Abstract
Ambient intelligence (AmI), a new paradigm in artificial intelligence, seeks to enhance people's abilities by utilizing sensitive digital surroundings that are responsive to human requirements, routines, activities, and emotions. With the use of perceptive communications that are intrusive, covert, and proactive, future society will be able to communicate with humans and machines in novel ways. Such AmI technology is a result of cutting-edge interaction paradigms. An appropriate choice for developing a variety of workable solutions, notably in the field of healthcare. To provide the necessary context for the scientific community, this survey will explore the development of AmI approaches in the healthcare industry. We will discuss the infrastructure and technology required to implement AmI's vision, such as smart environments and wearable medical technologies. The most recent artificial intelligence (AI) development approaches used to produce AmI systems in the healthcare area will be outlined. making use of a variety of learning strategies (to learn from user interaction), reasoning techniques (to reason about the aims and objectives of users), and planning approaches (for organising activities and interactions). We'll also go over the possible advantages of AmI technology for those with various long-term physical, mental, or emotional conditions. In order to determine new avenues for future studies, we will showcase some of the successful case studies in the field and examine current and upcoming difficulties.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
European Commission Eurostat: Causes of death statistics 2011. Available from: http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Causes_of_death_statistics.
Sahoo, P. K., Mishra, S., Panigrahi, R., Bhoi, A. K., & Barsocchi, P. (2022). An improvised deep-learning-based mask R-CNN model for laryngeal cancer detection using CT images. Sensors, 22(22), 8834.
Srinivasu, P. N., Sandhya, N., Jhaveri, R. H., & Raut, R. (2022). From blackbox to explainable AI in healthcare: Existing tools and case studies. Mobile Information Systems, 20. Article ID 8167821. https://doi.org/10.1155/2022/8167821
Mishra, S., Thakkar, H. K., Singh, P., & Sharma, G. (2022). A decisive metaheuristic attribute selector enabled combined unsupervised-supervised model for chronic disease risk assessment. Computational Intelligence and Neuroscience.
Weiser, M. (1993). Some computer science issues in ubiquitous computing. Special issue on computer augmented environments: Back to the real world. Communications of the ACM, 36(7), 75–84.
Tapia, D. I., Abraham, A., Corchado, J. M., & Alonso, R. S. (2010). Agents and ambient intelligence: Case studies. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-009-0006-2
Aarts, E., & Roovers, R. (2003). Embedded system design issues in ambient intelligence. In T. Basten, M. Geilen, & H. D. Groot (Eds.), Ambient intelligence: Impact on embedded system design (pp. 11–29). Kluwer.
Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. (2011, April). Body area networks: A survey. Mobile Networks and Applications, 16(2), 171–193. https://doi.org/10.1007/s11036-010-0260-8.
Latré, B., Braem, B., Moerman, I., Blondia, C., & Demeester, P. (2011, January). A survey on wireless body area networks. Wireless Networks, 17(1), 1–18.
Lyytinen, K., & Yoo, Y. (2002). Issues and challenges in ubiquitous computing. Communications of the ACM, 45(12), 63–65.
Sivani, T., & Mishra, S. (2022). Wearable devices: Evolution and usage in remote patient monitoring system. In Connected e-Health (pp. 311–332). Springer.
Mohapatra, S. K., Mishra, S., Tripathy, H. K., & Alkhayyat, A. (2022). A sustainable data-driven energy consumption assessment model for building infrastructures in resource constraint environment. Sustainable Energy Technologies and Assessments, 53, 102697
Phillips Research. (2007). Ambient intelligence: Changing lives for the better. www.research.phillips.com/.
Rech, J., & Althoff, K.-D. (2004). Artificial intelligence and software engineering: Status and future trends. Themenschwerpunkt K & SE, KI, 3, 5–11.
Mishra, S., Jena, L., Tripathy, H. K., & Gaber, T. (2022). Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment. PloS One, 17(8), e0272383.
Guleria, P., Ahmed, S., Alhumam, A., & Srinivasu, P. N. (2022). Empirical study on classifiers for earlier prediction of COVID-19 infection cure and death rate in the Indian states. Healthcare, 10(1), 85. https://doi.org/10.3390/healthcare10010085
Praveen, S. P., Jyothi, V. E., Anuradha, C., VenuGopal, K., Shariff, V., & Sindhura, S. (2022). Chronic kidney disease prediction using ML-based neuro-fuzzy model. International Journal of Image and Graphics, 2340013. https://doi.org/10.1142/S0219467823400132
Gao, T., Massey, T., Selavo, L., Crawford, D., Rong Chen, B., Lorincz, K., Shnayder, V., Hauenstein, L., Dabiri, F., Jeng, J., Chanmugam, A., White, D., Sarrafzadeh, M., & Welsh, M. (2007, September). The advanced health and disaster aid network: A light-weight wireless medical system for triage. IEEE Transactions on Biomedical Circuits and Systems, 1(3), 203–216.
He, D., Chen, C., Chan, S., Bu, J., & Vasilakos, A. (2012, July). Retrust: Attack-resistant and lightweight trust management for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, 16(4), 623–632.
Pauwels, E., Salah, A., & Tavenard, R. (2007, October). Sensor networks for ambient intelligence. In Proceedings of IEEE 9th Workshop Multimedia Signal Processing, October 2007, pp. 13–16.
Mishra, S., Panda, A., & Tripathy, K. H. (2018). Implementation of re-sampling technique to handle skewed data in tumor prediction. Journal of Advanced Research in Dynamical and Control Systems, 10(14), 526–530.
Guo, W. W., Healy, W. M., & Zhou, M. (2011, March). Wireless mesh networks in intelligent building automation control: A survey. International Journal of Intelligent Control Systems, 16(1), 28–36.
Mishra, S., Dash, A., & Mishra, B. K. (2020). An insight of Internet of Things applications in pharmaceutical domain. In Emergence of pharmaceutical industry growth with industrial IoT approach (pp. 245–273). Academic Press.
Mishra, S., Tripathy, H. K., & Acharya, B. (2021). A precise analysis of deep learning for medical image processing. In Bio-inspired recomputing (pp. 25–41). Springer.
Wu, G., & Xue, S. (2008). Portable pre impact fall detector with inertial sensors. IEEE Transactions on Neural Systems Rehabilitation Engineering, 16(2), 178–183 [Pub Med] [Google Scholar].
Lai, C., Chang, S., Chao, H., & Huang, Y. (2011). Detection of cognitive injured body region using multiple trickily accelerometers for elderly falling. IEEE Sensors Journal, 11(3), 763–770 [Google Scholar].
Zhuang, X., Huang, J., Potamianos, G., & Hasegawa-Johnson, M. (2009). Acoustic fall detection using Gaussian mixture models and gem super-vectors. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009 (pp. 69–72). IEEE [Google Scholar].
Alwan, M., Rajendran, P., Kell, S., Mack, D., Dalai, S., & Wolfe, M., & Felder, R. (2006). A smart and passive floor-vibration based fall detector for elderly. In 2006 2nd International Conference on Information and Communication Technologies, ICTTA’06 (Vol. 1, pp. 1003–1007). IEEE [Google Scholar].
Mukherjee, D., Tripathy, H. K., & Mishra, S. (2021). Scope of medical bots in clinical domain. In Technical advancements of machine learning in healthcare (pp. 339–363). Springer.
Shi, G., Chan, C., Li, W., Leung, K., Zou, Y., & Jin, Y. (2009). Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier. Sensors, 9(5), 495–503 [Google Scholar].
Wu, W., Au, L., Jordan, B., Stathopoulos, T., Batalin, M., Kaiser, W., Vahdatpour, A., Sarrafzadeh, M., Fang, M., & Chodosh, J. (2008). The smartcane system: An assistive device for geriatrics. In International Conference on Body Area Networks (pp. 1–4) [Google Scholar].
Haux, R. (2006). Individualization, globalisation and health about sustainable information technologies and the aim of medical informatics. International Journal of Medical Informatics, 75, 795–808 [PubMed] [Google Scholar].
Jena, K. C., Mishra, S., Sahoo, S., & Mishra, B. K. (2017, January). Principles, techniques and evaluation of recommendation systems. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1–6). IEEE.
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of Neuro Engineering and Rehabilitation, 9(1), 21 [Online]. Available: http://www.jneuroengrehab.com/content/9/1/21 [PMC free article] [PubMed] [Google Scholar].
Jarochowski, B. P., Shin, S., Ryu, D., & Kim, H. (2007). Ubiquitous rehabilitation center: An implementation of a wireless sensor network based rehabilitation management system. In Proceedings of the 2007 International Conference on Convergence Information Technology, ser. ICCIT‘07 (pp. 2349–2358). IEEE Computer Society, Washington, DC, USA. [Online]. https://doi.org/10.1109/ICCIT.2007.383 [Google Scholar].
Piotrowicz, E., Jasionowska, A., Banaszak-Bednarczyk, M., Gwilkowska, J., & Piotrowicz, R. (2012). ECG telemonitoring during home-based cardiac rehabilitation in heart failure patients. Journal of Telemedicine and Telecare, 18(4), 193–197 [PubMed] [Google Scholar].
Helmer, A., Song, B., Ludwig, W., Schulze, M., Eichelberg, M., Hein, A., Tegtbur, U., Kayser, R., Haux, R., & Marschollek, M. (2010). A sensor-enhanced health information system to support automatically controlled exercise training of COPD patients. In 2010 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), March, pp. 1–6. NO PERMISSIONS [Google Scholar].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Samanta, S., Mitra, A., Mishra, S., Parvathaneni, N.S. (2023). Ambient Healthcare: A New Paradigm in Medical Zone. In: Barsocchi, P., Parvathaneni, N.S., Garg, A., Bhoi, A.K., Palumbo, F. (eds) Enabling Person-Centric Healthcare Using Ambient Assistive Technology. Studies in Computational Intelligence, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-031-38281-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-38281-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38280-2
Online ISBN: 978-3-031-38281-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)