Gait and Balance Assessments in Patients and Weightlifting Athletes Using Inertial and Pressure Sensors
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- Lilli Schmitt
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1 DISS. ETH NO Gait and Balance Assessments in Patients and Weightlifting Athletes Using Inertial and Pressure Sensors A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by Rolf Simon Adelsberger MSc ETH in Computer Science born on citizen of Stein AR, Switzerland accepted on the recommendation of Prof. Dr. Gerhard Tröster, examiner Prof. Dr. Dominik Straumann, co-examiner 2014
2 Abstract Movement analyses focusing on the walking frame are used in multiple disciplines. However, the existing approaches differ in terms of complexity and significance. There are various methods and sensor systems that sample and analyze human motion. There are clinical methods that base on questionnaires, others require expert knowledge, or use technology to assess the movements of subjects. In sports, expert knowledge and technology are used to assess movements of atheltes. The approaches differ significantly in terms of overhead, objectivity and validity. In this thesis, we address the complementary properties of existing methods and systems. It was our goal to create an autonomous (wearable) multi-modal sensor system. We required the system to be able to detect different static postures: to sit, to stand to lie etc. The system further should be able to distinguish between dynamic situation such as to walk, to run, climb stairs etc. We decided to combine an inertial measurement unit with a high-resolution plantar-pressure sensor to be controlled by a smart phone. Temporal properties of gait and spatial dynamics can be assessed with the IMU. Estimations of dynamic postural configurations and discrimination between multiple static states can be performed with the pressure sensor. The use of a smart phone and its display capabilities allow the creation of a feedback loop that is important for training (physio therapy or athletics). In the first part of this work, we introduce the physical properties and possibilities of our sensor system. Also, we elaborate on algorithms implemented in firmware and software. In a medical context, we address gait analyses and assessments of balance. We show that a single device suffices to answer specific questions of clinical relevance by analyzing the step-frequency variability of subjects during dual-task exposure. We use both modalities of our system to show that functional gait analyses (FGA) can be performed to some extent automatically. Further, we focus on balance assessments and show that the system can be used in assessments of patients with benign paroxysmal positional vertigo, BPPV. In weightlifting, the temporal synchronicity between various muscular groups is important for a sucessful execution of various exer-
3 x cises. We analyzed the accuracy in movement during a complex barbell movement with the IMU part of our sensor system. Further, we show that stretching exercises have an impact on weightlifing athletes. We analyzed characteristica of the center-of-pressure (COP) during various weightlifting exercises.
4 Zusammenfassung Bewegungsanalysen mit Fokus auf den Gehapparat werden vielseitig angewandt. Existierende Analyseansätze unterscheiden sich in der technologischen Komplexität und der Aussagekraft. So gibt es klinische Analysen, die auf Fragebögen basieren, solche, die von medizinischen Experten-(wissen) abhängen oder aber auch Analysen, die mit technologischer Unterstützung die Patienten beurteilen. Im Sportkontext werden meist Expertenwissen oder technische Systeme zur Bewegungsanalyse eingesetzt. Diese Ansätze unterscheiden sich jeweils stark im Durchführungsaufwand, Objektivität und in der Aussagekraft. In dieser Arbeit adressieren wir die komplementären Fähigkeiten existierender Analysen und Analysesystemen und vereinen sie in einem neuen System. Ein Ziel unserer Arbeit war, ein autonomes, tragbares, multimodales Sensorsystem zu entwickeln, das zwischen mehreren posturalen statischen Zuständen (Sitzen, Stehen, Liegen etc.) sowie verschiedenen dynamischen Zuständen unterscheiden kann (Laufen, Gehen, Treppensteigen etc.). Wir entschieden uns für die Kombination einer "Inertial-Measurement-Unit" (IMU) mit einer hoch-aufgelösten Drucksensorik mit 1260 Sensorpunkten und einer Kontroll- und Anzeigeeinheit (Smart-Phone). Zeitliche Eigenschaften des Gehverhaltens, aber auch räumliche Dynamik können mit der IMU erfasst werden. Annäherungen an dynamische posturale Konfigurationen sowie Unterscheidungen zwischen verschiedenen Zuständen können mit der Drucksensorik erreicht werden. Zudem erlaubt es das System einen Trainingskreislauf zu generieren, indem Daten z.b. an ein Smart-Phone weiter geleitet werden können. Es kann dadurch anwendungsabhängig auch als Trainingswerkzeug eingesetzt werden. Im ersten Teil dieser Arbeit erklären wir die physikalischen Eigenschaften und Möglichkeiten unseres Sensorsystems sowie algorithmische Details der Firmware und anderer Software-Komponenten. Im medizinischen Kontext setzen wir uns mit Gang-Analysen (gait analysis) und Analysen des Gleichgewichts (balance) auseinander. Wir zeigen, dass eine IMU-Sensoreinheit reicht, um klinisch relevante Fragen bezüglich der Zusammenhänge zwischen Kognition und posturaler Stabilität zu beantworten. Dazu analysierten wir die
5 xii Gangfrequenz-Variabilität von älteren Probanden in einer Dual-Task- Aufgabe. Weiter nutzten wir beide Modalitäten des Systems, um zu zeigen, dass klinische "Functional Gait Assessments" (FGA) automatisch durchgeführt werden können, indem Gang- und Gleichgewichtsparameter während FGA erfasst und ausgewertet werden. Schliesslich fokussierten wir auf die Gleichgewichts-Schätzung, als wir zeigten, dass das System bei der Diagnose von Patienten mit "Benignem Paroxysmalen Positionalen Vertigo" (BPPV, gutartiger Lagerungsschwindel) eingesetzt werden kann, indem das Gleichgewicht bei Romberg-Tests analysiert wird. Wir konnten zeigen, dass sich BPPV-Patienten von gesunden Probanden in verschiedenen Gleichgewichtsparametern unterscheiden. Beim Gewichtheben ist das Zusammenspiel verschiedener Muskelgruppen wichtig für eine erfolgreiche Ausführung verschiedener Übungen. Wir analysierten mit der IMU-Komponente unseres Sensor- Systems die Genauigkeit verschiedener Athleten bei einer komplexen Langhantel-Übung. Dazu erfassten wir die Bewegungen von Rumpf und Handgelenk und zeigten, dass sich erfahrene Athleten signifikant von Anfängern bezüglich der zeitlichen Synchronisation unterscheiden. Weiter zeigten wir auf, wie sich Übungen zur Flexibilitätserhöhung auf Gleichgewicht und Massezentrum beim Gewichtheben auswirken. Dazu analysierten wir Charakteristika vom "Center-of- Pressure" (COP) von 13 Athleten während der Durchführung verschiedener Übungen.
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