years by placing the sensor on the fingertip then the measurement results are compared with a standard device that has been calibrated. The measurement results show that the device is feasible to use because the measurement error value is ±5%. Testing the efficiency of battery power in normal mode and save mode. In normal mode, the current used in the device is 154.9 mA, while the save mode by not activating the LCD TTGO ESP32 requires a current of 126.7 mA. The results of the analysis show that using the battery in normal mode can activate the device for up to ±14 hours and in save mode for ±17 hours. This designed method is useful for measuring power efficiency in different device modes and the user knows the battery charging time at regular intervals.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design And Development Of Spo2, Bpm, And Body Temperature For Monitoring Patient Conditions In Iot-Based Special Isolation Rooms

Purwitosari,

Irianto,

Triwiyanto

et al. 2023

j.teknokes

View full text Add to dashboard Cite

show abstract

“…The explosive growth of web data and quick development of online media, it is presently a typical exercise for patients to share their perspectives on cure and medications via web-based media stages [3]. Structure of sentiment analysis is shown in fig 1 . The large volume of unstructured printed information which might be used in significant applications including finding antagonistic medication responses aiding determination and treatment decisions, suggesting customized treatment choices, and finding fortunate medication utilization [4]. These applications customarily use organized informa-tion which is hard to produce and restricted in amount [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Elephant Herd Random Forest Machine Learning Approach To Estimate the Sentiment Value of Online Customer Review

Sanshi¹,

Krishna²,

Vatambeti³

2023

IJCDS

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Nowadays, big data has attracted the attention of whole advanced world with its applications and features. Machine learning (ML) models are used to perform the online administrations in good manner .The ML approaches turned into a moving field in analyzing enormous data; consequently the accomplishment of online administrations or business depends on the client audits. Nearly the online customer review contains positive, negative and neutral sentiment value. In marketing system and product development fields the sentiment analysis value prediction has important role. In this paper, a novel Elephant Herd Random Forest Machine Learning (EHRFML) methodology is proposed to compute the sentiment value of online customer review. Moreover, customer review datasets are preprocessed and unwanted information is removed using machine learning approach. Sequentially, the outcomes of proposed system are compared with existing technique using parameters like accuracy, precision, recall, aspect term specification and opinion condition obtained good results by getting high accuracy based on opinion specification.

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Homecare Robotic Systems: Wearable Remote Sensing with Interactive AI Features

Maity,

Maji,

Sil

et al. 2024

2024 IEEE Applied Sensing Conference (APSCON)

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Health Monitoring and Predicting System using Internet of Things & Machine Learning

Cited by 9 publications

References 10 publications

Design And Development Of Spo2, Bpm, And Body Temperature For Monitoring Patient Conditions In Iot-Based Special Isolation Rooms

Design And Development Of Spo2, Bpm, And Body Temperature For Monitoring Patient Conditions In Iot-Based Special Isolation Rooms

A Novel Elephant Herd Random Forest Machine Learning Approach To Estimate the Sentiment Value of Online Customer Review

Homecare Robotic Systems: Wearable Remote Sensing with Interactive AI Features

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