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Mobile Gait Analysis: from Prototype Towards Clinical Grade Wearable FAU Studien aus der Informatik 6 Julius Hannink Mobile Gait Analysis: From Prototype towards Clinical Grade Wearable Julius Hannink Mobile Gait Analysis: From Prototype towards Clinical Grade Wearable FAU Studien aus der Informatik Band 6 Herausgeber der Reihe: Björn Eskofier, Richard Lenz, Andreas Maier, Michael Philippsen, Lutz Schröder, Wolfgang Schröder-Preikschat, Marc Stamminger, Rolf Wanka Julius Hannink Mobile Gait Analysis: From Prototype towards Clinical Grade Wearable Erlangen FAU University Press 2019 Bibliografische Information der Deutschen Nationalbibliothek: Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.d-nb.de abrufbar. Das Werk, einschließlich seiner Teile, ist urheberrechtlich geschützt. Die Rechte an allen Inhalten liegen bei ihren jeweiligen Autoren. Sie sind nutzbar unter der Creative Commons Lizenz BY-NC. Der vollständige Inhalt des Buchs ist als PDF über den OPUS Server der Friedrich-Alexander-Universität Erlangen-Nürnberg abrufbar: https://opus4.kobv.de/opus4-fau/home Bitte zitieren als Hannink, Julius. 2019. Mobile Gait Analysis: From Prototype towards Clinical Grade Wearable. FAU Studies FAU Studien aus der Informatik Band 6. Erlangen: FAU University Press. DOI: 10.25593/978-3-96147-173-7 Verlag und Auslieferung: FAU University Press, Universitätsstraße 4, 91054 Erlangen Druck: docupoint GmbH ISBN: 978-3-96147-172-0 (Druckausgabe) eISBN: 978-3-96147-173-7 (Online-Ausgabe) ISSN: 2509-9981 DOI: 10.25593/978-3-96147-173-7 Mobile Gait Analysis: From Prototype towards Clinical Grade Wearable Mobile Ganganalyse: Vom Prototyp in Richtung klinisch anwendbarer Systeme Der Technischen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades Dr.-Ing. vorgelegt von Julius Hannink aus Oldenburg (Oldb) Als Dissertation genehmigt von der Technischen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg Tag der Promotion: 06.09.2018 Vorsitzender des Promotionsorgans : Prof. Dr.-Ing. Reinhard Lerch Gutachter: Prof. Dr. Björn M. Eskofier Prof. Dr. Lorenzo Chiari Prof. Dr. Jochen Klucken Zusammenfassung Ziel der vorliegenden Arbeit ist es, mobile Ganganalysesysteme ba- sierend auf Inertialsensorik in Richtung klinischer Anwendbarkeit zu führen. Solche Geräte sollen sowohl im klinischen Alltag zur objektiven Gangbeurteilung unter kontrollierten Bedingungen, als auch außerhalb des klinischen Umfeldes eingesetzt werden. Diese Art der Anwendungen erfordern jedoch die behördlich attestierte, klinische Qualität des jeweiligen tragbaren Geräts, was ein hohes Maß wissenschaftlicher Forschung voraussetzt. Die vorliegende Arbeit bewegt sich in drei Aspekten auf dieses Ziel zu: (1) Verschiedene Methoden zur Rekonstruktion der Fußtrajek- torie werden standardisiert und auf identischer Datenbasis vergli- chen, (2) die Schrittparametrisierung wird um kinetische Merkmale erweitert und (3) kritische Annahmen, auf denen aktuelle mobi- le Ganganalysesysteme aufbauen, werden aufgehoben, um den Anwendungsbereich dieser Systeme zu erweitern. In Bezug auf die Rekonstruktion der Fußtrajektorie aus den Messda- ten werden drei Orientierungsbestimmungen und drei Doppelin- tegrationsschemata aus der Literatur herangezogen. Jede dieser Methoden wird bei gesunden Kontrollen gegen ein Motion-Capture- System evaluiert sowie untereinander verglichen. Hier wird eine Lücke in bestehender Literatur geschlossen, der derzeit ein fairer Vergleich von Rekonstruktionsverfahren auf einem identischen Da- tensatz fehlt. Weiterhin werden die messbaren Schrittparameter durch Surrogat- marker für Bodenkontaktkräfte ergänzt. Dadurch können nicht nur räumliche und zeitliche, sondern auch kinetische Parameter mittels mobiler Ganganalyse betrachtet werden. Diese kinetische Charak- terisierung einzelner Schritte wird in einer Studie zur posturalen Stabilität bei 200 Patienten mit Parkinson und einer Kontrollgruppe von 100 Gesunden klinisch angewendet. Hier werden erste Erkennt- nisse zu quantifizierbaren, mobil erfassten Gangmaßen hinsichtlich posturaler Stabilität bei Parkinson gewonnen. Schließlich beurteilen Experimente zu einem weniger einschrän- kenden Satz an Grundannahmen in mobilen Ganganalysesystemen die datengetriebene Bestimmung von Schrittparametern mittels tiefer Lernverfahren. Am Beispiel der Schrittlänge wird die tech- nische Machbarkeit bei drei zu Grunde liegenden Schrittdefinitio- nen betrachtet, bevor ein Heel-Strike basiertes Modell in exem- plarischen Querschnitts- und Längsschnittstudien beim Parkin- son Syndrom klinische Anwendung findet. Die Erweiterung auf eine Heel-Strike basierte Bestimmung von fünf räumlichen und zeitlichen Schrittparametern untersucht dann zwei exemplarische Netzwerkarchitekturen zur Bestimmung der Gangparameter. Das erfolgreichere Modell kommt anschließend in einer Interventions- studie im geriatrischen Bereich zur klinischen Anwendung. Diese Beiträge stellen die ersten Untersuchungen tiefer Lernverfahren zur mobilen Bestimmung von Schrittparametern dar und eröffnen den Zugang zu einem breiten Spektrum verwandter theoretischer und angewandter Forschungsfragen. Zusammenfassend bringt die vorliegenden Arbeit mobile Gangana- lyse näher an den klinischen Alltag indem methodische Entschei- dungen in der Signalverarbeitungskette standardisiert verglichen werden, der Kreis der erfassbaren biomechanischen Variablen er- weitert wird und kritische Annahmen fallen gelassen werden. Vor allem Letzteres ist hervorzuheben, da die Annahmen mobiler Gang- analysesysteme deren Einsatz im klinischen Alltag aktuell aufgrund der Vielzahl an vorhandenen Gangstörungen einschränken. Somit trägt die vorliegende Arbeit zur Vision der breiten klinischen An- wendbarkeit objektiver Ganganalyse mittels tragbarer Technologie bei. Diese soll bei der Überwachung von Therapien bzw. Krankheits- verläufen zum Einsatz kommen sowie zeitlich stark schwankende Symptome, seltene Ereignisse oder langfristiges Patientenverhal- ten außerhalb des klinischen Umfeldes charakterisieren. Die voll- ständige Integration mobiler Ganganalyse in die klinische Routine verspricht dabei letztendlich eine Verbesserung der individuellen Patientenversorgung, die aktuell um einzeitige Messungen herum organisiert ist. Abstract The aim of this thesis is to move mobile gait analysis systems based on inertial sensing closer towards clinical grade wearable devices. Such devices are envisioned to be used in everyday clinical practice for objective gait assessment under supervised conditions as well as for remote monitoring of gait in real-life environments. Such applications, however, require clinical grade of the wearable device established through clearance by the authorities and this process needs to be based on scientific research. The present thesis moves towards this aim in three main areas: Benchmarking methodological choices in foot trajectory recon- struction, extending the stride parameterization with kinetic fea- tures and reducing the assumption set current mobile gait analysis systems are built upon in order to widen the scope of gait disorders these systems can be used in. Regarding the benchmarking of methods for foot trajectory recon- struction from the measured data, three orientation estimation and three double integration schemes are drawn from literature. Each of these is evaluated in healthy controls against a motion cap- ture system to assess estimation performance. This closes a gap in existing literature that is currently missing a fair comparison of reconstruction techniques on an identical dataset. Further, the space of measurable stride parameters is comple- mented with surrogate markers for ground reaction forces. Thereby, not only spatial or temporal, but also kinetic parameters can be assessed by means of mobile gait analysis. This kinetic characteri- zation of strides is applied in a clinical study on postural instability in 200 Parkinson’s disease patients and 100 healthy controls. As a result, first insights into quantifiable gait measures from inertial sensing that express impairments in postural control are gained. Finally, investigations into a less constraining assumption set ex- plore data driven estimation of stride parameters with deep convo- lutional neural network regression regarding technical feasibility as well as potential clinical applications. On the example of stride length as a parameter, different underlying stride definitions are evaluated before a heel-strike based estimation of stride length is applied in two exemplary cross-sectional as well as longitudinal studies in Parkinson’s disease. Extension to heel-strike based esti- mation of five spatial as well as temporal stride parameters then explores different network architectures (joint vs. individual mod- elling) before applying the superior model in an interventional study with geriatric patients. These contributions represent the first investigations into deep learning as a method for mobile esti- mation of stride parameters and open the door to a vast scope of related theoretical as well as experimental research questions. To conclude, the present thesis brings mobile gait analysis closer to clinical grade by benchmarking methodological choices, extend- ing the space of assessable biomechanical variables and dropping critical assumptions. Especially the latter currently limits applica- tion of these systems in daily clinical routine due to the variety of gait impairments present. By addressing these points, the current
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