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Sensory data fusion of pressure mattress and wireless inertial magnetic measurement units

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Abstract

Head movement of infants is an important parameter for analysing infant motor patterns. Despite its importance, this field has received little sensory-based research in the past years. Therefore, we present a sensory-supported data fusion model for head movement analysis of infants in supine position. The sensory system comprises a pressure mattress and two wireless inertial magnetic measurement units, rendering precise, objective and non-intrusive information on pressure distribution and 3D trunk orientation, respectively. Algorithms first perform pressure data pre-processing and calculate image moments to acquire 2D trunk orientation. Afterwards, unscented Kalman filter is used for sensory data fusion. After additional data processing, head and trunk coordinates are calculated along with head displacement distance. The sensory system was tested on experimental measurements, performed in eight normally developing infants aged from 1 to 5 months. Results of several algorithm combinations were compared to referential video recordings in terms of head lifts. Combination of algorithms, incorporating head tracking and sensory data fusion provides completely accurate results in comparison to normative data. Statistical data analysis and referential optoelectronic measurements were performed to evaluate accuracy of the sensory fusion model. Suitability of the proposed sensory system for head movement analysis of infants in supine position was verified.

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References

  1. Adde L, Helbostad JL, Jensenius AR, Taraldsen G, Grunewaldt KH, Støen R (2010) Early prediction of cerebral palsy by computer-based video analysis of general movements: a feasibility study. Dev Med Child Neurol 52(8):773–778

    Article  PubMed  Google Scholar 

  2. Adrien JL, Lenoir P, Martineau J, Perrot A, Hameruy L, Larmande C, Sauvage D (1993) Blind ratings of early symptoms of autism based upon family home movies. J Am Acad Child Psychiatry 32(3):617–626

    Article  CAS  Google Scholar 

  3. Beravs T, Podobnik J, Munih M (2012) Three-axial accelerometer calibration using Kalman filter covariance matrix for online estimation of optimal sensor orientation. IEEE Tran Instrum Meas 61(9):2501–2511

    Article  Google Scholar 

  4. Blauw-Hospers CH, Hadders-Algra M (2005) A systematic review of the effects of early intervention on motor development. Dev Med Child Neurol 47(6):421–432

    Article  PubMed  Google Scholar 

  5. Bobath K, Bobath B (1956) The diagnosis of cerebral palsy in infancy. Arch Dis Child 31(159):408–414

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Bryson SE, Zwaigenbaum L, McDermott C, Rombough V, Brian J (2008) The autism observation scale for infants: scale development and reliability data. J Autism Dev Disord 38(4):731–738

    Article  PubMed  Google Scholar 

  7. Campbell SK, Kolobe TH, Osten ET, Lenke M, Girolami GL (1995) Construct validity of the test of infant motor performance. Phys Ther 75(7):585–596

    CAS  PubMed  Google Scholar 

  8. Crassidis JL, Markley FL, Cheng Y (2007) Survey of nonlinear attitude estimation methods. J Guid Control Dyn 30(1):12–28

    Article  Google Scholar 

  9. Darrah J, Piper M, Watt MJ (1998) Assessment of gross motor skills of at-risk infants: predictive validity of the Alberta Infant Motor Scale. Dev Med Child Neurol 40(7):485–491

    Article  CAS  PubMed  Google Scholar 

  10. Dusing S, Mercer V, Yu B, Reilly M, Thorpe D (2005) Trunk position in supine of infants born preterm and at term: an assessment using a computerised pressure mat. Pediatr Phys Ther 17(1):2–10

    Article  PubMed  Google Scholar 

  11. Dusing SC, Kyvelidou A, Mercer VS, Stergiou N (2009) Infants born preterm exhibit different patterns of center-of-pressure movement than infants born at full term. Phys Ther 89(12):1354–1362

    Article  PubMed Central  PubMed  Google Scholar 

  12. Einspieler C, Cioni G, Paolicelli PB, Bos AF, Dressler A, Ferrari F, Roversi MF, Prechtl HFR (2002) The early markers for later dyskinetic cerebral palsy are different from those for spastic cerebral palsy. Neuropediatrics 33(2):73–78

    Article  CAS  PubMed  Google Scholar 

  13. Einspieler C, Marschik PB, Bos AF, Ferrari F, Cioni G, Prechtl HFR (2012) Early markers for cerebral palsy: insights from the assessment of general movements. Future Neurol 7(6):709–717

    Article  CAS  Google Scholar 

  14. Elsabbagh M, Divan G, Koh YJ, Kim YS, Kauchali S, Marcín C, Montiel-Nava C, Patel V, Paula CS, Wang C, Yasamy MT, Fombonne E (2012) Global prevalence of autism and other pervasive developmental disorders. Autism Res 5(3):160–179

    Article  PubMed Central  PubMed  Google Scholar 

  15. Franchak JM, Kretch KS, Soska KC, Adolph KE (2011) Head-mounted eye tracking: a new method to describe infant looking. Child Dev 82(6):1738–1750

    Article  PubMed Central  PubMed  Google Scholar 

  16. Groot L (2000) Posture and motility in preterm infants. Dev Med Child Neurol 42(1):65–68

    Article  PubMed  Google Scholar 

  17. Guzzetta A, Belmonti V, Battini R, Boldrini A, Paolicelli PB, Cioni G (2007) Does the assessment of general movements without video observation reliably predict neurological outcome? Eur J Paediatr Neurol 11(6):362–367

    Article  PubMed  Google Scholar 

  18. Hadders-Algra M (2004) General movements: a window for early identification of children at high risk for developmental disorders. J Pediatr 145(2):12–18

    Article  Google Scholar 

  19. Hadders-Algra M (2012) Active head lifting from supine in infancy: a significant stereotypy? Dev Med Child Neurol 54(6):489–490

    Article  PubMed  Google Scholar 

  20. Heinze F, Hesels K, Breitbach-Faller N, Schmitz-Rode T, Disselhorst-Klug C (2010) Movement analysis by accelerometry of newborns and infants for the early detection of movement disorders due to infantile cerebral palsy. Med Biol Eng Comput 48(8):765–772

    Article  PubMed  Google Scholar 

  21. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Tran Inform Theory 8(2):179–187

    Article  Google Scholar 

  22. Johnson A (2002) Prevalence and characteristics of children with cerebral palsy in Europe. Dev Med Child Neurol 44(09):633–640

    Article  Google Scholar 

  23. Krigger KW (2006) Cerebral palsy: an overview. Am Fam Physician 73(1):91–100

    PubMed  Google Scholar 

  24. Kyvelidou A, Harbourne RT, Shostrom VK, Stergiou N (2010) Reliability of center of pressure measures for assessing the development of sitting postural control in infants with or at risk of cerebral palsy. Arch Phys Med Rehabil 91(10):1593–1601

    Article  PubMed Central  PubMed  Google Scholar 

  25. Lee HM, Galloway JC (2012) Early intensive postural and movement training advances head control in very young infants. Phys Ther 92(7):935–947

    Article  PubMed  Google Scholar 

  26. Meinecke L, Breitbach-Faller N, Bartz C, Damen R, Rau G, Disselhorst-Klug C (2006) Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy. Hum Mov Sci 25(2):125–144

    Article  CAS  PubMed  Google Scholar 

  27. Palmer FB (2004) Strategies for the early diagnosis of cerebral palsy. J Pediatr 145(2):S8–S11

    Article  PubMed  Google Scholar 

  28. Pincus SM, Goldberger AL (1994) Physiological time-series analysis: what does regularity quantify? Am J Physiol Heart C 266(4):H1643–H1656

    CAS  Google Scholar 

  29. Platt MJ, Cans C, Johnson A, Surman G, Topp M, Torrioli MG, Krageloh-Mann I (2007) Trends in cerebral palsy among infants of very low birthweight (<1500 g) or born prematurely (<32 weeks) in 16 European centres: a database study. Lancet 369(9555):43–50

    Article  PubMed  Google Scholar 

  30. Robins DL, Fein D, Barton ML, Green JA (2001) The modified checklist for autism in toddlers: an initial study investigating the early detection of autism and pervasive developmental disorders. J Autism Dev Disord 31(2):131–144

    Article  CAS  PubMed  Google Scholar 

  31. Rönnqvist L, Hopkins B (1998) Head position preference in the human newborn: a new look. Child Dev 69(1):13–23

    Article  PubMed  Google Scholar 

  32. Rosenbaum P, Paneth N, Leviton A, Goldstein M, Bax M, Damiano D, Dan B, Jacobsson B (2007) A report: the definition and classification of cerebral palsy April 2006. Dev Med Child Neurol 49(Suppl 109):8–14

    Google Scholar 

  33. Stahl A, Schellewald C, Stavdahl Ø, Aamo OM, Adde L, Kirkerod H (2012) An optical flow-based method to predict infantile cerebral palsy. IEEE Tran Neural Syst Rehabil 20(4):605–614

    Article  Google Scholar 

  34. Teitelbaum P, Teitelbaum O, Nye J, Fryman J, Maurer RG (1998) Movement analysis in infancy may be useful for early diagnosis of autism. Proc Natl Acad Sci USA 95(23):13982–13987

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. van den Noort JC, Ferrari A, Cutti AG, Becher JG, Harlaar J (2013) Gait analysis in children with cerebral palsy via inertial and magnetic sensors. Med Biol Eng Comput 51(4):1–10

    Google Scholar 

  36. van der Merwe R (2004) Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. PhD Thesis, University of Stellenbosch, Western Cape, South Africa

  37. VanDyke MC, Schwartz JL, Hall CD (2004) Unscented Kalman filtering for spacecraft attitude state and parameter estimation. Department of Aerospace & Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, Virginia

  38. van Haastert IC, Groenendaal F, van de Waarsenburg MK, Eijsermans MJ, Koopman-Esseboom C, Jongmans MJ, Helders PJM, de Vries LS (2012) Active head lifting from supine in early infancy: an indicator for non-optimal cognitive outcome in late infancy. Dev Med Child Neurol 54(6):538–543

    Article  PubMed  Google Scholar 

  39. Yuge M, Marschik PB, Nakajima Y, Yamori Y, Kanda T, Hirota H, Yoshida N, Einspieler C (2011) Movements and postures of infants aged 3 to 5 months: to what extent is their optimality related to perinatal events and to the neurological outcome? Early Hum Dev 87(3):231–237

    Article  PubMed  Google Scholar 

  40. Zwaigenbaum L, Bryson S, Rogers T, Roberts W, Brian J, Szatmari P (2005) Behavioral manifestations of autism in the first year of life. Int J Dev Neurosci 23(2):143–152

    Article  PubMed  Google Scholar 

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Acknowledgments

This work was funded by the European Union Collaborative Project CareToy grant ICT-2011.5.1-287932 and additionally supported by the Slovenian Research Agency. The authors gratefully thank Giuseppina Sgandurra, Giovanni Cioni, Francesca Cecchi and Paolo Dario for help with recruitment of infants, experimental set-up and data acquisition.

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Correspondence to Andraž Rihar.

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Rihar, A., Mihelj, M., Kolar, J. et al. Sensory data fusion of pressure mattress and wireless inertial magnetic measurement units. Med Biol Eng Comput 53, 123–135 (2015). https://doi.org/10.1007/s11517-014-1217-z

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  • DOI: https://doi.org/10.1007/s11517-014-1217-z

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