The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations

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Autor(en): Gaßner H, Raccagni C, Eskofier B, Klucken J, Wenning GK
Zeitschrift: Frontiers in Neurology
Jahr der Veröffentlichung: 2019
Band: 10
ISSN: 1664-2295


Background: Differentiating idiopathic Parkinson's disease (IPD) from atypical Parkinsonian disorders (APD) is challenging, especially in early disease stages. Postural instability and gait difficulty (PIGD) are substantial motor impairments of IPD and APD. Clinical evidence implies that patients with APD have larger PIGD impairment than IPD patients. Sensor-based gait analysis as instrumented bedside test revealed more gait deficits in APD compared to IPD. However, the diagnostic value of instrumented bedside tests compared to clinical assessments in differentiating APD from IPD patients have not been evaluated so far. Objective: The objectives were (a) to evaluate whether sensor-based gait parameters provide additional information to validated clinical scores in differentiating APD from matched IPD patients, and (b) to investigate if objective, instrumented gait assessments have comparable discriminative power to clinical scores. Methods: In a previous study we have recorded instrumented gait parameters in patients with APD (Multiple System Atrophy and Progressive Supranuclear Palsy). Here, we compared gait parameters to those of retrospectively pairwise disease duration-, age-, and gender-matched IPD patients in order to address this new research questions. To this aim, the PIGD score was calculated as sum of the MDS-UPDRS-3-items "gait," "postural stability," "arising from chair," and "posture." Gait characteristics were evaluated in standardized gait tests using an instrumented, sensor-based gait analysis system. Machine learning algorithms were used to extract spatio-temporal gait parameters. Receiver Operating Characteristic analysis was performed in order to detect the discriminative power of the instrumented vs. the clinical bedside tests in differentiating IPD from APD. Results: Sensor-based stride length, gait velocity, toe off angle, and parameters representing gait variability significantly differed between IPD and APD groups. ROC analysis revealed a high Area Under the Curve (AUC) for PIGD score (0.919), and UPDRS-3 (0.848). Particularly, the objective parameters stance time variability (0.841), swing time variability (0.834), stride time variability (0.821), and stride length variability (0.804) reached high AUC's as well. Conclusions: PIGD symptoms showed high discriminative power in differentiating IPD from APD supporting gait disorders as substantial diagnostic target. Sensor-based gait variability parameters provide metric, objective added value, and serve as complementary outcomes supporting clinical diagnostics and long-term home-monitoring concepts.

FAU-Autoren / FAU-Herausgeber

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Gaßner, Heiko Dr. phil.
Molekular-Neurologische Abteilung in der Neurologischen Klinik
Klucken, Jochen Prof. Dr.
Molekular-Neurologische Abteilung in der Neurologischen Klinik

Autor(en) der externen Einrichtung(en)
Medizinische Universität Innsbruck


Gaßner, H., Raccagni, C., Eskofier, B., Klucken, J., & Wenning, G.K. (2019). The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations. Frontiers in Neurology, 10.

Gaßner, Heiko, et al. "The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations." Frontiers in Neurology 10 (2019).


Zuletzt aktualisiert 2019-20-03 um 06:02