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Systematic literature review of ambient assisted living systems supported by the Internet of Things

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Abstract

Ambient assisted living (AAL) proposes a vision of the future in which older people can remain in their homes on their own for as long as possible, guaranteeing care and attention thanks to intelligent systems capable of making their lives easier. In parallel, the Internet of Things (IoT) proposes environments where different ‘things’ surrounding the user are able to communicate with each other through the Internet. This allows the creation of intelligent environments, which, in turn, are a requirement of AAL systems. Therefore, there is a relevant synergy between AAL and IoT, where the latter allows the creation of more intelligent and transparent AAL systems for users. This paper makes a systematic literature review (SLR) of AAL systems supported by IoT. We have explored aspects of interest such as the types of systems, the most popular technologies used in their development and the degree of compliance regarding the characteristics that any system of this type should have. Besides, the difficulty of evaluating user satisfaction due to the lack of real evidence is analyzed. This SLR, carried out according to the procedure proposed by Kitchenhan, is based on a selection of 61 papers from among 643 initial results published between 2015 and 2020. As a result of the analysis conducted, several challenges and opportunities that remain open in the field of IoT-supported AAL have been outlined.

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References

  1. John R, Beard., Simon, Biggs., David, E., Bloom, et al.: Global Population ageing: peril or promise? World Economic Forum. Published January 26, 2012. Accessed January 17, (2021). https://www.weforum.org/reports/global-population-ageing-peril-or-promise/

  2. AAL Association.: The ageing demographic. AAL Programme. Accessed December 9, (2020). https://www.aal-europe.eu/about/the-ageing-demographic/

  3. Ageing.: Published January 7, 2016. Accessed December 14, 2020. https://www.un.org/en/sections/issues-depth/ageing/

  4. Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013). https://doi.org/10.1109/JBHI.2012.2234129

    Article  Google Scholar 

  5. World Health Organization.: Ageing and health. Published 2018. Accessed January 17, (2021). https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

  6. Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009). https://doi.org/10.1016/j.pmcj.2009.04.001

    Article  Google Scholar 

  7. Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010). https://doi.org/10.1016/j.comnet.2010.05.010

    Article  MATH  Google Scholar 

  8. Balasubramanian, V., Jolfaei, A.: A scalable framework for healthcare monitoring application using the Internet of Medical Things. Software: Practice and Experience. n/a(n/a). https://doi.org/10.1002/spe.2849

  9. Friedewald, M., Costa, O.D., Punie, Y., Alahuhta, P., Heinonen, S.: Perspectives of ambient intelligence in the home environment. Telematics Inform. 22(3), 221–238 (2005). https://doi.org/10.1016/j.tele.2004.11.001

    Article  Google Scholar 

  10. da Silva, F.S.C., Vasconcelos, W.W.: Managing responsive environments with software agents. Appl. Artif. Intell. 21(4–5), 469–488 (2007). https://doi.org/10.1080/08839510701253682

    Article  Google Scholar 

  11. Busetta, P., Kuflik, T., Merzi, M., Rossi, S.: Service delivery in smart environments by implicit organizations. In: The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004, pp 356–363. (2004) https://doi.org/10.1109/MOBIQ.2004.1331742

  12. Petersen, SA., Kofod-Petersen, A.: The non-accidental tourist: using ambient intelligence for enhancing tourist experiences. In: Network-Centric Collaboration and Supporting Frameworks. IFIP International Federation for Information Processing. Springer US, pp 619–626. (2006) https://doi.org/10.1007/978-0-387-38269-2_65

  13. Ashton, K.: That ‘internet of things’ thing. RFID journal. 22(7), 97–114 (2009)

    Google Scholar 

  14. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013). https://doi.org/10.1016/j.future.2013.01.010

    Article  Google Scholar 

  15. Gomez, C., Oller, J., Paradells, J.: Overview and evaluation of bluetooth low energy: an emerging low-power wireless technology. Sensors 12(9), 11734–11753 (2012). https://doi.org/10.3390/s120911734

    Article  Google Scholar 

  16. Silva, B.M.C., Rodrigues, J.J.P.C., de la Torre, D.I., López-Coronado, M., Saleem, K.: Mobile-health: a review of current state in 2015. J. Biomed. Inform. 56, 265–272 (2015). https://doi.org/10.1016/j.jbi.2015.06.003

    Article  Google Scholar 

  17. Islam, S.M.R., Kwak, D., Kabir, M.H., Hossain, M., Kwak, K.: The Internet of Things for health care: a comprehensive survey. IEEE Access. 3, 678–708 (2015). https://doi.org/10.1109/ACCESS.2015.2437951

    Article  Google Scholar 

  18. Almeida, A., Mulero, R., Rametta, P., Urošević, V., Andrić, M., Patrono, L.: A critical analysis of an IoT—aware AAL system for elderly monitoring. Futur. Gener. Comput. Syst. 97, 598–619 (2019). https://doi.org/10.1016/j.future.2019.03.019

    Article  Google Scholar 

  19. Mainetti, L., Patrono, L., Secco, A., Sergi, I.: An IoT-aware AAL system for elderly people. In: 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech), pp 1–6. (2016) https://doi.org/10.1109/SpliTech.2016.7555929

  20. Hussain, A., Wenbi, R., da Silva, A.L., Nadher, M., Mudhish, M.: Health and emergency-care platform for the elderly and disabled people in the Smart City. J. Syst. Softw. 110, 253–263 (2015). https://doi.org/10.1016/j.jss.2015.08.041

    Article  Google Scholar 

  21. Al-khafajiy, M., Baker, T., Chalmers, C., et al.: Remote health monitoring of elderly through wearable sensors. Multimed Tools Appl. 78(17), 24681–24706 (2019). https://doi.org/10.1007/s11042-018-7134-7

    Article  Google Scholar 

  22. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014). https://doi.org/10.1109/SURV.2013.042313.00197

    Article  Google Scholar 

  23. Hassanalieragh, M., Page, A., Soyata, T., et al.: Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: opportunities and challenges. In: 2015 IEEE International Conference on Services Computing, pp 285–292. (2015) https://doi.org/10.1109/SCC.2015.47

  24. Kitchenham, BA., Charters. S.: Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University Joint Report /Keele University; 2007. https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf

  25. Alaa, M., Zaidan, A.A., Zaidan, B.B., Talal, M., Kiah, M.L.M.: A review of smart home applications based on Internet of Things. J. Netw. Comput. Appl. 97, 48–65 (2017). https://doi.org/10.1016/j.jnca.2017.08.017

    Article  Google Scholar 

  26. Talavera, J.M., Tobón, L.E., Gómez, J.A., et al.: Review of IoT applications in agro-industrial and environmental fields. Comput. Electron. Agric. 142, 283–297 (2017). https://doi.org/10.1016/j.compag.2017.09.015

    Article  Google Scholar 

  27. Bedi, G., Venayagamoorthy, G.K., Singh, R., Brooks, R.R., Wang, K.: Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet Things J. 5(2), 847–870 (2018). https://doi.org/10.1109/JIOT.2018.2802704

    Article  Google Scholar 

  28. Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., Alizadeh, M.: The application of internet of things in healthcare: a systematic literature review and classification. Univ Access Inf Soc. 18(4), 837–869 (2019). https://doi.org/10.1007/s10209-018-0618-4

    Article  Google Scholar 

  29. Calvaresi, D., Cesarini, D., Sernani, P., Marinoni, M., Dragoni, A.F., Sturm, A.: Exploring the ambient assisted living domain: a systematic review. J Ambient Intell. Human Comput. 8(2), 239–257 (2017). https://doi.org/10.1007/s12652-016-0374-3

    Article  Google Scholar 

  30. Erazo-Garzon, L., Erraez, J., Cedillo, P., Illescas-Peña, L.: Quality assessment approaches for ambient assisted living systems: a systematic review. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds.) Applied Technologies. Communications in Computer and Information Science. Springer International Publishing, pp 421–439. (2020) https://doi.org/10.1007/978-3-030-42517-3_32

  31. Queirós, A., Silva, A., Alvarelhão, J., Rocha, N.P., Teixeira, A.: Usability, accessibility and ambient-assisted living: a systematic literature review. Univ Access Inf. Soc. 14(1), 57–66 (2015). https://doi.org/10.1007/s10209-013-0328-x

    Article  Google Scholar 

  32. Maskeliūnas, R., Damaševičius, R., Segal, S.: A review of internet of things technologies for ambient assisted living environments. Future Internet. 11(12), 259 (2019). https://doi.org/10.3390/fi11120259

    Article  Google Scholar 

  33. Maswadi, K., Ghani, N.B.A., Hamid, S.B.: Systematic literature review of smart home monitoring technologies based on IoT for the elderly. IEEE Access. 8, 92244–92261 (2020). https://doi.org/10.1109/ACCESS.2020.2992727

    Article  Google Scholar 

  34. Qadri, Y.A., Nauman, A., Zikria, Y.B., Vasilakos, A.V., Kim, S.W.: The future of healthcare Internet of Things: a survey of emerging technologies. IEEE Commun. Surv. Tutorials. 22(2), 1121–1167 (2020). https://doi.org/10.1109/COMST.2020.2973314

    Article  Google Scholar 

  35. Critical Appraisals Skills Programme.: Critical appraisal skills programme. CASP–Critical Appraisal Skills Programme. Accessed January 17, (2021). https://casp-uk.net/

  36. World Health Organization.: Life expectancy at birth (years). Published 2020. Accessed January 17, (2021). https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)

  37. Marques, G., Pires, I.M., Miranda, N., Pitarma, R.: Air quality monitoring using assistive robots for ambient assisted living and enhanced living environments through Internet of Things. Electronics 8(12), 1375 (2019). https://doi.org/10.3390/electronics8121375

    Article  Google Scholar 

  38. Zhang, S., Liu, X., Liu, Y., Ding, B., Guo, S., Wang, J.: Accurate respiration monitoring for mobile users with commercial RFID devices. IEEE J. Select. Areas Commun. (2020). https://doi.org/10.1109/JSAC.2020.3020604

    Article  Google Scholar 

  39. Cunha, M., Fuks, H.: AmbLEDs collaborative healthcare for AAL systems. In: 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp 626–631. (2015) https://doi.org/10.1109/CSCWD.2015.7231030

  40. Mandaric, K., Skocir, P., Vukovic, M., Jezic, G.: Anomaly detection based on fixed and wearable sensors in assisted living environments. In: 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, pp 1–6. (2019) https://doi.org/10.23919/SOFTCOM.2019.8903796

  41. Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., De Munari, I.: 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 (2019). https://doi.org/10.1109/JIOT.2019.2920283

    Article  Google Scholar 

  42. Zia Uddin, Md.: A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. J. Parallel Distrib. Comput. 123, 46–53 (2019). https://doi.org/10.1016/j.jpdc.2018.08.010

    Article  Google Scholar 

  43. Subasi, A., Radhwan, M., Kurdi, R., Khateeb, K.: IoT based mobile healthcare system for human activity recognition. In: 2018 15th Learning and Technology Conference (L T), pp 29–34. (2018) https://doi.org/10.1109/LT.2018.8368507

  44. Keum, SS., Lee, CH., Kang, SJ.: Device to device collaboration architecture for real- time identification of user and abnormal activities in home. In: 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), pp 1–3. (2019) https://doi.org/10.1109/ITNAC46935.2019.9077981

  45. Gerina, F., Massa, S.M., Moi, F., Reforgiato Recupero, D., Riboni, D.: Recognition of cooking activities through air quality sensor data for supporting food journaling. HCIS 10(1), 27 (2020). https://doi.org/10.1186/s13673-020-00235-9

    Article  Google Scholar 

  46. Ajerla, D., Mahfuz, S., Zulkernine, F.: A real-time patient monitoring framework for fall detection. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2019/9507938

  47. Chandra, I., Sivakumar, N., Gokulnath, C.B., Parthasarathy, P.: IoT based fall detection and ambient assisted system for the elderly. Cluster Comput. 22(1), 2517–2525 (2019). https://doi.org/10.1007/s10586-018-2329-2

    Article  Google Scholar 

  48. Toda, K., Shinomiya, N.: Machine learning-based fall detection system for the elderly using passive RFID sensor tags. In: 2019 13th International Conference on Sensing Technology (ICST), pp 1–6. (2019) https://doi.org/10.1109/ICST46873.2019.9047732

  49. Yacchirema, D.C., Sarabia-JáCome, D., Palau, C.E., Esteve, M.: A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access. 6, 35988–36001 (2018). https://doi.org/10.1109/ACCESS.2018.2849822

    Article  Google Scholar 

  50. Veiga, A., Garcia, L., Parra, L., Lloret, J., Augele, V.: An IoT-based smart pillow for sleep quality monitoring in AAL environments. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pp 175–180. (2018) https://doi.org/10.1109/FMEC.2018.8364061

  51. Cerina, L., Notargiacomo, S., Paccanit, MG., Santambrogio, MD.: A fog-computing architecture for preventive healthcare and assisted living in smart ambients. In: 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pp 1–6. (2017)https://doi.org/10.1109/RTSI.2017.8065939

  52. Compton, K., Hauck, S.: Reconfigurable computing: a survey of systems and software. ACM Comput Surv. 34(2), 171–210 (2002). https://doi.org/10.1145/508352.508353

    Article  Google Scholar 

  53. Ngankam, HK., Pigot, H., Parenteau, M., et al.: An IoT architecture of microservices for ambient assisted living environments to promote aging in smart cities. In: Pagán, J., Mokhtari, M., Aloulou, H., Abdulrazak, B., Cabrera, MF. (eds.) How AI Impacts Urban Living and Public Health. Lecture Notes in Computer Science. Springer International Publishing, pp 154–167 (2019) https://doi.org/10.1007/978-3-030-32785-9_14

  54. Valsamakis, Y., Savidis, A.: Sharable personal automations for ambient assisted living. In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments. PETRA ’17. Association for Computing Machinery, pp 103–110. (2017) https://doi.org/10.1145/3056540.3056560

  55. Pandey, P., Litoriya, R.: Elderly care through unusual behavior detection: a disaster management approach using IoT and intelligence. IBM J. Res. Develop. 64(1/2), 1–15 (2020). https://doi.org/10.1147/JRD.2019.2947018

    Article  Google Scholar 

  56. Debauche, O., Mahmoudi, S., Manneback, P., Assila, A.: Fog IoT for health: a new architecture for patients and elderly monitoring. Procedia Comput. Sci. 160, 289–297 (2019). https://doi.org/10.1016/j.procs.2019.11.087

    Article  Google Scholar 

  57. Costa, A., Rincon, J.A., Carrascosa, C., Julian, V., Novais, P.: Emotions detection on an ambient intelligent system using wearable devices. Futur. Gener. Comput. Syst. 92, 479–489 (2019). https://doi.org/10.1016/j.future.2018.03.038

    Article  Google Scholar 

  58. Mrozek, D., Koczur, A., Małysiak-Mrozek, B.: Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Inf. Sci. 537, 132–147 (2020). https://doi.org/10.1016/j.ins.2020.05.070

    Article  Google Scholar 

  59. Sarabia-Jacome, D., Lacalle, I., Palau, CE., Estevé, M.: Efficient deployment of predictive analytics in edge gateways: fall detection scenario. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp 41–46. (2019)https://doi.org/10.1109/WF-IoT.2019.8767231

  60. Hamim, Mohd., Paul, S., Hoque, SI., Rahman, MdN., Baqee, IA.: IoT based remote health monitoring system for patients and elderly people. In: 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp 533–538. (2019) https://doi.org/10.1109/ICREST.2019.8644514

  61. Woznowski, P., Fafoutis, X., Song, T., et al.: A multi-modal sensor infrastructure for healthcare in a residential environment. In: 2015 IEEE International Conference on Communication Workshop (ICCW), pp 271–277. (2015) https://doi.org/10.1109/ICCW.2015.7247190

  62. Guerrero-Ulloa, G., Rodríguez-Domínguez, C., Hornos, MJ.: IoT-based system to help care for dependent elderly. In: Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., Zúñiga Sánchez. M., (eds.) Technology trends. Communications in Computer and Information Science. Springer International Publishing, pp 41–55. (2019) https://doi.org/10.1007/978-3-030-05532-5_4

  63. Naya, K., Hu, X., Miyazaki, T., Li, P., Wang, K.: Non-invasive and quick respiratory-rate monitoring at bedtime using passive RFIDs. In: 2019 International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp 244–249. (2019)https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00061

  64. Gonzalez-Usach, R., Collado, V., Esteve, M., Palau, CE.: AAL open source system for the monitoring and intelligent control of nursing homes. In: 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), pp 84–89. (2017) https://doi.org/10.1109/ICNSC.2017.8000072

  65. Sandeepa, C., Moremada, C., Dissanayaka, N., Gamage, T., Liyanage, M.: An emergency situation detection system for ambient assisted living. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp 1–6. (2020) https://doi.org/10.1109/ICCWorkshops49005.2020.9145053

  66. Tabbakha, NE., Tan, WH., Ooi, CP.: Indoor location and motion tracking system for elderly assisted living home. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS), (2017) https://doi.org/10.1109/ICORAS.2017.8308073

  67. Koutli, M., Theologou, N., Tryferidis, A., Tzovaras, D.: Abnormal behavior detection for elderly people living alone leveraging IoT sensors. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp 922–926. (2019) https://doi.org/10.1109/BIBE.2019.00173

  68. Yacchirema, D., de Puga, J.S., Palau, C., Esteve, M.: Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers Ubiquit Comput. 23(5), 801–817 (2019). https://doi.org/10.1007/s00779-018-01196-8

    Article  Google Scholar 

  69. Alexandru, A., Coardos, D., Tudora, E.: IoT-based healthcare remote monitoring platform for elderly with fog and cloud computing. In: 2019 22nd International Conference on Control Systems and Computer Science (CSCS). pp 154–161. (2019) https://doi.org/10.1109/CSCS.2019.00034

  70. Corno, F., De Russis, L., Roffarello, AM.: A healthcare support system for assisted living facilities: an IoT solution. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). 1: 344–352. (2016) https://doi.org/10.1109/COMPSAC.2016.29

  71. International Organization for Standardization. ISO 9241–11:2018(en), Ergonomics of human-system interaction—Part 11: Usability: Definitions and concepts. Published 2018. Accessed January 29, 2021. https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en

  72. Kandil, M., AlBaghdadi, R., AlAttar, F., Damaj, I., AmIE.: An ambient intelligent environment for assisted living. In: 2019 Advances in Science and Engineering Technology International Conferences (ASET), (2019) https://doi.org/10.1109/ICASET.2019.8714499

  73. Naik, N.: Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In: 2017 IEEE International Systems Engineering Symposium (ISSE). (2017) https://doi.org/10.1109/SysEng.2017.8088251

  74. Aloulou, H., Mokhtari, M., Abdulrazak, B.: Deployment of an IoT solution for early behavior change detection. In: Pagán, J., Mokhtari, M., Aloulou, H., Abdulrazak, B., Cabrera, MF., (eds.) How AI Impacts Urban Living and Public Health. Lecture Notes in Computer Science. Springer International Publishing, pp 27–35. (2019) https://doi.org/10.1007/978-3-030-32785-9_3

  75. Silva, M.P., Goncalves, A.L., Dantas, M.A.R., et al.: Implementation of IoT for monitoring ambient air in ubiquitous AAL environments. Brazilian Symposium on Computing Systems Engineering (SBESC) IEEE 2015, 158–161 (2015). https://doi.org/10.1109/SBESC.2015.37

    Article  Google Scholar 

  76. Hassan, M.K., El Desouky, A.I., Elghamrawy, S.M., Sarhan, A.M.: Intelligent hybrid remote patient-monitoring model with cloud-based framework for knowledge discovery. Comput. Electr. Eng. 70, 1034–1048 (2018). https://doi.org/10.1016/j.compeleceng.2018.02.032

    Article  Google Scholar 

  77. Matsui, T., Onishi, K., Misaki, S., Fujimoto, M., Suwa, H., Yasumoto, K.: Easy-to-deploy living activity sensing system and data collection in general homes. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), (2020) https://doi.org/10.1109/PerComWorkshops48775.2020.9156124

  78. Gomes, B., Muniz, L., da Silva e Silva, FJ., Talavera Rios, LE., Endler, M.: A comprehensive cloud-based IoT software infrastructure for ambient assisted living. In: 2015 International Conference on Cloud Technologies and Applications (CloudTech). pp 1–8. (2015) https://doi.org/10.1109/CloudTech.2015.7336998

  79. Gupta, P., Caleb-Solly, P.: A framework for semi-supervised adaptive learning for activity recognition in healthcare applications. In: Pimenidis E, Jayne C, (eds.) Engineering Applications of Neural Networks. Communications in Computer and Information Science. Springer International Publishing, pp 3–15. (2018) https://doi.org/10.1007/978-3-319-98204-5_1

  80. Titi, S., Elhadj, HB., Chaari, L.: An ontology-based healthcare monitoring system in the Internet of Things. In: 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). pp 319–324. (2019) https://doi.org/10.1109/IWCMC.2019.8766510

  81. Jesús-Azabal, M., Berrocal, J., García-Alonso, J., Soares, VNGJ., Galán-Jiménez J.: An opportunistic routing solution to monitor isolated elderly people in rural areas. In: García-Alonso J, Fonseca C, (eds.) Gerontechnology. Communications in Computer and Information Science. Springer International Publishing, pp 195–203. (2020) https://doi.org/10.1007/978-3-030-41494-8_19

  82. Lee, C., Park, S., Jung, Y., Lee, Y., Mathews, MJ.: Internet of Things: technology to enable the elderly. In: 2018 Second IEEE International Conference on Robotic Computing (IRC). pp 358–362. (2018) https://doi.org/10.1109/IRC.2018.00075

  83. Basanta, H., Huang, YP., Lee, TT.: Intuitive IoT-based H2U healthcare system for elderly people. In: 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC). (2016) https://doi.org/10.1109/ICNSC.2016.7479018

  84. Marques, G., Pitarma, R.: mHealth: indoor environmental quality measuring system for enhanced health and well-being based on Internet of Things. J. Sens. Actuator Netw. 8(3), 43 (2019). https://doi.org/10.3390/jsan8030043

    Article  Google Scholar 

  85. Altulyan, MS., Huang, C., Yao, L., Wang, X., Kanhere, S., Cao, Y.: Reminder care system: an activity-aware cross-device recommendation system. In: Li J, Wang S, Qin S, Li X, Wang S, (eds.) Advanced Data Mining and Applications. Lecture Notes in Computer Science. Springer International Publishing, pp 207–220. (2019) https://doi.org/10.1007/978-3-030-35231-8_15

  86. Staifi, N., Brahimi, S., Maamri, R., Belguidoum, M.: Towards a smart home for elder healthcare. In: 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud). pp 230–237. (2019) https://doi.org/10.1109/FiCloud.2019.00039

  87. Griffiths, N., Chin, J.: Towards unobtrusive ambient sound monitoring for smart and assisted environments. In: 2016 8th Computer Science and Electronic Engineering (CEEC). pp 18–23. (2016) https://doi.org/10.1109/CEEC.2016.7835882

  88. Abdel-Basset, M., Hawash, H., Chang, V., Chakrabortty, R.K., Ryan, M.: Deep learning for heterogeneous human activity recognition in complex IoT applications. IEEE Internet Things J. (2020). https://doi.org/10.1109/JIOT.2020.3038416

    Article  Google Scholar 

  89. Divya, V., Leena, R.S.: Docker based intelligent fall detection using edge-fog cloud infrastructure. IEEE Internet Things J. (2020). https://doi.org/10.1109/JIOT.2020.3042502

    Article  Google Scholar 

  90. Hu, Y., Wang, B., Sun, Y., An, J., Wang, Z.: Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home. Int. J. Distrib. Sens. Netw. 16(11), 1550147720971513 (2020). https://doi.org/10.1177/1550147720971513

    Article  Google Scholar 

  91. Kraft, D., Srinivasan, K., Bieber, G.: Wrist-worn accelerometer based fall detection for embedded systems and IoT devices using deep learning algorithms. pp 352–361. (2020) https://doi.org/10.1145/3389189.3397983

  92. Plaza, SL., Carrizo, JMV., Domínguez, JJG., Martín, AJ., Gómez, DG.: frailwear: A wearable IoT device for daily activity monitoring of elderly patients. In: 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS). pp 1–6. (2020) https://doi.org/10.1109/DCIS51330.2020.9268629

  93. Marques, G., Pitarma, R.: A cost-effective air quality supervision solution for enhanced living environments through the internet of things. Electronics 8(2), 170 (2019). https://doi.org/10.3390/electronics8020170

    Article  Google Scholar 

  94. Jita, H., Pieterse, V.: A Framework to apply the internet of things for medical care in a home environment. In: Proceedings of the 2018 International Conference on Cloud Computing and Internet of Things. CCIOT 2018. Association for Computing Machinery, pp 45–54. (2018) https://doi.org/10.1145/3291064.3291065

  95. Yacchirema, DC., Palau, CE., Esteve, M.: Enable IoT interoperability in ambient assisted living: Active and healthy aging scenarios. In: 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC). pp 53–58. (2017) https://doi.org/10.1109/CCNC.2017.7983081

  96. Ferreira, G., Penicheiro, P., Bernardo, R., Mendes, L., Barroso, J., Pereira, A.: Low Cost Smart Homes for Elders. In: Antona M, Stephanidis C, (eds.) Universal Access in Human–Computer Interaction. Human and Technological Environments. Lecture Notes in Computer Science. Springer International Publishing, pp 507–517. (2017)https://doi.org/10.1007/978-3-319-58700-4_41

  97. Pires, G., Correia, P., Jorge, D., et al.: VITASENIOR-MT: a telehealth solution for the elderly focused on the interaction with TV. In: 2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom). 1–6. (2018) https://doi.org/10.1109/HealthCom.2018.8531126

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Funding

This work was partly supported by grant PID2021-122215NB-C33 (AwESOMe Project) funded by MCIN/AEI/https://doi.org/10.13039/501100011033 and by “ERDF A way to do Europe” and partly through grant programme for R&D&i projects, for universities and public research entities qualified as agents of the Andalusian Knowledge System, within the scope of the Andalusian Plan for Research, Development and Innovation (PAIDI 2020). Project 80% co-financed by the European Union, within the framework of the Andalusia ERDF Operational Programme 2014–2020 "Smart growth: an economy based on knowledge and innovation". Project funded by the Ministry of Economic Transformation, Industry, Knowledge and Universities of the Andalusian Regional Government. DECISION project with reference P20_00865.

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All authors have contributed to the design of this review. The literature search, data analysis and the elaboration of the manuscript were carried out by PC. GO and IM-B reviewed the correct completion of each of the parts involved in the review, as well as the different drafts elaborated up to the current version. They also performed a validation to verify the correct inclusion or exclusion of the papers in this review.

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Correspondence to Pablo Caballero.

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Appendix 1

Appendix 1

See Tables 9, 10, 11

Table 9 Quality checklist (adapted from CASP)
Table 10 Quality evaluation
Table 11 Characteristics of AmI systems

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Caballero, P., Ortiz, G. & Medina-Bulo, I. Systematic literature review of ambient assisted living systems supported by the Internet of Things. Univ Access Inf Soc (2023). https://doi.org/10.1007/s10209-023-01022-w

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