Autonomous Healthcare Systems: Deep Learning-Based IoT Solutions for Continuous Monitoring and Adaptive Treatment
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Abstract
Autonomous healthcare systems are a big change in the way medicine is done. They use deep learning algorithms and Internet of Things (IoT) devices to keep an eye on patients all the time and change their treatment as needed. This new way of doing things could change the way patients are cared for by giving real-time information and personalized treatments. With the help of deep learning, these systems can look at huge amounts of data produced by IoT sensors, like those in medical implants and smart tech, to spot small changes in health and spot problems before they get worse.Autonomous healthcare systems are based on their ability to constantly gather and analyze data from a variety of sources, such as vital signs, biological markers, and patient-reported complaints. Deep learning algorithms are very important to this process because they can find complicated patterns and connections in the data. These algorithms can get useful information from raw sensor data by using methods like CNN, MobileNet, and InceptionV3. This algorithm lets healthcare professionals move quickly and proactively.Also, independent healthcare systems are made to change based on the wants and interests of each patient by using personalized treatment plans. These systems can improve results and patient happiness by constantly checking how patients respond to actions and making changes to treatment plans on the fly. Adding IoT devices also makes it easier for patients and healthcare workers to talk to each other, allowing for distant discussions and quick solutions. The automated healthcare systems are a revolutionary way to provide medical care. They use deep learning-based IoT solutions to keep an eye on patients all the time and adjust their treatment as needed. These systems might be able to improve patient results, lower healthcare costs, and raise general quality of life by using the power of data-driven insights and individual actions
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References
Poongodi, T.; Krishnamurthi, R.; Indrakumari, R.; Suresh, P.; Balusamy, B. Wearable devices and IoT. In A Handbook of Internet of Things in Biomedical and Cyber Physical System; Springer: Berlin/Heidelberg, Germany, 2020; pp. 245–273.
Ashfaq, Z.; Rafay, A.; Mumtaz, R.; Zaidi, S.M.H.; Saleem, H.; Zaidi, S.A.R.; Mumtaz, S.; Haque, A. A review of enabling technologies for Internet of Medical Things (IoMT) Ecosystem. Ain Shams Eng. J. 2022, 13, 101660.
Borkar, P., Wankhede, V.A., Mane, D.T. et al. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Comput (2023).
Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023).
Limkar, Suresh, Ashok, Wankhede Vishal, Singh, Sanjeev, Singh, Amrik, Wagh, Sharmila K. & Ajani, Samir N.(2023) A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1597–1611
Razzak, M.I.; Naz, S.; Zaib, A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps: Automation of Decision Making; Springer: Berlin/Heidelberg, Germany, 2018; pp. 323–350.
Li, J.; Jin, K.; Zhou, D.; Kubota, N.; Ju, Z. Attention mechanism-based CNN for facial expression recognition. Neurocomputing 2020, 411, 340–350.
Ali, O.; Abdelbaki, W.; Shrestha, A.; Elbasi, E.; Alryalat, M.A.A.; Dwivedi, Y.K. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. J. Innov. Knowl. 2023, 8, 100333.
Ghosh, A.M.; Halder, D.; Hossain, S.A. Remote health monitoring system through IoT. In Proceedings of the 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 13–14 May 2016; pp. 921–926.
Valsalan, P.; Baomar, T.A.B.; Baabood, A.H.O. IoT based health monitoring system. J. Crit. Rev. 2020, 7, 739–743.
Durán-Vega, L.A.; Santana-Mancilla, P.C.; Buenrostro-Mariscal, R.; Contreras-Castillo, J.; Anido-Rifón, L.E.; García-Ruiz, M.A.; Montesinos-López, O.A.; Estrada-González, F. An IoT system for remote health monitoring in elderly adults through a wearable device and mobile application. Geriatrics 2019, 4, 34.
Hamim, M.; Paul, S.; Hoque, S.I.; Rahman, M.N.; Baqee, I.A. IoT based remote health monitoring system for patients and elderly people. In Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 10–12 January 2019; pp. 533–538.
Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., &Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559
Ali, A.; Almaiah, M.A.; Hajjej, F.; Pasha, M.F.; Fang, O.H.; Khan, R.; Teo, J.; Zakarya, M. An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network. Sensors 2022, 22, 572.
Almaiah, M.A.; Hajjej, F.; Ali, A.; Pasha, M.F.; Almomani, O. A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS. Sensors 2022, 22, 1448.
Sujith, A.; Sajja, G.S.; Mahalakshmi, V.; Nuhmani, S.; Prasanalakshmi, B. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neurosci. Inform. 2022, 2, 100028.
Ghayvat, H.; Pandya, S.; Patel, A. Deep learning model for acoustics signal based preventive healthcare monitoring and activity of daily living. In Proceedings of the 2nd International Conference on Data, Engineering and Applications (IDEA), Bhopal, India, 28–29 February 2020; pp. 1–7.
Cimtay, Y.; Ekmekcioglu, E. Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition. Sensors 2020, 20, 2034.
Maier, M.; Elsner, D.; Marouane, C.; Zehnle, M.; Fuchs, C. DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Montreal, QC, Canada, 13–17 May 2019; pp. 2108–2110.
Pacheco, A.G.; Krohling, R.A. An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification. IEEE J. Biomed. Health Inform. 2021, 25, 3554–3563.
Rghioui, A.; Lloret, J.; Sendra, S.; Oumnad, A. A smart architecture for diabetic patient monitoring using machine learning algorithms. Healthcare 2020, 8, 348.
Tiwari, S.; Jain, A.; Sapra, V.; Koundal, D.; Alenezi, F.; Polat, K.; Alhudhaif, A.; Nour, M. A smart decision support system to diagnose arrhythymia using ensembledConvNet and ConvNet-LSTM model. Expert Syst. Appl. 2023, 213, 118933.
Ed-Driouch, C.; Mars, F.; Gourraud, P.A.; Dumas, C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine Intelligence. Sensors 2022, 22, 8313.
Chandrasekhar, N.; Peddakrishna, S. Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes 2023, 11, 1210.
Mirjalali, S.; Peng, S.; Fang, Z.; Wang, C.H.; Wu, S. Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of COVID-19. Adv. Mater. Technol. 2022, 7, 2100545.