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Scalable real-time health data sensing and analysis enabling collaborative care delivery

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This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.

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Availability of data and materials

Not applicable.

Code availability

The main DIRAP code in the form of preprepared Docker images, will become available upon the end of the LifeChamps project.
















  15. We mainly provide this functionality on the cloud, since Flink is notoriously good for low-latency streaming applications, however, we have also incorporated CEP functionality on the edge if required.



  • Aazam M, Zeadally S, Flushing EF (2021) Task offloading in edge computing for machine learning-based smart healthcare. Comput Netw 191(108):019.

    Article  Google Scholar 

  • Aggarwal CC (2017) An introduction to outlier analysis. In: Outlier analysis. Springer, pp 1–34

  • Agrawal S (2014) Late effects of cancer treatment in breast cancer survivors. South Asian J Cancer 3(02):112–115

    Google Scholar 

  • Aminikhanghahi S, Wang T, Cook DJ (2018) Real-time change point detection with application to smart home time series data. IEEE Trans Knowl Data Eng 31(5):1010–1023

    Google Scholar 

  • Arifoglu D, Bouchachia A (2019) Detection of abnormal behaviour for dementia sufferers using convolutional neural networks. Artif Intell Med 94:88–95

    Google Scholar 

  • Balducci L (2007) Aging, frailty, and chemotherapy. Cancer Control 14(1):7–12

    Google Scholar 

  • Banos O, Amin MB, Khan WA et al (2016) The mining minds digital health and wellness framework. Biomed Eng Online 15(1):165–186

    Google Scholar 

  • Bennett JA, Winters-Stone KM, Dobek J et al (2013) Frailty in older breast cancer survivors: age, prevalence, and associated factors. In: Oncology nursing forum, NIH Public Access, p E126

  • Bok K, Kim D, Yoo J (2018) Complex event processing for sensor stream data. Sensors 18(9):3084

    Google Scholar 

  • Browne HK, Arbaugh WA, McHugh J et al (2000) A trend analysis of exploitations. In: Proceedings 2001 IEEE symposium on security and privacy. S &P 2001. IEEE, pp 214–229

  • Carbone P, Katsifodimos A, Ewen S et al (2015) Apache flink\(^\text{ TM }\): stream and batch processing in a single engine. IEEE Data Eng Bull 38(4):28–38

    Google Scholar 

  • Comito C, Talia D (2017) Energy consumption of data mining algorithms on mobile phones: evaluation and prediction. Pervasive Mob Comput 42:248–264

    Google Scholar 

  • Dautov R, Distefano S, Buyya R (2019) Hierarchical data fusion for smart healthcare. J Big Data 6(1):1–23

    Google Scholar 

  • Dawar N, Kehtarnavaz N (2018) A convolutional neural network-based sensor fusion system for monitoring transition movements in healthcare applications. In: 2018 IEEE 14th international conference on control and automation (ICCA), IEEE, pp. 482–485

  • Desale KS, Shinde SV (2022) Addressing concept drifts using deep learning for heart disease prediction: a review. In: Proceedings of second doctoral symposium on computational intelligence. Springer, pp 157–167

  • Dhillon A, Majumdar S, St-Hilaire M et al (2018) Mcep: a mobile device based complex event processing system for remote healthcare. In: 2018 IEEE 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), IEEE, pp 203–210

  • Enshaeifar S, Zoha A, Markides A et al (2018) Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PLoS ONE 13(5):e0195605

    Google Scholar 

  • Ethun CG, Bilen MA, Jani AB et al (2017) Frailty and cancer: implications for oncology surgery, medical oncology, and radiation oncology. CA Cancer J Clin 67(5):362–377

    Google Scholar 

  • Fagherazzi G, Fischer A, Ismael M et al (2021) Voice for health: the use of vocal biomarkers from research to clinical practice. Digital Biomark 5(1):78–88

    Google Scholar 

  • Fawcett TE, Provost F (2002) Fraud detection. In: Handbook of data mining and knowledge discovery, pp 726–731

  • Fernández-Alemán JL, Señor IC, Lozoya PÁO et al (2013) Security and privacy in electronic health records: a systematic literature review. J Biomed Inform 46(3):541–562

    Google Scholar 

  • Ferreira D, Kostakos V, Dey AK (2015) Aware: mobile context instrumentation framework. Front ICT 2:6

    Google Scholar 

  • Ganz PA (2001) Late effects of cancer and its treatment. In: Seminars in oncology nursing. Elsevier, pp 241–248

  • Gomes HM, Read J, Bifet A et al (2019) Machine learning for streaming data: state of the art, challenges, and opportunities. ACM SIGKDD Explor Newsl 21(2):6–22

    Google Scholar 

  • Graf C (2008) The lawton instrumental activities of daily living scale. Am J Nurs 108(4):52–62

    Google Scholar 

  • Graubner P, Thelen C, Körber M et al (2018) Multimodal complex event processing on mobile devices. In: Proceedings of the 12th ACM international conference on distributed and event-based systems, pp 112–123

  • Halliday V, Porock D, Arthur A et al (2012) Development and testing of a cancer appetite and symptom questionnaire. J Hum Nutr Diet 25(3):217–224

    Google Scholar 

  • Hawkins DM (1980) Identification of outliers, vol 11. Springer, Berlin

    MATH  Google Scholar 

  • Hossain SM, Hnat T, Saleheen N et al (2017) mcerebrum: a mobile sensing software platform for development and validation of digital biomarkers and interventions. In: Proceedings of the 15th ACM conference on embedded network sensor systems, pp 1–14

  • Ijaz MF, Attique M, Son Y (2020) Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors 20(10):2809

    Google Scholar 

  • Jiang S, Song X, Wang H et al (2006) A clustering-based method for unsupervised intrusion detections. Pattern Recogn Lett 27(7):802–810

    Google Scholar 

  • Kashani MH, Madanipour M, Nikravan M et al (2021) A systematic review of iot in healthcare: applications, techniques, and trends. J Netw Comput Appl 192(103):164

    Google Scholar 

  • Khazael B, Malazi HT, Clarke S (2021) Complex event processing in smart city monitoring applications. IEEE Access 9:143150–143165

    Google Scholar 

  • Kontaki M, Gounaris A, Papadopoulos AN et al (2016) Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inf Syst 55:37–53

    Google Scholar 

  • Kotronoulas G, Kearney N, Maguire R et al (2014) What is the value of the routine use of patient-reported outcome measures toward improvement of patient outcomes, processes of care, and health service outcomes in cancer care? a systematic review of controlled trials. J Clin Oncol 32(14):1480–1510

    Google Scholar 

  • Kulshrestha U, Durbha S (2020) Edge analytics and complex event processing for real time air pollution monitoring and control. In: IGARSS 2020-2020 IEEE international geoscience and remote sensing symposium. IEEE, pp 893–896

  • Kumar D, Jeuris S, Bardram JE et al (2021) Mobile and wearable sensing frameworks for mhealth studies and applications: a systematic review. ACM Trans Comput Healthc.

    Article  Google Scholar 

  • Lan L, Shi R, Wang B et al (2019) A universal complex event processing mechanism based on edge computing for internet of things real-time monitoring. IEEE Access 7:101865–101878

    Google Scholar 

  • Lee CS, Lee AY (2020) Clinical applications of continual learning machine learning. The Lancet Digital Health 2(6):e279–e281

    Google Scholar 

  • Lenihan DJ, Cardinale DM (2012) Late cardiac effects of cancer treatment. J Clin Oncol 30(30):3657–3664

    Google Scholar 

  • Li Y, Pan W, Li K et al (2018) Sliding trend fuzzy approximate entropy as a novel descriptor of heart rate variability in obstructive sleep apnea. IEEE J Biomed Health Inform 23(1):175–183

    Google Scholar 

  • Loreti D, Chesani F, Mello P et al (2019) Complex reactive event processing for assisted living: the habitat project case study. Expert Syst Appl 126:200–217

    Google Scholar 

  • Ma Z, Yu W, Zhai X et al (2019) A complex event processing-based online shopping user risk identification system. IEEE Access 7:172088–172096

    Google Scholar 

  • Mohamed MB, Meddeb-Makhlouf A, Fakhfakh A (2019) Intrusion cancellation for anomaly detection in healthcare applications. In: 2019 15th international wireless communications and mobile computing conference (IWCMC). IEEE, pp 313–318

  • Morid MA, Sheng ORL, Kawamoto K et al (2020) Learning hidden patterns from patient multivariate time series data using convolutional neural networks: a case study of healthcare cost prediction. J Biomed Inform 111(103):565

    Google Scholar 

  • Ness KK, Wogksch MD (2020) Frailty and aging in cancer survivors. Transl Res 221:65–82

    Google Scholar 

  • Ommundsen N, Wyller TB, Nesbakken A et al (2014) Frailty is an independent predictor of survival in older patients with colorectal cancer. Oncologist 19(12):1268

    Google Scholar 

  • Park C, Mishra R, Golledge J et al (2021) Digital biomarkers of physical frailty and frailty phenotypes using sensor-based physical activity and machine learning. Sensors 21(16):5289

    Google Scholar 

  • Pedrelli P, Fedor S, Ghandeharioun A et al (2020) Monitoring changes in depression severity using wearable and mobile sensors. Front Psych 11:1413

    Google Scholar 

  • Peng SL, Liu CJ, He J et al (2019) Optimization rfid-enabled retail store management with complex event processing. Int J Autom Comput 16(1):52–64

    Google Scholar 

  • Pereira J, Silveira M (2019) Learning representations from healthcare time series data for unsupervised anomaly detection. In: 2019 IEEE international conference on big data and smart computing (BigComp). IEEE, pp 1–7

  • Quasim MT (2021) Resource management and task scheduling for iot using mobile edge computing. Wirel Pers Commun 1–18

  • Rahmani AM, Babaei Z, Souri A (2021) Event-driven iot architecture for data analysis of reliable healthcare application using complex event processing. Clust Comput 24(2):1347–1360

    Google Scholar 

  • Ramoly N, Bouzeghoub A, Finance B (2018) A framework for service robots in smart home: an efficient solution for domestic healthcare. IRBM 39(6):413–420

    Google Scholar 

  • Ranjan Y, Rashid Z, Stewart C et al (2019) Radar-base: open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR mHealth uHealth 7(8):e11734

    Google Scholar 

  • Reynolds WM, Gould JW (1981) A psychometric investigation of the standard and short form beck depression inventory. J Consult Clin Psychol 49(2):306

    Google Scholar 

  • Riboni D, Civitarese G, Bettini C (2016) Analysis of long-term abnormal behaviors for early detection of cognitive decline. In: 2016 IEEE international conference on pervasive computing and communication workshops (PerCom Workshops). IEEE, pp 1–6

  • Rodrigues JJ, De La Torre I, Fernández G et al (2013) Analysis of the security and privacy requirements of cloud-based electronic health records systems. J Med Internet Res 15(8):e2494

    Google Scholar 

  • Roldán J, Boubeta-Puig J, Martínez JL et al (2020) Integrating complex event processing and machine learning: an intelligent architecture for detecting iot security attacks. Expert Syst Appl 149(113):251

    Google Scholar 

  • Šabić E, Keeley D, Henderson B et al (2021) Healthcare and anomaly detection: using machine learning to predict anomalies in heart rate data. AI Soc 36(1):149–158

    Google Scholar 

  • Schover LR, van der Kaaij M, van Dorst E et al (2014) Sexual dysfunction and infertility as late effects of cancer treatment. Eur J Cancer Suppl 12(1):41–53

    Google Scholar 

  • Shahid N, Rappon T, Berta W (2019) Applications of artificial neural networks in health care organizational decision-making: a scoping review. PLoS ONE 14(2):e0212356

    Google Scholar 

  • Smit E, Bouwstra H, van der Wouden J et al (2020) Development of a patient-reported outcomes measurement information system (promis®) short form for measuring physical function in geriatric rehabilitation patients. Qual Life Res 29(9):2563–2572

    Google Scholar 

  • Stein KD, Syrjala KL, Andrykowski MA (2008) Physical and psychological long-term and late effects of cancer. Cancer 112(S11):2577–2592

    Google Scholar 

  • Toliopoulos T, Bellas C, Gounaris A et al (2020a) PROUD: parallel outlier detection for streams. In: Proceedings of the 2020 international conference on management of data, SIGMOD conference 2020, online conference [Portland, OR, USA], June 14–19, 2020. ACM, pp 2717–2720

  • Toliopoulos T, Gounaris A, Tsichlas K et al (2020b) Continuous outlier mining of streaming data in flink. Inf Syst 93(101):569.

    Article  Google Scholar 

  • Torous J, Kiang MV, Lorme J et al (2016) New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Mental Health 3(2):e16

    Google Scholar 

  • Torous J, Wisniewski H, Bird B et al (2019) Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach. J Technol Behav Sci 4(2):73–85

    Google Scholar 

  • Virag N, Sutton R, Vetter R et al (2007) Prediction of vasovagal syncope from heart rate and blood pressure trend and variability: experience in 1155 patients. Heart Rhythm 4(11):1375–1382

    Google Scholar 

  • Vitabile S, Marks M, Stojanovic D et al (2019) Medical data processing and analysis for remote health and activities monitoring. High-performance modelling and simulation for big data applications. Springer, Cham, pp 186–220

  • Wang C, Patriquin M, Vaziri A et al (2021) Mobility performance in community-dwelling older adults: potential digital biomarkers of concern about falling. Gerontology 67(3):365–373

    Google Scholar 

  • Wildiers H, Heeren P, Puts M et al (2014) International society of geriatric oncology consensus on geriatric assessment in older patients with cancer. J Clin Oncol 32(24):2595

    Google Scholar 

  • Xiong H, Huang Y, Barnes LE et al (2016) Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, pp 415–426

  • Yang P, Dumont G, Ansermino JM (2006) Adaptive change detection in heart rate trend monitoring in anesthetized children. IEEE Trans Biomed Eng 53(11):2211–2219

    Google Scholar 

  • Yin K, Liu Z, Liu P (2017) Trend analysis of global stock market linkage based on a dynamic conditional correlation network. J Bus Econ Manag 18(4):779–800

    Google Scholar 

  • Zhou H, Park C, Shahbazi M et al (2021) Digital biomarkers of cognitive frailty: the value of detailed gait assessment beyond gait speed. Gerontology 1–10

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This research work has been supported by the European Commission under the Horizon 2020 Programme, through funding of the LifeChamps project (Grant 875329).


This research work has been supported by the European Commission under the Horizon 2020 Programme, through funding of the LifeChamps project (Grant 875329).

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All authors have actively contributed to the design of the solution and the development of the material described in this work. The system implementation was done by the researchers I.D., I.M., S.K. and T.T.

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Correspondence to Ilias Dimitriadis.

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Dimitriadis, I., Mavroudopoulos, I., Kyrama, S. et al. Scalable real-time health data sensing and analysis enabling collaborative care delivery. Soc. Netw. Anal. Min. 12, 63 (2022).

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