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.
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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
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
(2017). State of the Nation: stroke statistics.
(2018). State of the Nation: stroke statistics.
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.
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
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.
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
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
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.
Vourganas, I., Stankovic, V., & Stankovic, L. (2021). Individualised responsible artificial intelligence for home-based rehabilitation. Sensors, 21, 2. https://doi.org/10.3390/s21010002
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
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.
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
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
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
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
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
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
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.
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
World Health Organization. (2011). Report on the burden of endemic health care associated infection worldwide. World Health Organization.
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.
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
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
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
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.
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/
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
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.
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
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
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
Division UP. (2019). World population ageing. UN.
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
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
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
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
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
Sadeghian, A., Alahi, A., & Savarese, S. (2017). Tracking the untrackable: Learning to track multiple cues with long-term dependencies. ArXiv170101909 Cs.
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
Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2019). A survey on distributed machine learning. ArXiv191209789 Cs Stat.
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
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.
Chakraborty, C., Banerjee, A., Kolekar, M. H., Garg, L., & Chakraborty, B. (2021). Internet of things for healthcare technologies. Springer.
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
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
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
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
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
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
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160.
Lahav, O., Mastronarde, N., & van der Schaar, M. (2018). What is interpretable? Using machine learning to design interpretable decision-support systems. ArXiv Prepr ArXiv181110799.
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.
Morrison, W. D. (1897). The interpretation of criminal statistics. Journal of the Royal Statistical Society, 60, 1–32.
Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018). Building ethics into artificial intelligence. ArXiv181202953 Cs.
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/
Larsson, S., & Heintz, F. (2020). Transparency in artificial intelligence. Internet Policy Review, 9(2), 1–16.
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
Abrardi, L., Cambini, C., & Rondi, L. (2019). The economics of artificial intelligence: A survey. Social Science Research Network.
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
Choudhary, A. (2019). Decoding the black box: An important introduction to interpretable machine learning models in python.
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.
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.
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.
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.
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
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.
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
Silva, J. (2017). Comparing machine learning approaches for fall risk assessment. SCITEPRESS.
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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
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