Maximizing Efficiency in Telemedicine: An IoT-Based Artificial Intelligence Optimization Framework for Health Analysis

Saad Rasool; Aftab Tariq; Yawar Hayat

Saad Rasool
Aftab Tariq
Yawar Hayat

Keywords: Internet of things, artificial intelligence, telemedicine, health monitoring, data analysis

Abstract

This paper delves into a comprehensive exploration of health IoT architecture and its implementation technologies, considering both theoretical and practical aspects. The study emphasizes the theoretical importance and practical applicability of the research. Key areas covered include cloud fusion health IoT architecture, multimodal information acquisition within the health IoT perception layer, multi-level service quality assurance based on human LAN in health IoT, and emotional perception and interaction within the health IoT context. In terms of health IoT architecture, the proposal introduces a cloud-converged approach that deeply integrates the health cloud platform and perception layer. This integration involves utilizing various communication technologies to enhance user experience, fostering closer connections between health IoT applications and individuals. The paper details basic concepts and main components of multimodal sensing information collection, outlining the design and implementation of a health monitoring cloud robotics platform. This platform involves robotics-based multimodal data sensing and aggregation, as well as the collection of comfortable and sustainable physiological signals through smart clothing. The feasibility and performance of the Quality of Service (QoS) framework proposed in this paper are validated through computer simulations. Migration learning is employed for emotion data labeling, and continuous conditional random fields are utilized for emotion identification based on data from smartphones and smart clothing. The paper concludes with decision layer fusion for emotion classification prediction.

References

Aliverti, A. (2017). Wearable technology: role in respiratory health and disease. Breathe, 13(2), e27-e36.
Barbosa Pereira, C., Yu, X., Czaplik, M., Blazek, V., Venema, B., & Leonhardt, S. (2017). Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects. Journal of clinical monitoring and computing, 31(6), 1241-1254.
Betti, S., Lova, R. M., Rovini, E., Acerbi, G., Santarelli, L., Cabiati, M., ... & Cavallo, F. (2017). Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers. IEEE Transactions on Biomedical Engineering, 65(8), 1748-1758.
Boursalie, O., Samavi, R., & Doyle, T. E. (2018). Machine learning and mobile health monitoring platforms: a case study on research and implementation challenges. Journal of Healthcare Informatics Research, 2(1), 179-203.
Chakraborty, S., Bhatt, V., & Chakravorty, T. (2020). Big-data, iot wearable and mhealth cloud platform integration triads-a logical way to patient-health monitoring. International Journal of Engineering and Advanced Technology, 9(3), 388-394.
Chen, J., Wang, W., Zhou, Y., Ahmed, S. H., & Wei, W. (2021). Exploiting 5G and blockchain for medical applications of drones. IEEE Network, 35(1), 30-36.
Chen, M., Ma, Y., Li, Y., Wu, D., Zhang, Y., & Youn, C. H. (2017). Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems. IEEE Communications Magazine, 55(1), 54-61.
Chen, M., Ma, Y., Song, J., Lai, C. F., & Hu, B. (2016). Smart clothing: Connecting human with clouds and big data for sustainable health monitoring. Mobile Networks and Applications, 21, 825-845.
Dey, N., Ashour, A. S., Shi, F., Fong, S. J., & Sherratt, R. S. (2017). Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Transactions on Consumer Electronics, 63(4), 442-449.
Friedl, K. E. (2018). Military applications of soldier physiological monitoring. Journal of Science and Medicine in Sport, 21(11), 1147-1153.
Giorgi, G., Galli, A., & Narduzzi, C. (2020). Smartphone-based IOT systems for personal health monitoring. IEEE Instrumentation & Measurement Magazine, 23(4), 41-47.
Goldsack, J. C., Coravos, A., Bakker, J. P., Bent, B., Dowling, A. V., Fitzer-Attas, C., ... & Dunn, J. (2020). Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). NPJ Digital Medicine, 3(1), 55.
Guo, M., Wang, Z., Yang, N., Li, Z., & An, T. (2018). A multisensor multiclassifier hierarchical fusion model based on entropy weight for human activity recognition using wearable inertial sensors. IEEE Transactions on Human-Machine Systems, 49(1), 105-111.
Gupta, P. K., Maharaj, B. T., & Malekian, R. (2017). A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres. Multimedia Tools and Applications, 76, 18489-18512.
Hassan, M. K., El Desouky, A. I., Badawy, M. M., Sarhan, A. M., Elhoseny, M., & Gunasekaran, M. (2019). EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm. Neural Computing and Applications, 31, 1275-1300.
Ke, Q., Zhang, J., Wei, W., Połap, D., Woźniak, M., Kośmider, L., & Damaševĭcius, R. (2019). A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert systems with applications, 126, 218-232.
Khan, S., Parkinson, S., Grant, L., Liu, N., & Mcguire, S. (2020). Biometric systems utilising health data from wearable devices: applications and future challenges in computer security. ACM Computing Surveys (CSUR), 53(4), 1-29.
Levett, D. Z. H., Jack, S., Swart, M., Carlisle, J., Wilson, J., Snowden, C., ... & Perioperative Exercise Testing and Training Society (POETTS. (2018). Perioperative cardiopulmonary exercise testing (CPET): consensus clinical guidelines on indications, organization, conduct, and physiological interpretation. British Journal of Anaesthesia, 120(3), 484-500.
Li, A., Riddell, M. C., Potashner, D., Brown, R. E., & Aronson, R. (2019). Time lag and accuracy of continuous glucose monitoring during high intensity interval training in adults with type 1 diabetes. Diabetes technology & therapeutics, 21(5), 286-294.
Muneer, A., Fati, S. M., & Fuddah, S. (2020). Smart health monitoring system using IoT based smart fitness mirror. Telkomnika (Telecommunication computing electronics and control), 18(1), 317-331.
Orujov, F., Maskeliūnas, R., Damaševičius, R., & Wei, W. J. A. S. C. (2020). Fuzzy based image edge detection algorithm for blood vessel detection in retinal images. Applied Soft Computing, 94, 106452.
Qi, S., Lu, Y., Wei, W., & Chen, X. (2020). Efficient data access control with fine-grained data protection in cloud-assisted IIoT. IEEE Internet of Things Journal, 8(4), 2886-2899.
Rasool, S., Ghafoor, A., & Fareed, Z. (2021). Forecasting the Trends and Patterns of Crime in San Francisco using Machine Learning Model. International Journal of Scientific & Engineering Research, 12(6).
Satija, U., Ramkumar, B., & Manikandan, M. S. (2017). Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring. IEEE Internet of Things Journal, 4(3), 815-823.
Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019). Wearable sensors for monitoring the physiological and biochemical profile of the athlete. NPJ Digital Medicine, 2(1), 72.
Sheth, A., Jaimini, U., & Yip, H. Y. (2018). How will the internet of things enable augmented personalized health?. IEEE intelligent systems, 33(1), 89-97.
Triloka, J., Senanayake, S. A., & Lai, D. (2017). Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles. Neural Computing and Applications, 28, 65-77.
Vanrenterghem, J., Nedergaard, N. J., Robinson, M. A., & Drust, B. (2017). Training load monitoring in team sports: a novel framework separating physiological and biomechanical load-adaptation pathways. Sports Medicine, 47, 2135-2142.
Wang, W., Gong, Z., Ren, J., Xia, F., Lv, Z., & Wei, W. (2020). Venue topic model–enhanced joint graph modelling for citation recommendation in scholarly big data. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 20(1), 1-15.
Wei, W., Ke, Q., Nowak, J., Korytkowski, M., Scherer, R., & Woźniak, M. (2020). Accurate and fast URL phishing detector: a convolutional neural network approach. Computer Networks, 178, 107275.
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017). How consumer physical activity monitors could transform human physiology research. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 312(3), R358-R367.
Wu, Q., Qiao, Y., Guo, R., Naveed, S., Hirtz, T., Li, X., ... & Ren, T. L. (2020). Triode-mimicking graphene pressure sensor with positive resistance variation for physiology and motion monitoring. ACS Nano, 14(8), 10104-10114.
Wu, W., Pirbhulal, S., Zhang, H., & Mukhopadhyay, S. C. (2018). Quantitative assessment for self-tracking of acute stress based on triangulation principle in a wearable sensor system. IEEE Journal of Biomedical and Health Informatics, 23(2), 703-713.
Xu, B., Li, L., Hu, D., Wu, B., Ye, C., & Cai, H. (2018). Healthcare data analysis system for regional medical union in smart city. Journal of Management Analytics, 5(4), 334-349.
Xu, H., Li, P., Yang, Z., Liu, X., Wang, Z., Yan, W., ... & Zhang, Z. (2020). Construction and application of a medical-grade wireless monitoring system for physiological signals at general wards. Journal of Medical Systems, 44(10), 1-15.
Yang, T., Jiang, X., Zhong, Y., Zhao, X., Lin, S., Li, J., ... & Zhu, H. (2017). A wearable and highly sensitive graphene strain sensor for precise home-based pulse wave monitoring. ACS Sensors, 2(7), 967-974.