Medical & Biological Engineering & Computing

, Volume 57, Issue 2, pp 463–476 | Cite as

Computer model for leg agility quantification and assessment for Parkinson’s disease patients

  • Christopher Ornelas-VencesEmail author
  • Luis Pastor Sánchez-Fernández
  • Luis Alejandro Sánchez-Pérez
  • Juan Manuel Martínez-Hernández
Original Article


Parkinson’s disease (PD) is a progressive disorder that affects motor regulation. The Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the illness progression based on clinical observations. The leg agility is an item in this scale, yet only a visual detection of the features is used, leading to subjectivity. Overall, 50 patients (85 measurements) with varying motor impairment severity were asked to perform the leg agility item while wearing inertial sensor units on each ankle. We quantified features based on the MDS-UPDRS and designed a fuzzy inference model to capture clinical knowledge for assessment. The model proposed is capable of capturing all details regardless of the task speed, reducing the inherent uncertainty of the examiner observations obtaining a 92.35% of coincidence with at least one expert. In addition, the continuous scale implemented in this work prevents the inherent “floor/ceil” effect of discrete scales. This model proves the feasibility of quantification and assessment of the leg agility through inertial signals. Moreover, it allows a better follow-up of the PD patient state, due to the repeatability of our computer model and the continuous output, which are not objectively achievable through visual examination.

Graphical abstract


Parkinson’s disease Leg agility Fuzzy logic Assessment 



We are grateful to the patients and healthcare professionals that contributed with their participation, ideas, and suggestions to accomplish this work.

Compliance with ethical standards

All procedures performed in this work were in accordance with The Code of Ethics of the World Medical Association and with Data Protection and Privacy Laws. The collected data was under explicit written patients consent.

Conflicts of interest

The authors declare that they have no conflict of interest.

Supplementary material

11517_2018_1894_MOESM1_ESM.pdf (760 kb)
ESM 1 (PDF 760 kb)


  1. 1.
    Shulman LM, Gruber-Baldini AL, Anderson KE, Vaughan CG, Reich SG, Fishman PS, Weiner WJ (2008) The evolution of disability in Parkinson disease. Mov Disord 23:790–796. CrossRefGoogle Scholar
  2. 2.
    Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79:368–376. CrossRefGoogle Scholar
  3. 3.
    De Lau LML, Giesbergen PCLM, De Rijk MC et al (2004) Incidence of parkinsonism and Parkinson disease in a general population the Rotterdam study. Neurology 63:1240–1244. CrossRefGoogle Scholar
  4. 4.
    Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, van Hilten JJ, LaPelle N, for the Movement Disorder Society UPDRS Revision Task Force (2008) Movement Disorder Society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23:2129–2170. CrossRefGoogle Scholar
  5. 5.
    Ramaker C, Marinus J, Stiggelbout AM, van Hilten BJ (2002) Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov Disord 17:867–876. CrossRefGoogle Scholar
  6. 6.
    Lee HJ, Kim SK, Park H, Kim HB, Jeon HS, Jung YJ, Oh E, Kim HJ, Yun JY, Jeon BS, Park KS (2015) Clinicians’ tendencies to under-rate parkinsonian tremors in the less affected hand. PLoS One 10:e0131703. CrossRefGoogle Scholar
  7. 7.
    Yang K, Xiong W-X, Liu F-T et al (2016) Objective and quantitative assessment of motor function in Parkinson’s disease—from the perspective of practical applications. Ann Transl Med 4:90–90. CrossRefGoogle Scholar
  8. 8.
    Stack E, Jupp K, Ashburn A (2004) Developing methods to evaluate how people with Parkinson’s disease turn 180°: an activity frequently associated with falls. Disabil Rehabil 26:478–484. CrossRefGoogle Scholar
  9. 9.
    Pan D, Dhall R, Lieberman A, Petitti DB (2015) A mobile cloud-based Parkinson’s disease assessment system for home-based monitoring. JMIR mHealth and uHealth 3:e29. CrossRefGoogle Scholar
  10. 10.
    Darwish A, Hassanien AE (2011) Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors 11:5561–5595. CrossRefGoogle Scholar
  11. 11.
    Pantelopoulos A, Bourbakis NG (2010) A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern Part C Appl Rev 40:1–12. CrossRefGoogle Scholar
  12. 12.
    Kubota KJ, Chen JA, Little MA (2016) Machine learning for large-scale wearable sensor data in Parkinson’s disease: concepts, promises, pitfalls, and futures. Mov Disord 31:1314–1326. CrossRefGoogle Scholar
  13. 13.
    Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR (2013) Quantitative wearable sensors for objective assessment of Parkinson’s disease. Mov Disord 28:1628–1637CrossRefGoogle Scholar
  14. 14.
    Pasluosta CF, Gassner H, Winkler J, Klucken J, Eskofier BM (2015) An emerging era in the management of Parkinson’s disease: wearable technologies and the internet of things. IEEE J Biomed Health Inform 19:1873–1881. CrossRefGoogle Scholar
  15. 15.
    Pastorino M, Arredondo MT, Cancela J, Guillen S (2013) Wearable sensor network for health monitoring: the case of Parkinson disease. J Phys Conf Ser 450:012055. CrossRefGoogle Scholar
  16. 16.
    Pierleoni P, Palma L, Belli A, Pernini L (2014) A real-time system to aid clinical classification and quantification of tremor in Parkinson’s disease. In: 2014 IEEE-EMBS international conference on biomedical and health informatics, BHI 2014. pp 113–116Google Scholar
  17. 17.
    Rigas G, Tzallas AT, Tsipouras MG, Bougia P, Tripoliti EE, Baga D, Fotiadis DI, Tsouli SG, Konitsiotis S (2012) Assessment of tremor activity in the Parkinsons disease using a set of wearable sensors. IEEE Trans Inf Technol Biomed 16:478–487. CrossRefGoogle Scholar
  18. 18.
    Dai H, Zhang P, Lueth TC (2015) Quantitative assessment of parkinsonian tremor based on an inertial measurement unit. Sensors 15:25055–25071. CrossRefGoogle Scholar
  19. 19.
    Zwartjes DGM, Heida T, Van Vugt JPP et al (2010) Ambulatory monitoring of activities and motor symptoms in Parkinsons disease. IEEE Trans Biomed Eng 57:2778–2786. CrossRefGoogle Scholar
  20. 20.
    Chelaru MI, Duval C, Jog M (2010) Levodopa-induced dyskinesias detection based on the complexity of involuntary movements. J Neurosci Methods 186:81–89. CrossRefGoogle Scholar
  21. 21.
    Griffiths RI, Kotschet K, Arfon S, Xu ZM, Johnson W, Drago J, Evans A, Kempster P, Raghav S, Horne MK (2012) Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J Parkinsons Dis 2:47–55. Google Scholar
  22. 22.
    Tsipouras MG, Tzallas AT, Rigas G, Tsouli S, Fotiadis DI, Konitsiotis S (2012) An automated methodology for levodopa-induced dyskinesia: assessment based on gyroscope and accelerometer signals. Artif Intell Med 55:127–135. CrossRefGoogle Scholar
  23. 23.
    Ossig C, Antonini A, Buhmann C, Classen J, Csoti I, Falkenburger B, Schwarz M, Winkler J, Storch A (2016) Wearable sensor-based objective assessment of motor symptoms in Parkinson’s disease. J Neural Transm 123:57–64CrossRefGoogle Scholar
  24. 24.
    Del Din S, Godfrey A, Rochester L (2016) Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson’s disease: toward clinical and at home use. IEEE J Biomed Heal Informatics 20:838–847. CrossRefGoogle Scholar
  25. 25.
    Salarian A, Burkhard PR, Vingerhoets FJG, Jolles BM, Aminian K (2013) A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE Trans Biomed Eng 60:72–77. CrossRefGoogle Scholar
  26. 26.
    Niazmand K, Tonn K, Zhao Y et al (2011) Freezing of gait detection in Parkinson’s disease using accelerometer based smart clothes. 2011 IEEE Biomed Circuits Syst Conf 201–204.
  27. 27.
    Moore ST, Yungher DA, Morris TR, Dilda V, MacDougall HG, Shine JM, Naismith SL, Lewis SJG (2013) Autonomous identification of freezing of gait in Parkinson’s disease from lower-body segmental accelerometry. J Neuroeng Rehabil 10:19. CrossRefGoogle Scholar
  28. 28.
    Mazilu S, Hardegger M, Zhu Z et al (2012) Online detection of freezing of gait with smartphones and machine learning techniques. Proc 6th Int ICST Conf Pervasive Comput Technol Healthc 123–130.
  29. 29.
    Coste CA, Sijobert B, Pissard-Gibollet R et al (2014) Detection of freezing of gait in Parkinson disease: preliminary results. Sensors 14:6819–6827. CrossRefGoogle Scholar
  30. 30.
    Tay A, Yen SC, Lee PY et al (2015) Freezing of gait (FoG) detection for Parkinson disease. In: 2015 10th Asian control conference: emerging control techniques for a sustainable world, ASCC 2015Google Scholar
  31. 31.
    Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P (2009) Monitoring motor fluctuations in patients with Parkinsons disease using wearable sensors. IEEE Trans Inf Technol Biomed 13:864–873. CrossRefGoogle Scholar
  32. 32.
    Roy SH, Cole BT, Gilmore LD, de Luca CJ, Thomas CA, Saint-Hilaire MM, Nawab SH (2013) High-resolution tracking of motor disorders in Parkinson’s disease during unconstrained activity. Mov Disord 28:1080–1087. CrossRefGoogle Scholar
  33. 33.
    Cole BT, Roy SH, De Luca CJ, Nawab SH (2014) Dynamical learning and tracking of tremor and dyskinesia from wearable sensors. IEEE Trans Neural Syst Rehabil Eng 22:982–991. CrossRefGoogle Scholar
  34. 34.
    Giuffrida JP, Riley DE, Maddux BN, Heldmann DA (2009) Clinically deployable kinesia technology for automated tremor assessment. Mov Disord 24:723–730. CrossRefGoogle Scholar
  35. 35.
    Das S, Trutoiu L, Murai A et al (2011) Quantitative measurement of motor symptoms in Parkinson’s disease: a study with full-body motion capture data. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf 2011:6789–6792. Google Scholar
  36. 36.
    Giuberti M, Ferrari G, Contin L, Cimolin V, Azzaro C, Albani G, Mauro A (2015) Assigning UPDRS scores in the leg agility task of Parkinsonians: can it be done through BSN-based kinematic variables? IEEE Internet Things J 2:41–51. CrossRefGoogle Scholar
  37. 37.
    Heldman DA, Filipkowski DE, Riley DE et al (2012) Automated motion sensor quantification of gait and lower extremity bradykinesia. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS IEEE, pp 1956–1959Google Scholar
  38. 38.
    Ornelas-Vences C, Sanchez-Fernandez LP, Sanchez-Perez LA, Garza-Rodriguez A, Villegas-Bastida A (2017) Fuzzy inference model evaluating turn for Parkinson’s disease patients. Comput Biol Med 89:379–388. CrossRefGoogle Scholar
  39. 39.
    Garza-Rodriguez A, Sanchez-Fernandez LP, Sanchez-Perez LA, et al (2017) Pronation and supination analysiS based on biomechanical signals from Parkinson’s disease patients. Artif Intell Med In Press:1–16.
  40. 40.
    Sanchez-Perez LA, Sanchez-Fernandez LP, Shaout A, Martinez-Hernandez JM, Alvarez-Noriega MJ (2018) Rest tremor quantification based on fuzzy inference systems and wearable sensors. Int J Med Inform 114:6–17. CrossRefGoogle Scholar
  41. 41.
    Madgwick SOH, Harrison AJL, Vaidyanathan R (2011) Estimation of IMU and MARG orientation using a gradient descent algorithm. In: IEEE International Conference on Rehabilitation Robotics, pp 1–7Google Scholar
  42. 42.
    Madgwick SOH (2010) An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Rep x-io Univ… 32.
  43. 43.
    Salarian A, Russmann H, Wider C, Burkhard PR, Vingerhoets FJG, Aminian K (2007) Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system. IEEE Trans Biomed Eng 54:313–322. CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico
  2. 2.Department of Electrical and Computer EngineeringUniversity of Michigan-DearbornDearbornUSA
  3. 3.Escuela Nacional de Medicina y HomeopatíaInstituto Politécnico NacionalMexico CityMexico

Personalised recommendations