Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

Abstract

The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson’s disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5–100%, sensitivity of 83.3–100% and specificity of 82–100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8–100%, sensitivity of 92.5–100% and specificity of 88–100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.

Study of the Distortion of the Indirect Angular Measurements of the Calcaneus Due to Perspective: In Vitro Testing

“…The stance phase is the period during which the foot is in contact with the support surface within one gait cycle. The swing phase is the period during which the foot is airborne within one gait cycle [ 30 ].…”

Wearables als unterstützendes Tool für den Paradigmenwechsel in der Versorgung von Parkinson Patienten

“…Digitale Bewegungsanalysen messen z. B. die Ganggeschwindigkeit, Variabilität und Symmetrie des Gangmusters oder den Tremor sensitiver und unabhängiger und sind damit ein verlässlicherer Studienendpunkt, sowohl im klinischen Setting als auch zu Hause [10]. Auch könnten dadurch Studienkosten reduziert werden, da weniger klinische Studien Visiten nötig sind [9].…”

“…Eine Vielzahl von Wearables erfüllen diese Anforderungen und zahlreiche systematische Reviews beleuchten dies kritisch. Die zur Ganganalyse eingesetzten Algorithmen haben eine Genauigkeit, Sensitivität und Spezifität von 85-100 % [8][9][10]. Es werden verschiedene Technologien zur Beurteilung von Bradykinese [10,18,20], Tremor [19,20] und axialen Symptomen [10,20] eingesetzt.…”

“…Zu den spatiotemporalen Merkmalen zählen die Standphase, Schwungphase, doppelte Bein Unterstützung, Einbein-Unterstützung, Schrittlänge, Schrittdauer und Schrittbreite. Die Kinematik misst den Bewegungsablauf, meist per Video, die Kinetik die Wirkung von Kräften und deren Effekt auf die Bewegung[10].…”

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

“…For patients with Parkinson's disease, various technologies have been proposed for gait analysis, together with different algorithms, for the diagnosis and monitoring of symptoms. Only a few of these technologies and algorithms, however, have been proven useful in clinical settings, and none has already been validated and used on a large scale [57].…”