Abstract
The Internet of Things is the emanating modernist communication paradigm that has evolved to fasten itself with various technologies by providing real-time measurements and observations of the environment, physiological and psychological parameters, the physical world itself. It facilitates various applications to serve humankind. Artificial Intelligence is the vastly applied machine intelligence that can trigger and empower an IoT device. The learning methods of AI with respect to an IoT application result in a prodigious device. Among many colossal applications, the functional biomedical application is widely researched and has already been developed. Motivated by this amalgamation’s come-out, this chapter focuses on IoT and its transformation of the biomedical industry by providing wearable technology, connected appliances/hospital machines, tracking biomedical performances, smart security systems, etc. Due to the body of the application, exploitation of private data, security breaching, power requirements, power consumption, and scalability are a few challenges that have been discussed. The outcome of this chapter is the pure integration of IoT and AI in biomedical systems. The structure of IoT and its union with AI along with the working model is discussed. Case studies and developments of IoT and AI in a biomedical application are highlighted. This chapter also provides the solution to many of the challenges listed above and throws light on implementing the solution as a possible research potential. The greatest challenge after World War II, i.e., COVID-19 pandemic and outstanding research that is currently serving many hospitals using AI as a tool for diagnosing and monitoring this global health crisis will be briefed. It can be interpreted after the global pandemic that AI and its blend with IoT-based approaches towards healthcare will change the world that it has been working and provide many logical solutions including remote health monitoring, disease prediction and diagnosis, and treatment.
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References
Lee H et al (2017) Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module. Sci Adv 3(3):e1601314
Chen G et al (2020) Prediction of chronic kidney disease using adaptive hybridized deep convolutional neural network on the Internet of Medical Things platform. IEEE Access 8:100497–100508
Sarosh P et al (2021) Secret sharing-based personal health records management for the Internet of Health Things. Sustain Cities Soc 74:103129
Cai Q et al (2019) A survey on multimodal data-driven smart healthcare systems: approaches and applications. IEEE Access 7:133583–133599
Rashid M, Hamid A, Parah SA (2019) Analysis of streaming data using big data and hybrid machine learning approach. In: Handbook of multimedia information security: techniques and applications. Springer, Cham, pp 629–643
Parah SA et al (2020) Efficient security and authentication for edge-based internet of medical things. IEEE Internet Things J 8:15652–15662
Wei Y et al (2020) A review of algorithm & hardware design for AI-based biomedical applications. IEEE Trans Biomed Circuits Syst 14(2):145–163
Steenkiste TV et al (2019) Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks. IEEE J Biomed Health Inform 23(6):2354–2364
Korkalainen H et al (2020) Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea. IEEE J Biomed Health Inform 24(7):2073–2081
Paranjape K et al (2020) Short keynote paper: Mainstreaming personalized healthcare–transforming healthcare through new era of artificial intelligence. IEEE J Biomed Health Inform 24(7):1860–1863
Gull S et al (2020) A self-embedding technique for tamper detection and localization of medical images for smart-health. Multimed Tools Appl 80(19):29939–29964
Chen Z et al (2018) An energy-efficient ECG processor with weak-strong hybrid classifier for arrhythmia detection. IEEE Trans Circuits Syst II Express Briefs 65(7):948–952
Mondal S, Agarwal K, Rashid M (2019) Deep learning approach for automatic classification of x-ray images using convolutional neural network. In: 2019 Fifth international conference on image information processing (ICIIP). IEEE, New York
Amin-Naji M et al (2019) Alzheimer’s disease diagnosis from structural MRI using Siamese convolutional neural network. In: 2019 4th International conference on pattern recognition and image analysis (IPRIA), Tehran, Iran, pp 75–79. https://doi.org/10.1109/PRIA.2019.8786031
Qadri YA et al (2020) The future of healthcare Internet of Things: a survey of emerging technologies. IEEE Commun Surv Tutorials 22(2):1121–1167
Shah AA et al (2020) Efficient image encryption scheme based on generalized logistic map for real time image processing. J Real-Time Image Process 17(6):2139–2151
Panwar M et al (2020) PP-Net: a deep learning framework for PPG-based blood pressure and heart rate estimation. IEEE Sensors J 20(17):10000–10011
Zhou Z et al (2020) Human activity recognition based on improved Bayesian convolution network to analyze health care data using wearable IoT device. IEEE Access 8:86411–86418
Ismail WN et al (2020) CNN-based health model for regular health factors analysis in Internet-of-Medical Things environment. IEEE Access 8:52541–52549
Qian X et al (2020) Wearable computing with distributed deep learning hierarchy: a study of fall detection. IEEE Sensors J 20(16):9408–9416
Zhang T et al (2020) A joint deep learning and Internet of Medical Things driven framework for elderly patients. IEEE Access 8:75822–75832
Ascioglu G et al (2020) Design of a wearable wireless multi-sensor monitoring system and application for activity recognition using deep learning. IEEE Access 8:169183–169195
Bianchi V et al (2019) IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet Things J 6(5):8553–8562
Rashid M, Singh H, Goyal V (2020) The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—a systematic review. Expert Syst 37(6):e12644
Chang W et al (2019) A deep learning-based intelligent medicine recognition system for chronic patients. IEEE Access 7:44441–44458
Gong C et al (2020) Intelligent cooperative edge computing in Internet of Things. IEEE Internet Things J 7(10):9372–9382
WHO Director-General says be prepared as coronavirus declared a pandemic. Riverine Herald, 12 Mar 2020. www.riverineherald.com.au/news/2020/03/12/1079364/who-director-general-says-be-prepared. Accessed 21 Feb 2021
Mertz L (2020) AI-driven COVID-19 tools to interpret, quantify lung images. IEEE Pulse 11(4):2–7
Gong C, Lin F et al (2020) Intelligent cooperative edge computing in Internet of Things. IEEE Internet Things J 7(10):9372–9382
AI in IoT Market | Growth, trends and forecasts (2021–2026). www.mordorintelligence.com, www.mordorintelligence.com/industry-reports/ai-in-iot-market
Rashid M et al (2019) Novel big data approach for drug prediction in health care systems. In: 2019 International conference on automation, computational and technology management (ICACTM). IEEE, New York
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Rajeswari, S.V.K.R., Ponnusamy, V. (2022). Internet of Things and Artificial Intelligence in Biomedical Systems. In: Parah, S.A., Rashid, M., Varadarajan, V. (eds) Artificial Intelligence for Innovative Healthcare Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-96569-3_8
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