Motor Patterns Recognition in Parkinson’s Disease

  • Pierpaolo Sorrentino
  • Valeria Agosti
  • Giuseppe Sorrentino
Reference work entry

Abstract

Parkinson’s disease (PD) is characterized clinically by main motor symptoms such as tremor at rest, rigidity, and bradykinesia that affect movements, including gait and postural adjustments. The diagnosis is based on the clinical recognition of these symptoms with the consequent high interrater variability. In order to perform an objective and early diagnosis, approaches that overcome the limitations inherent to clinical examination are needed. In the present work, we will describe several classical technological approaches, such as 3D motion analysis, to achieve an objective evaluation of the cardinal motor symptoms in PD. Furthermore, we will take into account the attempts to identify pathological patterns of integrated, more complex functions such as gait and posture. Finally, as future directions, we will discuss the machine learning approaches in the individuation of specific gait patterns in PD.

Keywords

Parkinson’s disease Movement pattern Gait analysis Machine learning Clinical scales Gait disorders Postural instability 

References

  1. Agosti V, Vitale C, Avella D et al (2016) Effects of Global Postural Reeducation on gait kinematics in parkinsonian patients: a pilot randomized three-dimensional motion analysis study. Neurol Sci 37:515–522.  https://doi.org/10.1007/s10072-015-2433-5CrossRefGoogle Scholar
  2. Amboni M, Barone P, Iuppariello L et al (2012) Gait patterns in parkinsonian patients with or without mild cognitive impairment. Mov Disord 27:1536–1543.  https://doi.org/10.1002/mds.25165CrossRefGoogle Scholar
  3. Ashburn A, Stack E, Pickering RM, Ward CD (2001) A community-dwelling sample of people with Parkinson’s disease: characteristics of fallers and non-fallers. Age Ageing 30:47–52.  https://doi.org/10.1093/ageing/30.1.47CrossRefGoogle Scholar
  4. Bächlin M, Roggen D, Tröster G et al (2009) Potentials of enhanced context awareness in wearable assistants for Parkinson’s disease patients with the freezing of gait syndrome. In: Proceedings – international symposium on wearable computers, ISWC, IEEE, 4–7 Sep - Linz, Austria, pp 123–130Google Scholar
  5. Berardelli A, Rothwell JC, Thompson PD, Hallett M (2001) Pathophysiology of bradykinesia in Parkinson’s disease. Brain 124:2131–2146.  https://doi.org/10.1093/brain/124.11.2131CrossRefGoogle Scholar
  6. Bloem BR, Hausdorff JM, Visser JE, Giladi N (2004) Falls and freezing of gait in Parkinson’s disease: a review of two interconnected, episodic phenomena. Mov Disord 19:871–884.  https://doi.org/10.1002/mds.20115CrossRefGoogle Scholar
  7. Bloem BR, Marinus J, Almeida Q et al (2016) Measurement instruments to assess posture, gait, and balance in Parkinson’s disease: critique and recommendations. Mov Disord.  https://doi.org/10.1002/mds.26572Google Scholar
  8. Bonora G, Carpinella I, Cattaneo D et al (2015) A new instrumented method for the evaluation of gait initiation and step climbing based on inertial sensors: a pilot application in Parkinson’s disease. J Neuroeng Rehabil 12:45.  https://doi.org/10.1186/s12984-015-0038-0CrossRefGoogle Scholar
  9. Cappa P, Patanè F, Rossi S et al (2008) Effect of changing visual condition and frequency of horizontal oscillations on postural balance of standing healthy subjects. Gait Posture 28:615–626.  https://doi.org/10.1016/j.gaitpost.2008.04.013CrossRefGoogle Scholar
  10. Catalá MM, Woitalla D, Arampatzis A (2016) Reactive but not predictive locomotor adaptability is impaired in young Parkinson’s disease patients. Gait Posture 48:177–182.  https://doi.org/10.1016/j.gaitpost.2016.05.008CrossRefGoogle Scholar
  11. Cole BT, Roy SH, Nawab SH (2011) Detecting freezing-of-gait during unscripted and unconstrained activity. In: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBS, IEEE, 30 Aug - 03 Sep - Boston, MA, USA, pp 5649–5652Google Scholar
  12. Daliri MR (2012) Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Meas J Int Meas Confed 45:1729–1734.  https://doi.org/10.1016/j.measurement.2012.04.013CrossRefGoogle Scholar
  13. Daliri MR (2013) Chi-square distance kernel of the gaits for the diagnosis of Parkinson’s disease. Biomed Signal Process Control 8:66–70.  https://doi.org/10.1016/j.bspc.2012.04.007CrossRefGoogle Scholar
  14. Daroff RB, Jankovic J, Mazziotta JC et al (2016) Bradley’s neurology in clinical practice, vols 1–2, 7th edn. Elsevier, LondonGoogle Scholar
  15. de Lau LM, Breteler MM (2006) Epidemiology of Parkinson’s disease. Lancet Neurol 5:525–535.  https://doi.org/10.1016/S1474-4422(06)70471-9CrossRefGoogle Scholar
  16. Dehaene S (2003) The neural basis of the c law: a logarithmic mental number line. Trends Cogn Sci 7:145–147.  https://doi.org/10.1016/S1364-6613(03)00055-XCrossRefGoogle Scholar
  17. Djuric-Jovicic MD, Jovicic NS, Radovanovic SM et al (2014) Automatic identification and classification of freezing of gait episodes in Parkinson’s disease patients. IEEE Trans Neural Syst Rehabil Eng 22:685–694.  https://doi.org/10.1109/TNSRE.2013.2287241CrossRefGoogle Scholar
  18. Doherty KM, van de Warrenburg BP, Peralta MC et al (2011) Postural deformities in Parkinson’s disease. Lancet Neurol 10:538–549CrossRefGoogle Scholar
  19. Doná F, Aquino CC, Gazzola JM et al (2015) Changes in postural control in patients with Parkinson’s disease: a posturographic study. Physiotherapy.  https://doi.org/10.1016/j.physio.2015.08.009Google Scholar
  20. Ebersbach G, Moreau C, Gandor F et al (2013) Clinical syndromes: Parkinsonian gait. Mov Disord 28:1552–1559.  https://doi.org/10.1002/mds.25675CrossRefGoogle Scholar
  21. Elble R, Deuschl G (2011) Milestones in tremor research. Mov Disord 26:1096–1105.  https://doi.org/10.1002/mds.23579CrossRefGoogle Scholar
  22. Endo T, Okuno R, Yokoe M et al (2009) A novel method for systematic analysis of rigidity in Parkinson’s disease. Mov Disord 24:2218–2224.  https://doi.org/10.1002/mds.22752CrossRefGoogle Scholar
  23. Enoka RM (2015) Neuromechanics of human movement. Human Kinetics, ChampaignGoogle Scholar
  24. Gao JB (2004) Analysis of amplitude and frequency variations of essential and Parkinsonian tremors. Med Biol Eng Comput 42:345–349.  https://doi.org/10.1007/BF02344710CrossRefGoogle Scholar
  25. Gelb DJ, Oliver E, Gilman S et al (1999) Diagnostic criteria for Parkinson disease. Arch Neurol 56:33.  https://doi.org/10.1001/archneur.56.1.33CrossRefGoogle Scholar
  26. Geroin C, Smania N, Schena F et al (2015) Does the Pisa syndrome affect postural control, balance, and gait in patients with Parkinson’s disease? An observational cross-sectional study. Parkinsonism Relat Disord 21:736–741.  https://doi.org/10.1016/j.parkreldis.2015.04.020CrossRefGoogle Scholar
  27. Giladi N, Nieuwboer A (2008) Understanding and treating freezing of gait in parkinsonism, proposed working definition, and setting the stage. Mov Disord 23:S423–S425.  https://doi.org/10.1002/mds.21927CrossRefGoogle Scholar
  28. Grabli D, Karachi C, Welter M et al (2012) Normal and pathological gait: what we learn from Parkinson’s disease. J Neurol Neurosurg Psychiatry 83:979–985.  https://doi.org/10.1136/jnnp-2012-302263
  29. Hoehn MM, Yahr MD (1967) Parkinsonism : onset, progression, and mortality. Neurology 17:427–442.  https://doi.org/10.1212/WNL.17.5.427CrossRefGoogle Scholar
  30. Hubble RP, Naughton GA, Silburn PA, Cole MH (2015) Wearable sensor use for assessing standing balance and walking stability in people with Parkinson’s disease: a systematic review. PLoS One 10:1–22.  https://doi.org/10.1371/journal.pone.0123705Google Scholar
  31. Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184.  https://doi.org/10.1136/jnnp.55.3.181CrossRefGoogle Scholar
  32. Jacobs JV, Horak FB, Tran VK, Nutt JG (2006) Multiple balance tests improve the assessment of postural stability in subjects with Parkinson’s disease. J Neurol Neurosurg Psychiatry 77:322–326.  https://doi.org/10.1136/jnnp.2005.068742CrossRefGoogle Scholar
  33. Jian Y, Winter D, Ishac M, Gilchrist L (1993) Trajectory of the body COG and COP during initiation and termination of gait. Gait Posture 1:9–22.  https://doi.org/10.1016/0966-6362(93)90038-3CrossRefGoogle Scholar
  34. Kandori A, Yokoe M, Sakoda S et al (2004) Quantitative magnetic detection of finger movements in patients with Parkinson’s disease. Neurosci Res 49:253–260.  https://doi.org/10.1016/j.neures.2004.03.004CrossRefGoogle Scholar
  35. Kelly VE, Johnson CO, McGough EL et al (2015) Association of cognitive domains with postural instability/gait disturbance in Parkinson’s disease. Parkinsonism Relat Disord 21:692–697.  https://doi.org/10.1016/j.parkreldis.2015.04.002CrossRefGoogle Scholar
  36. Kim J-W, Kwon Y, Yun J-S et al (2015) Regression models for the quantification of Parkinsonian bradykinesia. Biomed Mater Eng 26(Suppl 1):S2249–S2258.  https://doi.org/10.3233/BME-151531Google Scholar
  37. Lee S-H, Lim JS (2012) Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction. Expert Syst Appl 39:7338–7344.  https://doi.org/10.1016/j.eswa.2012.01.084CrossRefGoogle Scholar
  38. Levine D, Richards J, Whittle M, Whittle M (2012) Whittle’s gait analysis. Churchill Livingstone/Elsevier, EdinburghGoogle Scholar
  39. Lin S-H, Chen S-W, Lo Y-C et al (2016) Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm. Med Biol Eng Comput 54:485–496.  https://doi.org/10.1007/s11517-015-1335-2CrossRefGoogle Scholar
  40. Lord S, Rochester L, Baker K, Nieuwboer A (2008) Concurrent validity of accelerometry to measure gait in Parkinsons disease. Gait Posture 27:357–359.  https://doi.org/10.1016/j.gaitpost.2007.04.001CrossRefGoogle Scholar
  41. Mancini M, Horak FB (2010) The relevance of clinical balance assessment tools to differentiate balance deficits. Eur J Phys Rehabil Med 46:239–248Google Scholar
  42. Martinez-Martin P, Gil-Nagel A, Gracia LM et al (1994) Unified Parkinson’s disease rating scale characteristics and structure. Mov Disord 9:76–83.  https://doi.org/10.1002/mds.870090112CrossRefGoogle Scholar
  43. Massion J (1992) Movement, posture and equilibrium: interaction and coordination. Prog Neurobiol 38:35–56.  https://doi.org/10.1016/0301-0082(92)90034-CCrossRefGoogle Scholar
  44. McDonough AL, Batavia M, Chen FC et al (2001) The validity and reliability of the GAITRite system’s measurements: a preliminary evaluation. Arch Phys Med Rehabil 82:419–425.  https://doi.org/10.1053/apmr.2001.19778CrossRefGoogle Scholar
  45. Meigal AY, Rissanen SM, Tarvainen MP et al (2013) Non-linear EMG parameters for differential and early diagnostics of Parkinson’s disease. Front Neurol.  https://doi.org/10.3389/fneur.2013.00135Google Scholar
  46. Mirelman A, Gurevich T, Giladi N et al (2011) Gait alterations in healthy carriers of the LRRK2 G2019S mutation. Ann Neurol 69:193–197.  https://doi.org/10.1002/ana.22165CrossRefGoogle Scholar
  47. Morris ME, McGinley J, Huxham F et al (1999) Constraints on the kinetic, kinematic and spatiotemporal parameters of gait in Parkinson’s disease. Hum Mov Sci 18:461–483CrossRefGoogle Scholar
  48. Morris M, Iansek R, McGinley J et al (2005) Three-dimensional gait biomechanics in Parkinson’s disease: evidence for a centrally mediated amplitude regulation disorder. Mov Disord 20:40–50.  https://doi.org/10.1002/mds.20278CrossRefGoogle Scholar
  49. Mortimer JA, Webster DD (1979) Evidence for a quantitative association between EMG stretch responses and Parkinsonian rigidity. Brain Res 162:169–173.  https://doi.org/10.1016/0006-8993(79)90768-6CrossRefGoogle Scholar
  50. Nelson AJ, Zwick D, Brody S et al (2002) The validity of the GaitRite and the functional ambulation performance scoring system in the analysis of Parkinson gait. NeuroRehabilitation 17:255–262Google Scholar
  51. Nieder A, Miller EK (2003) Coding of cognitive magnitude: compressed scaling of numerical information in the primate prefrontal cortex. Neuron 37:149–157.  https://doi.org/10.1016/S0896-6273(02)01144-3CrossRefGoogle Scholar
  52. Nieminen H, Takala EP (1996) Evidence of deterministic chaos in the myoelectric signal. Electromyogr Clin Neurophysiol 36:49–58Google Scholar
  53. Nutt JG, Bloem BR, Giladi N et al (2011) Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol 10:734–744.  https://doi.org/10.1016/S1474-4422(11)70143-0CrossRefGoogle Scholar
  54. Obeso JA, Rodriguez-Oroz MC, Goetz CG (2010) Missing pieces in the Parkinson’s disease puzzle. Nat Med 16:653–661.  https://doi.org/10.1038/nm.2165CrossRefGoogle Scholar
  55. Palmerini L, Mellone S, Avanzolini G et al (2013) Quantification of motor impairment in Parkinson’s disease using an instrumented timed up and go test. IEEE Trans Neural Syst Rehabil Eng 21:664–673.  https://doi.org/10.1109/tnsre.2012.2236577CrossRefGoogle Scholar
  56. Park BK, Kwon Y, Kim JW et al (2011) Analysis of viscoelastic properties of wrist joint for quantification of parkinsonian rigidity. IEEE Trans Neural Syst Rehabil Eng 19:167–176.  https://doi.org/10.1109/TNSRE.2010.2091149CrossRefGoogle Scholar
  57. Perry J, Burnfield JM, Cabico LM (2010) Gait analysis: normal and pathological function. Second Edition. SLACK Incorporated. Thorofare, NJ, USAGoogle Scholar
  58. Rocchi L, Palmerini L, Weiss A et al (2013) Balance testing with inertial sensors in patients with Parkinson’s disease: assessment of motor subtypes. IEEE Trans Neural Syst Rehabil Eng 22:1064–1071.  https://doi.org/10.1109/TNSRE.2013.2292496CrossRefGoogle Scholar
  59. Roiz RDM, Cacho EWA, Pazinatto MM et al (2010) Gait analysis comparing Parkinson’s disease with healthy elderly subjects. Arq Neuropsiquiatr 68:81–86.  https://doi.org/10.1590/S0004-282X2010000100018CrossRefGoogle Scholar
  60. Salarian A, Russmann H, Wider C et al (2007) Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system. IEEE Trans Biomed Eng 54:313–322CrossRefGoogle Scholar
  61. Sale P, De Pandis MF, Vimercati SL et al (2013) The relation between Parkinson’s disease and ageing. Comparison of the gait patterns of young Parkinson’s disease subjects with healthy elderly subjects. Eur J Phys Rehabil Med 49:161–167Google Scholar
  62. Schrag A, Horsfall L, Walters K et al (2015) Prediagnostic presentations of Parkinson’s disease in primary care: a case–control study. Lancet Neurol 14:57–64.  https://doi.org/10.1016/S1474-4422(14)70287-XCrossRefGoogle Scholar
  63. Scoppa F, Capra R, Gallamini M, Shiffer R (2013) Clinical stabilometry standardization. Basic definitions – acquisition interval – sampling frequency. Gait Posture 37:290–292.  https://doi.org/10.1016/j.gaitpost.2012.07.009CrossRefGoogle Scholar
  64. Shima K, Tsuji T, Kan E et al (2008) Measurement and evaluation of finger tapping movements using magnetic sensors. Conf Proc IEEE Eng Med Biol Soc 1–8:5628–5631.  https://doi.org/10.1109/IEMBS.2008.4650490Google Scholar
  65. Shulman LM, Armstrong M, Ellis T et al (2016) Disability rating scales in Parkinson’s disease: critique and recommendations. Mov Disord.  https://doi.org/10.1002/mds.26649Google Scholar
  66. Sofuwa O, Nieuwboer A, Desloovere K et al (2005) Quantitative gait analysis in Parkinson’s disease: comparison with a healthy control group. Arch Phys Med Rehabil 86:1007–1013.  https://doi.org/10.1016/j.apmr.2004.08.012CrossRefGoogle Scholar
  67. Sorrentino P, Barbato A, Del Gaudio L et al (2016) Impaired gait kinematics in type 1 Gaucher’s disease. J Parkinsons Dis 6:191–195.  https://doi.org/10.3233/JPD-150660CrossRefGoogle Scholar
  68. Stamatakis J, Ambroise J, Crémers J et al (2013) Finger tapping clinimetric score prediction in Parkinson’ s disease using low-cost accelerometers. Comput Intell Neurosci.  https://doi.org/10.1155/2013/717853Google Scholar
  69. Švehlík M, Zwick EB, Steinwender G et al (2009) Gait analysis in patients with Parkinson’s disease off dopaminergic therapy. Arch Phys Med Rehabil 90:1880–1886.  https://doi.org/10.1016/j.apmr.2009.06.017CrossRefGoogle Scholar
  70. Swie YW, Sakamoto K, Shimizu Y (2005) Chaotic analysis of electromyography signal at low back and lower limb muscles during forward bending posture. Electromyogr Clin Neurophysiol 45:329–342Google Scholar
  71. Tahir NM, Manap HH (2012) Parkinson disease gait classification based on machine learning approach. J Appl Sci 12:180–185.  https://doi.org/10.3923/jas.2012.180.185CrossRefGoogle Scholar
  72. Thanawattano C, Pongthornseri R, Anan C et al (2015) Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson’s disease from essential tremor. Biomed Eng Online 14:101.  https://doi.org/10.1186/s12938-015-0098-1CrossRefGoogle Scholar
  73. Timmer J, Lauk M, Deuschl G (1996) Quantitative analysis of tremor time series. Electroencephalogr Clin Neurophysiol 101:461–468.  https://doi.org/10.1016/0924-980X(96)94658-5CrossRefGoogle Scholar
  74. Tripoliti EE, Tzallas AT, Tsipouras MG et al (2013) Automatic detection of freezing of gait events in patients with Parkinson’s disease. Comput Methods Programs Biomed 110:12–26.  https://doi.org/10.1016/j.cmpb.2012.10.016CrossRefGoogle Scholar
  75. Tzallas AT, Tsipouras MG, Rigas G et al (2014) PERFORM: a system for monitoring, assessment and management of patients with Parkinson’s disease. Sensors (Basel) 14:21329–21357.  https://doi.org/10.3390/s141121329CrossRefGoogle Scholar
  76. Visser JE, Oude Nijhuis LB, Janssen L et al (2010) Dynamic posturography in Parkinson’s disease: diagnostic utility of the “first trial effect”. Neuroscience 168:387–394.  https://doi.org/10.1016/j.neuroscience.2010.03.068CrossRefGoogle Scholar
  77. Vitale C, Agosti V, Avella D et al (2012) Effect of global postural rehabilitation program on spatiotemporal gait parameters of parkinsonian patients: a three-dimensional motion analysis study. Neurol Sci 33:1337–1343.  https://doi.org/10.1007/s10072-012-1202-yCrossRefGoogle Scholar
  78. Wahid F, Begg RK, Hass CJ et al (2015) Classification of Parkinson’s disease gait using spatial-temporal gait features. IEEE J Biomed Health Inform 19:1794–1802.  https://doi.org/10.1109/JBHI.2015.2450232CrossRefGoogle Scholar
  79. Walshe FMR, Dejerine J, Ferrier D et al (1961) Contributions of John Hughlings Jackson to neurology. Arch Neurol 5:119–131.  https://doi.org/10.1001/archneur.1961.00450140001001CrossRefGoogle Scholar
  80. Xia R, Powell D, Zev Rymer W et al (2011) Differentiation between the contributions of shortening reaction and stretch-induced inhibition to rigidity in Parkinson’s disease. Exp Brain Res 209:609–618.  https://doi.org/10.1007/s00221-011-2594-2CrossRefGoogle Scholar
  81. Yokoe M, Okuno R, Hamasaki T et al (2009) Opening velocity, a novel parameter, for finger tapping test in patients with Parkinson’s disease. Parkinsonism Relat Disord 15:440–444.  https://doi.org/10.1016/j.parkreldis.2008.11.003CrossRefGoogle Scholar
  82. Yunfeng W, Krishnan S (2010) Statistical analysis of gait rhythm in patients with Parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng 18:150–158.  https://doi.org/10.1109/tnsre.2009.2033062CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Pierpaolo Sorrentino
    • 3
  • Valeria Agosti
    • 1
    • 2
  • Giuseppe Sorrentino
    • 1
    • 2
  1. 1.Department of Motor Sciences and WellnessUniversity of Naples ParthenopeNaplesItaly
  2. 2.Institute Hermitage-CapodimonteNaplesItaly
  3. 3.Department of EngineeringUniversity of Naples ParthenopeNaplesItaly

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