Abstract
This article outlines recent developments in the clinical and automated assessment of neurological gait disorders. With a primary focus on vestibular, cerebellar, and functional gait disorders, we discuss how instrumented gait examination may assist clinical decision making in these disorders with respect to the initial differential diagnosis and prognosis as well as the objective monitoring of disease progression and therapeutic interventions. We delineate strategies for data handling and analysis of quantitative gait examinations that can facilitate the clinical characterization and interpretation of walking impairments. These strategies include data normalization and dimensionality reduction procedures. We further emphasize the value of a comprehensive, standardized gait assessment protocol. Accordingly, the examination of walking conditions that challenge patients with respect to their biomechanical, sensory, or cognitive resources are particularly helpful to disclose and characterize the causes underlying their gait impairment. Finally, we provide a perspective on the emerging implementation of pattern recognition approaches within the framework of clinical management of gait disorders and discuss their potential to assist clinical decision making with respect to the differential diagnosis and the prognosis of fall risk in individual patients.
Similar content being viewed by others
References
Dietz V (1992) Human neuronal control of automatic functional movements: interaction between central programs and afferent input. Physiol Rev 72(1):33–69
Angelaki DE, Cullen KE (2008) Vestibular system: the many facets of a multimodal sense. Annu Rev Neurosci 31:125–150
Ito M (1984) The cerebellum and neural control. Raven Press, Nashville
Snijders AH, van de Warrenburg BP, Giladi N, Bloem BR (2007) Neurological gait disorders in elderly people: clinical approach and classification. Lancet Neurol 6(1):63–74
Schlick C, Schniepp R, Loidl V, Wuehr M, Hesselbarth K, Jahn K (2016) Falls and fear of falling in vertigo and balance disorders: A controlled cross-sectional study. J Vestib Res 25(5–6):241–251
Müller B, Ilg W, Giese MA, Ludolph N (2017) Validation of enhanced kinect sensor based motion capturing for gait assessment. PLoS One 12(4):e0175813
Schmitz-Hübsch T, Brandt AU, Pfueller C, Zange L, Seidel A, Kühn AA et al (2016) Accuracy and repeatability of two methods of gait analysis—GaitRite™ und Mobility Lab™—in subjects with cerebellar ataxia. Gait Posture 48:194–201
Krafczyk S, Tietze S, Swoboda W, Valkovic P, Brandt T (2006) Artificial neural network: a new diagnostic posturographic tool for disorders of stance. Clin Neurophysiol 117(8):1692–1698
Lord S, Galna B, Verghese J, Coleman S, Burn D, Rochester L (2013) Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. J Gerontol A Biol Sci Med Sci 68(7):820–827
Schniepp R, Wuehr M, Huth S, Pradhan C, Brandt T, Jahn K (2014) Gait characteristics of patients with phobic postural vertigo: effects of fear of falling, attention, and visual input. J Neurol 261(4):738–746
Schniepp R, Kugler G, Wuehr M, Eckl M, Huppert D, Huth S et al (2014) Quantification of gait changes in subjects with visual height intolerance when exposed to heights. Front Hum Neurosci 8:963
Schniepp R, Wuehr M, Neuhaeusser M, Kamenova M, Dimitriadis K, Klopstock T et al (2012) Locomotion speed determines gait variability in cerebellar ataxia and vestibular failure. Mov Disord 27(1):125–131
Schniepp R, Schlick C, Schenkel F, Pradhan C, Jahn K, Brandt T et al (2017) Clinical and neurophysiological risk factors for falls in patients with bilateral vestibulopathy. J Neurol 264(2):277–283
Schniepp R, Wuehr M, Schlick C, Huth S, Pradhan C, Dieterich M et al (2014) Increased gait variability is associated with the history of falls in patients with cerebellar ataxia. J Neurol 261(1):213–223
Schniepp R, Schlick C, Pradhan C, Dieterich M, Brandt T, Jahn K et al (2016) The interrelationship between disease severity, dynamic stability, and falls in cerebellar ataxia. J Neurol 263(7):1409–1417
Dakin CJ, Inglis JT, Chua R, Blouin JS (2013) Muscle-specific modulation of vestibular reflexes with increased locomotor velocity and cadence. J Neurophysiol 110(1):86–94
Straka H, Simmers J, Chagnaud BP (2018) A new perspective on predictive motor signaling. Curr Biol 28(5):R232–R243
Ilg W, Golla H, Thier P, Giese MA (2007) Specific influences of cerebellar dysfunctions on gait. Brain 130(Pt 3):786–798
Palliyath S, Hallett M, Thomas SL, Lebiedowska MK (1998) Gait in patients with cerebellar ataxia. Mov Disord 13(6):958–964
Brandt T, Kugler G, Schniepp R, Wuehr M, Huppert D (2015) Acrophobia impairs visual exploration and balance during standing and walking. Ann N Y Acad Sci 1343:37–48
Wuehr M, Brandt T, Schniepp R (2017) Distracting attention in phobic postural vertigo normalizes leg muscle activity and balance. Neurology 88(3):284–288
Sokol LL, Espay AJ (2016) Clinical signs in functional (psychogenic) gait disorders: a brief survey. J Clin Mov Disord 3:3
Lempert T, Brandt T, Dieterich M, Huppert D (1991) How to identify psychogenic disorders of stance and gait. A video study in 37 patients. J Neurol 238(3):140–146
Bessot N, Denise P, Toupet M, Van Nechel C, Chavoix C (2012) Interference between walking and a cognitive task is increased in patients with bilateral vestibular loss. Gait Posture 36(2):319–321
Wuehr M, Schniepp R, Schlick C, Huth S, Pradhan C, Dieterich M et al (2014) Sensory loss and walking speed related factors for gait alterations in patients with peripheral neuropathy. Gait Posture 39(3):852–858
Schniepp R, Wuehr M, Huth S, Pradhan C, Schlick C, Brandt T et al (2014) The gait disorder in downbeat nystagmus syndrome. PLoS One 9(8):e105463
Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56
Pradhan C, Wuehr M, Akrami F, Neuhaeusser M, Huth S, Brandt T et al (2015) Automated classification of neurological disorders of gait using spatio-temporal gait parameters. J Electromyogr Kinesiol 25(2):413–422
Brandt T, Strupp M, Novozhilov S, Krafczyk S (2012) Artificial neural network posturography detects the transition of vestibular neuritis to phobic postural vertigo. J Neurol 259(1):182–184
Joyseeree R, Abou Sabha R, Mueller H (2015) Applying machine learning to gait analysis data for disease identification. Stud Health Technol Inform 210:850–854
Acknowledgment
The work was supported by the German Federal Ministry for Education and Science (BMBF, IFB 01EO1401).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
This manuscript is part of a supplement sponsored by the German Federal Ministry of Education and Research within the funding initiative for integrated research and treatment centers.
Rights and permissions
About this article
Cite this article
Schniepp, R., Möhwald, K. & Wuehr, M. Clinical and automated gait analysis in patients with vestibular, cerebellar, and functional gait disorders: perspectives and limitations. J Neurol 266 (Suppl 1), 118–122 (2019). https://doi.org/10.1007/s00415-019-09378-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00415-019-09378-x