A Smart Inertial Pattern for the SUMMIT IoT Multi-platform

  • Bruno Andò
  • Salvatore Baglio
  • Ruben CrispinoEmail author
  • Lucia L’Episcopo
  • Vincenzo Marletta
  • Marco Branciforte
  • Maria Celvisia Virzì
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 544)


The SUMMIT project funded by the Italian MISE under the PON2020 Action, aims to the development a IoT (Internet of Things) platform which should be flexible and adaptive to easily embed several smart objects such as sensors, multi-sensor architectures and mobile terminals. The main idea is to lunch an open and dynamic eco-system to support the development of IoT based services both for the private and public sectors. The concept of “pattern” will lead the overall development of the SUMMIT platform which represents each element to be integrated in the SUMMIT framework by assuring security, privacy and dependability properties. Above patterns will be also self-evolving on the basis of their behavioral analysis to be performed during the system operation. The three main cases of study addressed by the project will be smart energy, smart health and smart cities. Among patterns addressed by the project the development of a smart inertial platform is considered. Such platform will find application in several contexts with a strong priority in the Smart Living framework. As an example, the architecture developed can be adopted for the sake of Activity of Daily Living monitoring (including Falls), postural instability detection, aided navigation, physical activity assessment, just to cite mostly addressed needs. Actually, above application contexts represent serious needs to be addressed to enable Active Ageing and Well Being. The Smart Inertial Pattern (SIP) is based on an embedded architecture equipped with sensors (accelerometer, gyroscope, compass) and communication facilities. In this paper the use of the SIP device for the implementation of a ADL classifier exploiting an event correlated approach is presented.


IoT Activity of daily living Assistive technology 



This work has been supported by the SUMMIT grant, funded under Horizon 2020—PON 2014/2020 programme, N. F/050270/03/X32—CUP B68I17000370008—COR: 130150.


  1. 1.
    Cameron ID, Gillespie LD, Robertson MC, Murray GR, Hill KD, Cumming RG, Kerse N (2012) Interventions for preventing falls in older people in care facilities and hospitals (review). In: The Cochrane Library, Wiley & Sons, issue 12Google Scholar
  2. 2.
    Slips, trips and falls in hospital, 3rd report from the Patient Safety Observatory, NPSA (2007)Google Scholar
  3. 3.
    National Institute for Health and Clinical Excellence. Falls in older people: assessing risk and prevention (2013).
  4. 4.
    The falls management program: a quality improvement initiative for nursing facilities, Agency for Healthcare Research and Quality, Rockville, MD, October 2014.
  5. 5.
    Module 3: falls prevention and management, Agency for Healthcare Research and Quality, Rockville, MD, October 2014.
  6. 6.
    Lou E, Durdle NG, Raso VJ, Hill DL (2000) A low-power posture measurement system for the treatment of scoliosis. IEEE Trans Instrum Meas 49(1):108–113CrossRefGoogle Scholar
  7. 7.
    Wong Wai-Yin, Wong Man-Sang (2009) Measurement of postural change in trunk movements using three sensor modules. IEEE Trans Instrum Meas 58(8):2737–2742CrossRefGoogle Scholar
  8. 8.
    Rescio G, Leone A, Siciliano P (2013) Supervised expert system for wearable MEMS accelerometer-based fall detector. J Sens 2013(254629):11Google Scholar
  9. 9.
    Panahandeh G, Mohammadiha N, Leijon A, Handel P (2012) Chest-mounted inertial measurement unit for pedestrian motion classification using continuous hidden Markov model. In: 2012 IEEE international instrumentation and measurement technology conference (I2MTC), pp 991–995, Graz, Austria, 13–16 MayGoogle Scholar
  10. 10.
    Kang JM, Yoo T, Kim HC (2006) A wrist-worn integrated health monitoring instrument with a tele-reporting device for telemedicine and telecare. IEEE Trans Instrum Meas 55(5):1655–1661CrossRefGoogle Scholar
  11. 11.
    Dunkel J, Bruns R, Stipkovic S (2013) Event-based smartphone sensor processing for ambient assisted living. In: 2013 IEEE eleventh international symposium on autonomous decentralized systems (ISADS), pp 1–6, Mexico City, Mexico, 6–8 MarchGoogle Scholar
  12. 12.
    Ketabdar H, Lyra M (2010) System and methodology for using mobile phones in live remote monitoring of physical activities. In: 2010 IEEE international symposium on technology and society (ISTAS), Wollongong, Australia, 7–9 JuneGoogle Scholar
  13. 13.
    Tacconi C, Mellone S, Chiari L (2011) Smartphone-based applications for investigating falls and mobility. In: 5th international conference on pervasive computing technologies for healthcare (PervasiveHealth) and workshops, Dublin, Ireland, 23–26 MayGoogle Scholar
  14. 14.
    Viet VQ, Lee G, Choi D (2012) Fall detection based on movement and smart phone technology. In: 2012 IEEE RIVF international conference on computing and communication technologies, research, innovation, and vision for the future (RIVF), Ho Chi Minh City, Vietnam, 27 February–1 MarchGoogle Scholar
  15. 15.
    He Y, Li Y, Bao S (2012) Fall detection by built-in tri-accelerometer of smartphone. In: 2012 IEEE-EMBS international conference on biomedical and health informatics (BHI), Hong Kong, China, 5–7 JanuaryGoogle Scholar
  16. 16.
    Fahim M, Fatima I, Lee S, Lee Y (2012) Daily life activity tracking application for smart homes using android smartphone. In: 2012 14th international conference on advanced communication technology (ICACT), PyeongChang, South Korea, 19–22 FebruaryGoogle Scholar
  17. 17.
    Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. BioMed Eng (Online) 12(66). Scholar
  18. 18.
    Andò B, Baglio S, Lombardo CO, Marletta V, Pergolizzi EA, Pistorio A (2014) An event polarized paradigm for ADL detection in AAL context. In: Proceedings of 2014 IEEE international instrumentation and measurement technology conference (I2MTC), Montevideo, Uruguay, 12–15 MayGoogle Scholar
  19. 19.
    Andò B, Baglio S, Lombardo CO, Marletta V (2015) An event polarized paradigm for ADL detection in AAL context. IEEE Trans Instrum Meas 64(7):1814–1825CrossRefGoogle Scholar
  20. 20.
    Andò B, Baglio S, Lombardo CO, Marletta V (2016) A multisensor data-fusion approach for ADL and fall classification. IEEE Trans Instrum Meas 65(9):1960–1967CrossRefGoogle Scholar
  21. 21.
    Chaudhuri S, Thompson H, Demiris G (Oct–Dec 2014) Fall detection devices and their use with older adults: a systematic review. s.l.: J Geriatr Phis TherGoogle Scholar
  22. 22.
  23. 23. STMicroelectronics

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bruno Andò
    • 1
  • Salvatore Baglio
    • 1
  • Ruben Crispino
    • 1
    Email author
  • Lucia L’Episcopo
    • 1
  • Vincenzo Marletta
    • 1
  • Marco Branciforte
    • 2
  • Maria Celvisia Virzì
    • 2
  1. 1.DIEEI-University of CataniaCataniaItaly
  2. 2.ST-MicroelectronicsMilanItaly

Personalised recommendations