Survival study on deep learning techniques for IoT enabled smart healthcare system

Munnangi, Ashok Kumar; UdhayaKumar, Satheeshwaran; Ravi, Vinayakumar; Ramesh, S.; Kannan, S.

doi:10.1007/s12553-023-00736-4

Cited by 20 publications

(3 citation statements)

References 82 publications

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“…With the help of this tool, users may see trends, keep tabs on their progress, make informed decisions regarding their wellbeing and health. [21]…”

Section: G Monitoring Historical Datamentioning

confidence: 99%

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Godara,

Kaur,

Baliyan

et al. 2024

IJRASET

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This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

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“…With the help of this tool, users may see trends, keep tabs on their progress, make informed decisions regarding their wellbeing and health. [21]…”

Section: G Monitoring Historical Datamentioning

confidence: 99%

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Godara,

Kaur,

Baliyan

et al. 2024

IJRASET

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show abstract

“…So, it failed to perform effective data analysis. Moran Autocorrelation and Regression-depend Elman Recurrent Neural Network (MAR-ERNN) was introduced by [20] to predict abnormalities for activity recognition by means of Artificial Intelligence (AI) in the IoT healthcare. But it failed to recognize the disease with minimal time consumption.…”

Section: Related Workmentioning

confidence: 99%

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

Akilandeswari,

Sivakumar,

Alkahtani

et al. 2024

CMC

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In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process. Here, the input dataset the continuous beat-to-beat heart data, triaxial accelerometer data, and psychological characteristics were acquired from IoT wearable devices. To attain highly accurate Smart Healthcare Monitoring with less time, Partial Least Square helps extract the dimensionality-reduced features. After that, with these resulting features, SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead. Experimental evaluation is carried out for factors such as time, overhead, and false positive rate accuracy concerning several instances. The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

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“…As a result, ambient environments seek to identify and offer personalized services to citizens who interact with and collaborate with the environment. An ambient assisted environment is also automated, using the Internet of Things (IoT) [ 14 ] framework, capable of responding to the needs of its occupants, offering comfort and wellbeing.…”

Section: Literature Reviewmentioning

confidence: 99%

A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology

Ahmed

Srinivasu

Alhumam

2023

Sensors

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Ambient assisted technology (AAT), which has the potential to enhance patient care and productivity and save costs, has emerged as a strategic goal for developing e-healthcare in the future. However, since the healthcare sensor must be interconnected with other systems at different network tiers, distant enemies have additional options to attack. Data and resources integrated into the AAT are vulnerable to security risks that might compromise privacy, integrity, and availability. The gadgets and network sensor devices are layered with clinical data since they save personal information such as patients’ names, addresses, and medical histories. Considering the volume of data, it is difficult to ensure its confidentiality and security. As sensing devices are deployed over a wider region, protecting the privacy of the collected data becomes more difficult. The current study proposes a lightweight security mechanism to ensure the data’s confidentiality and integrity of the data in ambient-assisted technology. In the current study, the data are encrypted by the master node with adequate residual energy, and the master node is responsible for encrypting the data using the data aggregation model using a node’s key generated using an exclusive basis system and a Chinese remainder theorem. The integrity of the data is evaluated using the hash function at each intermediate node. The current study defines the design model’s layered architecture and layer-wise services. The model is further analyzed using various evaluation metrics, such as energy consumption, network delay, network overhead, time in generating hash, tradeoff between encryption and decryption, and entropy metrics. The model is shown to adequately perform on all measures considered in the analysis.

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Survival study on deep learning techniques for IoT enabled smart healthcare system

Cited by 20 publications

References 82 publications

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology

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