Extracting Gait Parameters Extracting Gait Parameters from Raw Data - - PowerPoint PPT Presentation

Extracting Gait Parameters from Raw Data Accelerometers

André DIASa,b,c, Lukas GORZELNIAKb, Angela DÖRINGc, Gunnar HARTVIGSENa,d , Alexander HORSCHb,d

aNorwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway bInstitut für Medizinische Statistik und Epidemiologie, Technische Universität München, Germany cInstitute of Epidemiology, Helmholtz Zentrum München, Germany dComputer Science Department, University of Tromsø, Norway

Extracting Gait Parameters from Raw Data Accelerometers

MIE 2011, Oslo

Introduction

• Gait parameters are

important for Gait impairment assessment

• Recovery therapy
• Several Methods

– Force plates – Pressure activated sensors – Motion analysis from video

http://amti.biz/

Force plates

http://www.mar-systems.co.uk

Video analysis

MIE 2011, Oslo

Motivation

• All existing methods are only feasible in

controlled settings [setup, cost, labour]

• Accelerometers have reached a stage

where high frequency raw data is possible

– Cheap & easy method to estimate gait parameters

MIE 2011, Oslo

Goal

• Can we extract basic parameters from

raw data collected with accelerometers?

• How do the results compare to a de-

facto standard?

MIE 2011, Oslo

Methods - Material

• GAITrite walkway (de-facto standard)
• GT3X

– 30 Hz, tri-axial – Both Legs, Both Arms

MIE 2011, Oslo

Methods - Subjects

• KORA-Age study
• 70 subjects * 4 walks each

– Normal walk – Slow walk – Running – Performing mental task (counting backwards)

MIE 2011, Oslo

Software

• First step: extract gait parameters from

GaitRite data

– Closed source, unstable software provided by GaitRite – Develop raw data processing tool

• Second step: estimate gait parameters

from accelerometer data

– Filters and peak detection

MIE 2011, Oslo

Results - GT3X

• Visual indications

MIE 2011, Oslo

• Algorithm

Selles et al 2005*

• Second
• rder

Butterworth filter with cut

• ff

frequencies

* Selles et al.; Automated Estimation

• f Initial and Terminal Contact Timing

Using Accelerometers; Development and Validation in Transtibial Amputees and Controls; IEEE Tran

• n neural systems and rehabilitation

eng, vol. 13, no. 1, 2005

Results – Gait parameters

MIE 2011, Oslo

It works... with a few subjects. But for most is a miserable failure

MIE 2011, Oslo

Conclusions

• Promising approach

– Significant room for improvement

• Sampling frequency still not enough?
• More work on algorithms?
• Future work

– Algorithm from Jung-Ah Lee et al 2010 – E-ar sensor [located in the ear, natural balance “centre”]

MIE 2011, Oslo

Questions?

• andre.dias@uit.no

Thanks to Matej Svedja, Jennifer Reinelt; Moritz Fuchs for the valuable help. This research was funded/supported by the Graduate School of Information Science in Health (GSISH) and the Technische Universität München Graduate School. A. Dias is supported by scholarship SFRH/BD/39867/2007 of the Portuguese Foundation for Science and Technology and Research Council of Norway Grant No. 174934.

MIE 2011, Oslo

It works... with a few subjects. But for most is a miserable failure

MIE 2011, Oslo

Results

• Algorithm from &&&&

– $$$$ filter + peak detection

MIE 2011, Oslo

Conclusions

• Promising approach

– Significant room for improvement

• Sampling frequency still not enough?
• More work on algorithms?
• Future work

– Algorithm from ???? – E-ar sensor [located in the ear, natural balance “centre”]