Journal of Neurology

, Volume 265, Issue 11, pp 2656–2665 | Cite as

Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease

  • Franz Marxreiter
  • Heiko Gaßner
  • Olga Borozdina
  • Jens Barth
  • Zacharias Kohl
  • Johannes C. M. Schlachetzki
  • Caroline Thun-Hohenstein
  • Dieter Volc
  • Bjoern M. Eskofier
  • Jürgen Winkler
  • Jochen KluckenEmail author
Original Communication


Mobile, sensor-based gait analysis in Parkinson’s disease (PD) facilitates the objective measurement of gait parameters in cross-sectional studies. Besides becoming outcome measures for clinical studies, the application of gait parameters in personalized clinical decision support is limited. Therefore, the aim of this study was to evaluate whether the individual response of PD patients to dopaminergic treatment may be measured by sensor-based gait analysis. 13 PD patients received apomorphine every 15 min to incrementally increase the bioavailable apomorphine dose. Motor performance (UPDRS III) was assessed 10 min after each apomorphine injection. Gait parameters were obtained after each UPDRS III rating from a 2 × 10 m gait sequence, providing 41.2 ± 9.2 strides per patient and injection. Gait parameters and UPDRS III ratings were compared cross-sectionally after apomorphine titration, and more importantly between consecutive injections for each patient individually. For the individual response, the effect size Cohen’s d for gait parameter changes was calculated based on the stride variations of each gait sequence after each injection. Cross-sectionally, apomorphine improved stride speed, length, gait velocity, maximum toe clearance, and toe off angle. Between injections, the effect size for individual changes in stride speed, length, and maximum toe clearance correlated to the motor improvement in each patient. In addition, significant changes of stride length between injections were significantly associated with UPDRS III improvements. We therefore show, that sensor-based gait analysis provides objective gait parameters that support clinical assessment of individual PD patients during dopaminergic treatment. We propose clinically relevant instrumented gait parameters for treatment studies and especially clinical care.


Parkinson’s disease Sensor-based gait analysis Apomorphine Gait parameter Precision medicine 



The authors would like to thank the study nurses (Kathrin Weinmann and Susanne Seifert) for their support, and the participants in this study.

Author contributions

FM: study concept and design, acquisition of data, analysis and interpretation of data, and wrote the manuscript. HG: acquisition of data, study design, and revision of the manuscript. OB: statistical revision of the manuscript. JS: acquisition of data and revision of the manuscript. Jens Barth: analysis and interpretation of data. ZK: acquisition of data and revision of the manuscript. CT-H: acquisition of data. DV: acquisition of data. BME: analysis and interpretation of data, and revision of the manuscript. JW: acquisition of data, critical revision and editing of manuscript for intellectual content. JK: initiation of the study, acquisition of data, analysis and interpretation of data, and revision and editing of the manuscript.

Compliance with ethical standards

Conflicts of interest

This study was supported by LicherMT GmbH and an intramural grant (Emerging Field Initiative—EFI_Moves) of the Friedrich-Alexander University Erlangen-Nürnberg (FAU). This work has been supported by MoveIT, an EIT Health innovation project. EIT Health is supported by EIT, a body of the European Union. The funding sources had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Franz Marxreiter is supported by the Interdisciplinary Center for Clinical Research (IZKF) at the University Hospital of the University of Erlangen-Nuremberg (Clinician Scientist Program; Junior project 51). He received travel grants from IPSEN and Abbvie Inc. Bjoern M Eskofier reports grants outside the submitted work from Adidas AG, Agaplesion gAG, and Bosch Sensortec GmbH. He received compensation from lecturing for AbbVie Deutschland GmbH & Co. KG and Agaplesion gAG. He is a co-founder and co-owner of Portabiles GmbH and Portabiles HealthCare Technologies GmbH, and co-inventor of gait analysis patent application EP 16174268.9. Jochen Klucken received compensation and honoraria from serving on scientific advisory boards for LicherMT GmbH, Abbvie GmbH, UCB Pharma GmbH and GlaxoSmithKline GmbH & Co. KG, Athenion GmbH, Thomashilfen GmbH and from lecturing for UCB Pharma GmbH, TEVA Pharma GmbH, Licher MT GmbH, Desitin GmbH, Abbvie GmbH, Solvay Pharmaceuticals, and Ever Neuro Pharma GmbH. He holds shares from Portabiles GmbH, Portabiles HCT GmbH, and alpha-Telemed AG and is and co-inventor of gait analysis patent application EP 16174268.9. Jürgen Winkler reports personal fees outside of the submitted work from Desitin Arzneimittel GmbH, Abbvie GmbH & Co. KG, and Biogen GmbH. The other authors declare no competing interests.

Supplementary material

415_2018_9012_MOESM1_ESM.pdf (406 kb)
Supplementary material 1 (PDF 405 KB)


  1. 1.
    Parkinson J (2002) An essay on the shaking palsy. J Neuropsychiatry Clin Neurosci:1–14Google Scholar
  2. 2.
    Hely MA, Morris JGL, Reid WGJ, Trafficante R (2005) Sydney multicenter study of Parkinson’s disease: non-l-dopa-responsive problems dominate at 15 years. Mov Disord 20:190–199. CrossRefPubMedGoogle Scholar
  3. 3.
    Goetz CG, Tilley BC, Shaftman SR et al (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. CrossRefPubMedGoogle Scholar
  4. 4.
    Klucken J, Friedl KE, Eskofier BM, Hausdorf JM (2016) Guest editorial: enabling technologies for Parkinson’s disease management. IEEE J Biomed Health Inform 19:1775–1776. CrossRefGoogle Scholar
  5. 5.
    Maetzler W, Klucken J, Horne M (2016) A clinical view on the development of technology-based tools in managing Parkinson’s disease. Mov Disord. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Espay AJ, Bonato P, Nahab FB et al (2016) Technology in Parkinson’s disease: challenges and opportunities. Mov Disord. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Horak FB, Mancini M (2013) Objective biomarkers of balance and gait for Parkinson’s disease using body-worn sensors. Mov Disord 28:1544–1551. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gassner H, Marxreiter F, Steib S et al (2017) Gait and cognition in Parkinson’s disease: cognitive impairment is inadequately reflected by gait performance during dual task. Front Neurol 8:911–955. CrossRefGoogle Scholar
  9. 9.
    Schlachetzki JCM, Barth J, Marxreiter F et al (2017) Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS One 12:e0183989–e0183918. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Barth J, Oberndorfer C, Pasluosta C et al (2015) Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data. Sensors 15:6419–6440. CrossRefPubMedGoogle Scholar
  11. 11.
    Barth J, Oberndorfer C, Kugler P et al (2012) Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities. In: Conference proceedings: Annual international conference of the IEEE Engineering in medicine and biology society IEEE Engineering in Medicine and Biology Society Conference, vol 2013, pp 6744–6747.
  12. 12.
    Klucken J, Barth J, Kugler P et al (2012) Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PLoS One 8:e56956–e56956. CrossRefGoogle Scholar
  13. 13.
    Sánchez-Ferro Á, Elshehabi M, Godinho C et al (2016) New methods for the assessment of Parkinson’s disease (2005 to 2015): a systematic review. Mov Disord 31:1283–1292. CrossRefPubMedGoogle Scholar
  14. 14.
    Trenkwalder C, Chaudhuri KR, García Ruiz PJ et al (2015) Expert consensus group report on the use of apomorphine in the treatment of Parkinson’s disease—clinical practice recommendations. Parkinsonism Relat Disord 21:1023–1030. CrossRefPubMedGoogle Scholar
  15. 15.
    Slotty PJ, Wille C, Kinfe TM, Vesper J (2014) Continuous perioperative apomorphine in deep brain stimulation surgery for Parkinson’s disease. Br J Neurosurg 28:378–382. CrossRefPubMedGoogle Scholar
  16. 16.
    Stocchi F, Vacca L, De Pandis MF et al (2001) Subcutaneous continuous apomorphine infusion in fluctuating patients with Parkinson’s disease: long-term results. Neurol Sci 22:93–94. CrossRefPubMedGoogle Scholar
  17. 17.
    Hagell P, Odin P, Shing M (2005) Apomorphine in Parkinson’s disease, 2nd edn. Uni-Med Science, Bremen, Germany. Google Scholar
  18. 18.
    Price J, Martin A, Parsons J, Turner M, Ebenezer L, Arnold C, Duggan-Carter P (2016) A case series of rapid titration of subcutaneous apomorphine in Parkinson's disease. Br J Neurosci Nurs 12(2):70–74CrossRefGoogle Scholar
  19. 19.
    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 Psychiatr 55:181–184CrossRefGoogle Scholar
  20. 20.
    Goetz CG, Poewe W, Rascol O et al (2004) Movement disorder society task force report on the Hoehn and Yahr staging scale: status and recommendations. Mov Disord 19:1020–1028. CrossRefPubMedGoogle Scholar
  21. 21.
    Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease (2003) The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommendations. Mov Disord 18:738–750. CrossRefGoogle Scholar
  22. 22.
    Rampp A, Barth J, Schülein S et al (2015) Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng 62:1089–1097. CrossRefPubMedGoogle Scholar
  23. 23.
    Mariani B, Rochat S, Büla CJ, Aminian K (2012) Heel and toe clearance estimation for gait analysis using wireless inertial sensors. IEEE Trans Biomed Eng 59:3162–3168. CrossRefPubMedGoogle Scholar
  24. 24.
    Curtze C, Nutt JG, Carlson-Kuhta P et al (2015) Levodopa is a double-edged sword for balance and gait in people with Parkinson’s disease. Mov Disord 30:1361–1370. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Molecular NeurologyUniversity Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU)ErlangenGermany
  2. 2.Department of Computer Science, Machine Learning and Data Analytics LabFAU Erlangen-NürnbergErlangenGermany
  3. 3.Department of NeurologyPrivatklinik ConfraternitaetViennaAustria
  4. 4.Department of Applied Econometrics and International Political EconomyGoethe University FrankfurtFrankfurtGermany

Personalised recommendations