Smart Objects and Biofeedback for a Pediatric Rehabilitation 2.0

  • Paolo MeriggiEmail author
  • Martina Mandalà
  • Elena Brazzoli
  • Tecla Piacente
  • Marcella Mazzola
  • Ivana Olivieri
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 544)


The progressive miniaturization of electronic devices and their exponential increase in processing, storage and transmission capabilities, is opening new scenarios in pervasive computing, like the Ambient Assisted Living (AAL) and Internet Of Things (IoT). Although most of the investigations in the recent years focused on remote monitoring and diagnostic efforts, rehabilitation too could be positively affected by the use of these solutions, since these small Smart Objects may enable novel quantitative approaches. In this paper, we present the preliminary efforts in designing a pediatric rehabilitation protocol based on Smart Objects and biofeedback, which we administered to a small sample of hemiplegic children. Despite the few treatments (not suitable to assess any change in the subjects’ abilities), children enjoyed participating in the study, and the initial qualitative/quantitative results highlight that such approach could represent an interesting starting point to fuel the scientific and clinical discussion towards a Pediatric Rehabilitation 2.0.


Smart objects Biofeedback Pediatric rehabilitation Internet of things 



This work has been partially funded by Italian Ministry of Health (Ricerca Corrente IRCCS).

The authors would like to thank the Elena Pajan Parola Foundation and the Associazione Zorzi per le Neuroscienze for financially supporting the development of CARE Lab. Moreover, authors would also thank Leroy Merlin Italia Srl whose donation was used to fund the acquisition of the sensing system used in this study.


  1. 1.
    Jekel K et al (2016) Development of a proxy-free objective assessment tool of instrumental activities of daily living in mild cognitive impairment using smart home technologies. J Alzheimers Dis 52(2):509–517CrossRefGoogle Scholar
  2. 2.
    Jovanov E, Nallathimmareddygari VR, Pryor JE (2016) SmartStuff: A case study of a smart water bottle. Conf Proc IEEE Eng Med Biol Soc 2016:6307–6310Google Scholar
  3. 3.
    Pharow P et al (2009) Portable devices, sensors and networks: wireless personalized eHealth services. Stud Health Technol Inform 150:1012–1016Google Scholar
  4. 4.
    Sacco G et al (2012) Detection of activities of daily living impairment in Alzheimer’s disease and mild cognitive impairment using information and communication technology. Clin Interv Aging 7:539–549CrossRefGoogle Scholar
  5. 5.
    Prioleau T, Moore Ii E, Ghovanloo M (2017) Unobtrusive and wearable systems for automatic dietary monitoring. IEEE Trans Biomed Eng 64(9):2075–2089CrossRefGoogle Scholar
  6. 6.
    Roy PC, Abidi SR, Abidi SSR (2017) Monitoring activities related to medication adherence in ambient assisted living environments. Stud Health Technol Inform 235:28–32Google Scholar
  7. 7.
    Grace SL et al (2017) Perceptions of seniors with heart failure regarding autonomous zero-effort monitoring of physiological parameters in the smart-home environment. Heart Lung 46(4):313–319CrossRefGoogle Scholar
  8. 8.
    Olivieri I et al (2018) Computer Assisted REhabilitation (CARE) Lab: A novel approach towards Pediatric Rehabilitation 2.0. J Pediatr Rehabil Med 11(1):43–51CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paolo Meriggi
    • 1
    Email author
  • Martina Mandalà
    • 1
  • Elena Brazzoli
    • 1
  • Tecla Piacente
    • 2
  • Marcella Mazzola
    • 1
  • Ivana Olivieri
    • 1
  1. 1.IRCCS Fondazione Don Carlo GnocchiMilanItaly
  2. 2.C.R.M. Coop. Sociale OnlusMilanItaly

Personalised recommendations