Skip to main content

Accountable, Responsible, Transparent Artificial Intelligence in Ambient Intelligence Systems for Healthcare

  • Chapter
  • First Online:

Abstract

The future due to various socioeconomic reasons will demand an increased need for the extension of rehabilitation in home environments. However, due to the rapid development of the Ambient Intelligent (AmI) systems, various solutions through different approaches could solve problems which have concerned the health industry. However, AmI approaches due to complexity and multidisciplinarity should utilize the right tools in order to become a successful solution in the health sector. AmI consists of two main components. The hardware part which utilizes various sensors (wearable, ambient, contactless). This is combined with an AI part, which utilizes advanced Machine Learning algorithms. Successful AmI systems should follow various criteria. This chapter aims to review the required criteria for the integration of AmI into home-based health and care. A case study is reviewed, which combines and complies with several identified criteria. The system was tested with human subjects it was non-intrusive nor wearable, with a patient centric approach. The system demonstrated encouraging results with high accuracy. Moreover, Accountable, Reliable and Transparent AI was applied successfully to proact individualization and increase the level of trust. Although the AmI systems are promising, research is premature. More systematic research is needed for integration to the healthcare domain.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Vourganas, I., Stankovic, V., Stankovic, L., & Kerr, A. (2019). Factors that contribute to the use of stroke self-rehabilitation technologies: A review. JMIR Biomedical Engineering, 4, e13732. https://doi.org/10.2196/13732

    Article  Google Scholar 

  2. Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation, 9, 21. https://doi.org/10.1186/1743-0003-9-21

    Article  Google Scholar 

  3. (2017). State of the Nation: stroke statistics.

    Google Scholar 

  4. (2018). State of the Nation: stroke statistics.

    Google Scholar 

  5. Szeto, A. (2005). 5 Rehabilitation engineering and assistive technology. In J. D. Enderle, S. M. Blanchard, & J. D. Bronzino (Eds.), Introduction to biomedical engineering (2nd ed., pp. 211–254). Academic Press.

    Chapter  Google Scholar 

  6. Debes, C., Merentitis, A., Sukhanov, S., Niessen, M., Frangiadakis, N., & Bauer, A. (2016). Monitoring activities of daily living in smart homes: understanding human behavior. IEEE Signal Processing Magazine, 33, 81–94. https://doi.org/10.1109/MSP.2015.2503881

    Article  Google Scholar 

  7. Fell, M., Kennard, H., Huebner, G., Nicolson, M., Elam, S., & Shipworth, D. (2017). Energising health: A review of the health and care applications of smart meter data. UCL Energy Institute.

    Google Scholar 

  8. Wang, Y., Chen, Q., Hong, T., & Kang, C. (2018). Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Transactions on Smart Grid, 10(3), 3125–3148. https://doi.org/10.1109/TSG.2018.2818167

    Article  Google Scholar 

  9. Alwateer, M., Almars, A. M., Areed, K. N., Elhosseini, M. A., Haikal, A. Y., & Badawy, M. (2021). Ambient healthcare approach with hybrid whale optimization algorithm and Naïve Bayes classifier. Sensors, 21, 4579. https://doi.org/10.3390/s21134579

    Article  Google Scholar 

  10. Vourganas, I., Stankovic, V., Stankovic, L., & Michala, A. L. (2020). Evaluation of home-based rehabilitation sensing systems with respect to standardised clinical tests. Sensors, 20, 26.

    Article  Google Scholar 

  11. Vourganas, I., Stankovic, V., & Stankovic, L. (2021). Individualised responsible artificial intelligence for home-based rehabilitation. Sensors, 21, 2. https://doi.org/10.3390/s21010002

    Article  Google Scholar 

  12. Haque, A., Milstein, A., & Fei-Fei, L. (2020). Illuminating the dark spaces of healthcare with ambient intelligence. Nature, 585, 193–202. https://doi.org/10.1038/s41586-020-2669-y

    Article  Google Scholar 

  13. A European approach to Artificial intelligence | Shaping Europe’s digital future. Retrieved July 23, 2021, from https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence.

  14. Adams, J. G., & Walls, R. M. (2020). Supporting the health care workforce during the COVID-19 global epidemic. JAMA, 323, 1439. https://doi.org/10.1001/jama.2020.3972

    Article  Google Scholar 

  15. Patel, R. S., Bachu, R., Adikey, A., Malik, M., & Shah, M. (2018). Factors related to physician burnout and its consequences: A review. Behavioral Science, 8, 98. https://doi.org/10.3390/bs8110098

    Article  Google Scholar 

  16. Lyon, M., Sturgis, L., Lendermon, D., Kuchinski, A. M., Mueller, T., Loeffler, P., Xu, H., & Gibson, R. (2015). Rural ED transfers due to lack of radiology services. The American Journal of Emergency Medicine, 33, 1630–1634. https://doi.org/10.1016/j.ajem.2015.07.050

    Article  Google Scholar 

  17. Halpern, N. A., Goldman, D. A., Tan, K. S., & Pastores, S. M. (2016). Trends in critical care beds and use among population groups and Medicare and Medicaid beneficiaries in the United States: 2000–2010. Critical Care Medicine, 44, 1490–1499. https://doi.org/10.1097/CCM.0000000000001722

    Article  Google Scholar 

  18. Halpern, N. A., & Pastores, S. M. (2010). Critical care medicine in the United States 2000–2005: An analysis of bed numbers, occupancy rates, payer mix, and costs. Critical Care Medicine, 38, 65–71. https://doi.org/10.1097/CCM.0b013e3181b090d0

    Article  Google Scholar 

  19. Zhang, L., Hu, W., Cai, Z., Liu, J., Wu, J., Deng, Y., Yu, K., Chen, X., Zhu, L., Ma, J., & Qin, Y. (2019). Early mobilization of critically ill patients in the intensive care unit: A systematic review and meta-analysis. PLoS One, 14, e0223185. https://doi.org/10.1371/journal.pone.0223185

    Article  Google Scholar 

  20. Reiter, A., Ma, A., Rawat, N., Shrock, C., & Saria, S. (2016). Process monitoring in the intensive care unit: Assessing patient mobility through activity analysis with a non-invasive mobility sensor. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, & W. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 (pp. 482–490). Springer International Publishing.

    Chapter  Google Scholar 

  21. Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N. L., Guo, M., Bianconi, G. M., Alahi, A., Lee, J., Campbell, B., Deru, K., Beninati, W., Fei-Fei, L., & Milstein, A. (2019). A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. Npj Digital Medicine, 2, 1–5. https://doi.org/10.1038/s41746-019-0087-z

    Article  Google Scholar 

  22. World Health Organization. (2011). Report on the burden of endemic health care associated infection worldwide. World Health Organization.

    Google Scholar 

  23. Haque, A., Guo, M., Alahi, A., Yeung, S., Luo, Z., Rege, A., Jopling, J., Downing, L., Beninati, W., Singh, A., Platchek, T., Milstein, A., & Fei-Fei, L. (2018). Towards vision-based smart hospitals: A system for tracking and monitoring hand hygiene compliance. ArXiv170800163 Cs.

    Google Scholar 

  24. Singh, A., Haque, A., Alahi, A., Yeung, S., Guo, M., Glassman, J. R., Beninati, W., Platchek, T., Fei-Fei, L., & Milstein, A. (2020). Automatic detection of hand hygiene using computer vision technology. Journal of the American Medical Informatics Association, 27, 1316–1320. https://doi.org/10.1093/jamia/ocaa115

    Article  Google Scholar 

  25. Anderson, O., Davis, R., Hanna, G. B., & Vincent, C. A. (2013). Surgical adverse events: a systematic review. American Journal of Surgery, 206, 253–262. https://doi.org/10.1016/j.amjsurg.2012.11.009

    Article  Google Scholar 

  26. Bonrath, E. M., Dedy, N. J., Gordon, L. E., & Grantcharov, T. P. (2015). Comprehensive surgical coaching enhances surgical skill in the operating room: A randomized controlled trial. Annals of Surgery, 262, 205–212. https://doi.org/10.1097/SLA.0000000000001214

    Article  Google Scholar 

  27. Law, H., Ghani, K., & Deng, J. (2017). Surgeon technical skill assessment using computer vision based analysis. In F. Doshi-Velez, J. Fackler, D. Kale, R. Ranganath, B. Wallace, & J. Wiens (Eds.), Proceedings of the 2nd Machine Learning for Healthcare Conference (pp. 88–99). PMLR.

    Google Scholar 

  28. Greenberg, C. C., Regenbogen, S. E., Lipsitz, S. R., Diaz-Flores, R., & Gawande, A. A. (2008). The frequency and significance of discrepancies in the surgical count. Annals of Surgery, 248(2), 337–341. Retrieved July 23, 2021, from https://pubmed.ncbi.nlm.nih.gov/18650646/

    Article  Google Scholar 

  29. Cima, R. R., Kollengode, A., Clark, J., Pool, S., Weisbrod, C., Amstutz, G. J., & Deschamps, C. (2011). Using a data-matrix-coded sponge counting system across a surgical practice: impact after 18 months. Joint Commission Journal on Quality and Patient Safety, 37, 51–58. https://doi.org/10.1016/s15537250(11)37007-9

    Article  Google Scholar 

  30. Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., & Padoy, N. (2017). A multi-view RGB-D approach for human pose estimation in operating rooms. ArXiv170107372 Cs.

    Google Scholar 

  31. Rich, N. (2017). The impact of working as a medical scribe. The American Journal of Emergency Medicine, 35, 517. https://doi.org/10.1016/j.ajem.2016.12.020

    Article  Google Scholar 

  32. Chiu, C. C., Tripathi, A., Chou, K., Co, C., Jaitly, N., Jaunzeikare, D., Kannan, A., Nguyen, P., Sak, H., Sankar, A., Tansuwan, J., Wan, N., Wu, Y., & Zhang, X. (2018). Speech recognition for medical conversations. ArXiv171107274 Cs Eess Stat

    Google Scholar 

  33. Pranaat, R., Mohan, V., O’Reilly, M., Hirsh, M., McGrath, K., Scholl, G., Woodcock, D., & Gold, J. A. (2017). Use of simulation based on an electronic health records environment to evaluate the structure and accuracy of notes generated by medical scribes: Proof-of-concept study. JMIR Medical Informatics, 5, e30. https://doi.org/10.2196/medinform.7883

    Article  Google Scholar 

  34. Division UP. (2019). World population ageing. UN.

    Google Scholar 

  35. Soh, S. L.-H., Lane, J., Xu, T., Gleeson, N., & Tan, C. W. (2021). Falls efficacy instruments for community-dwelling older adults: a COSMIN-based systematic review. BMC Geriatrics, 21, 21. https://doi.org/10.1186/s12877-020-01960-7

    Article  Google Scholar 

  36. Park, E.-Y., Lee, Y.-J., & Choi, Y.-I. (2018). The sensitivity and specificity of the falls efficacy scale and the activities-specific balance confidence scale for hemiplegic stroke patients. Journal of Physical Therapy Science, 30, 741–743. https://doi.org/10.1589/jpts.28.741

    Article  Google Scholar 

  37. Carlsson, G., Haak, M., Nygren, C., & Iwarsson, S. (2012). Self-reported versus professionally assessed functional limitations in community-dwelling very old individuals. International Journal of Rehabilitation Research. Internationale Zeitschrift für Rehabilitationsforschung. Revue Internationale de Recherches de Réadaptation, 35, 299–304. https://doi.org/10.1097/MRR.0b013e3283544d07

    Article  Google Scholar 

  38. Wang, Z., Yang, Z., & Dong, T. (2017). A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors, 17, E341. https://doi.org/10.3390/s17020341

    Article  Google Scholar 

  39. Katz, S. (1983). Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society, 31, 721–727. https://doi.org/10.1111/j.1532-5415.1983.tb03391.x

    Article  Google Scholar 

  40. Sadeghian, A., Alahi, A., & Savarese, S. (2017). Tracking the untrackable: Learning to track multiple cues with long-term dependencies. ArXiv170101909 Cs.

    Google Scholar 

  41. Halamka, J. D. (2014). Early experiences with big data at an academic medical center. Health Affairs Project HOPE, 33, 1132–1138. https://doi.org/10.1377/hlthaff.2014.0031

    Article  Google Scholar 

  42. Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2019). A survey on distributed machine learning. ArXiv191209789 Cs Stat.

    Google Scholar 

  43. Bader, J., & Michala, A. L. (2021). Searchable encryption with access control in industrial Internet of Things (IIoT). Wireless Communications and Mobile Computing, 2021, e5555362. https://doi.org/10.1155/2021/5555362

    Article  Google Scholar 

  44. Kishor, A., Chakraborty, C., & Jeberson, W. (2021). A novel fog computing approach for minimization of latency in healthcare using machine learning. International Journal of Interactive Multimedia and Artificial Intelligence, 1, 6.

    Google Scholar 

  45. Chakraborty, C., Banerjee, A., Kolekar, M. H., Garg, L., & Chakraborty, B. (2021). Internet of things for healthcare technologies. Springer.

    Book  Google Scholar 

  46. Attar, H. H., Solyman, A. A. A., Mohamed, A.-E. F., Khosravi, M. R., Menon, V. G., Bashir, A. K., & Tavallali, P. (2020). Efficient equalisers for OFDM and DFrFT-OCDM multicarrier systems in mobile E-health video broadcasting with machine learning perspectives. Physical Communication, 42, 101173. https://doi.org/10.1016/j.phycom.2020.101173

    Article  Google Scholar 

  47. Attar, H., Khosravi, M., Igorovich, S., Georgievna, K., & Alhihi, M. (2021). E-Health communication system with multiservice data traffic evaluation based on a G/G/1 analysis method. Current Signal Transduction Therapy, 16(2). https://doi.org/10.2174/1574362415666200224094706

  48. Rockhold, F., Nisen, P., & Freeman, A. (2016). Data sharing at a crossroads. The New England Journal of Medicine, 375, 1115–1117. https://doi.org/10.1056/NEJMp1608086

    Article  Google Scholar 

  49. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25, 1337–1340. https://doi.org/10.1038/s41591-019-0548-6

    Article  Google Scholar 

  50. Emam, K. E., Jonker, E., Arbuckle, L., & Malin, B. (2011). A systematic review of reidentification attacks on health data. PLoS One, 6, e28071. https://doi.org/10.1371/journal.pone.0028071

    Article  Google Scholar 

  51. Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019). Putting the data before the algorithm in big data addressing personalized healthcare. npj Digital Medicine, 2, 1–6. https://doi.org/10.1038/s41746-019-0157-2

    Article  Google Scholar 

  52. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160.

    Article  Google Scholar 

  53. Lahav, O., Mastronarde, N., & van der Schaar, M. (2018). What is interpretable? Using machine learning to design interpretable decision-support systems. ArXiv Prepr ArXiv181110799.

    Google Scholar 

  54. Langan, P. A. (1995). The racial disparity in US drug arrests. Bureau of Justice Statistics BJS US Department Justice of Justice Programs U S Am.

    Google Scholar 

  55. Morrison, W. D. (1897). The interpretation of criminal statistics. Journal of the Royal Statistical Society, 60, 1–32.

    Article  Google Scholar 

  56. Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018). Building ethics into artificial intelligence. ArXiv181202953 Cs.

    Google Scholar 

  57. Taylor, S., Boniface, M., Pickering, B., Anderson, M., Danks, D., Følstad, A., Leese, M., Müller, V., Sorell, T., Winfield, A., & Woollard, F. (2018). Responsible AI – Key themes, concerns & recommendations for European research and innovation. Retrieved September 11, 2020, from https://eprints.soton.ac.uk/426307/

  58. Larsson, S., & Heintz, F. (2020). Transparency in artificial intelligence. Internet Policy Review, 9(2), 1–16.

    Article  Google Scholar 

  59. Iphofen, R., & Kritikos, M. (2019). Regulating artificial intelligence and robotics: Ethics by design in a digital society. Contemporary Social Science, 16, 1–15. https://doi.org/10.1080/21582041.2018.1563803

    Article  Google Scholar 

  60. Abrardi, L., Cambini, C., & Rondi, L. (2019). The economics of artificial intelligence: A survey. Social Science Research Network.

    Google Scholar 

  61. Coeckelbergh, M. (2020). Artificial intelligence, responsibility attribution, and a relational justification of explainability. Science and Engineering Ethics, 26, 2051–2068. https://doi.org/10.1007/s11948-019-00146-8

    Article  Google Scholar 

  62. Choudhary, A. (2019). Decoding the black box: An important introduction to interpretable machine learning models in python.

    Google Scholar 

  63. Admin. (2017). Briefing: Health and care of older people in England 2017. In: healthierfuture. Retrieved December 2, 2020, from https://www.healthierfuture.org.uk/publications/2017/february/briefing-health-and-care-older-people-england-2017.

  64. Stojić, A., Stanić, N., Vuković, G., Stanišić, S., Perišić, M., Šoštarić, A., & Lazić, L. (2019). Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition. Science of the Total Environment, 653, 140–147.

    Article  Google Scholar 

  65. Sharma, N., & Anju, J. A. (2019). Extreme gradient boosting with squared logistic loss function. In M. Tanveer & R. B. Pachori (Eds.), Machine intelligence and signal analysis (pp. 313–322). Springer.

    Chapter  Google Scholar 

  66. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.

    Article  Google Scholar 

  67. Kazienko, P., Lughofer, E., & Trawinski, B. (2015). Editorial on the special issue “Hybrid and ensemble techniques in soft computing: Recent advances and emerging trends.”. Soft Computing, 19, 3353–3355. https://doi.org/10.1007/s00500-015-1916-x

    Article  Google Scholar 

  68. Acorn, E., Dipsis, N., Pincus, T., & Stathis, K. (2015). Sit-to-stand movement recognition using kinect. In A. Gammerman, V. Vovk, & H. Papadopoulos (Eds.), Statistical learning and data sciences (pp. 179–192). Springer International Publishing.

    Chapter  Google Scholar 

  69. Hellmers, S., Fudickar, S., Lau, S., Elgert, L., Diekmann, R., Bauer, J. M., & Hein, A. (2019). Measurement of the chair rise performance of older people based on force plates and IMUs. Sensors, 19, 1370. https://doi.org/10.3390/s19061370

    Article  Google Scholar 

  70. Silva, J. (2017). Comparing machine learning approaches for fall risk assessment. SCITEPRESS.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ioannis Vourganas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vourganas, I., Attar, H., Michala, A.L. (2022). Accountable, Responsible, Transparent Artificial Intelligence in Ambient Intelligence Systems for Healthcare. In: Chakraborty, C., Khosravi, M.R. (eds) Intelligent Healthcare. Springer, Singapore. https://doi.org/10.1007/978-981-16-8150-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8150-9_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8149-3

  • Online ISBN: 978-981-16-8150-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics