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Annika Mücke

  • Job title: Bachelor's Thesis

Advisors
Martin Ullrich, M. Sc.,Arne Küderle, M. Sc.

Duration
05/2020 – 10/2020

Abstract

Motor impairments, often accompanied with postural instability and freezing of gait, are a severe
symptom in Parkinson’s disease (PD). Consequently, the risk for injury-causing falls is increased
for PD patients. On average, 39 % of all Parkinson’s patients fall recurrently [1]. Sensor-based
gait analysis using inertial measurement units (IMUs) has been shown to be a helpful tool for
determining the severity of the disease objectively. Based on gyroscope and accelerometer data
from IMUs, spatio-temporal parameters such as stride length, stance and swing phase time or
gait velocity can be determined from supervised 4×10 m or other standardized gait tests. These
parameters can be utilized to assess the stage of disease [2] or predict the risk of falling [3] of a
patient.
The recording of data can either be done in a clinical setting or under free-living conditions
using long-term mobile gait monitoring. The latter is advantageous since it provides a comprehensive,
continuous insight into the patient’s condition rather than a momentary impression [3].
However, the analysis of IMU signals recorded in free-living conditions is challenging as it contains
the data from arbitrary activities in contrast to supervised gait tests where only walking is
recorded. Therefore, semi-standardized gait tests recorded in the patients’ home environment can
provide helpful to obtain walking data from repetitive conditions in terms of location and time
during the day. For an efficient analysis of these gait tests, a reliable timestamp annotation of
their executions is required for the identification of the respective data from the continuous sensor
signal streams.
The MaD Lab currently runs a joint study with the University Hospital (FallRiskPD) where
machine learning algorithms are investigated for the prediction of fall risk from long-term IMU
gait data [4]. For a period of two weeks, patients were instructed to wear IMU sensors, attached to
both feet and the lower back, during their daily activities. Additionally, they were asked to perform
4x10m tests at different speeds as well as a Timed Up and Go test in their home environment three
times a day. To extract those gait test sequences from the long-term data for further processing, the
corresponding timestamps are required. For approximate annotations, a smartphone application
was provided to the participants. Because this constitutes a possible source of error, a manual
verification and refinement or correction of false labels is necessary.
The main goal of this thesis is to develop an algorithm for gait test detection in free-living
gait data that does not require manual labeling or smartphone annotations. The key benefits are
not only to reduce the amount of time needed for data processing, but also to decrease the effort
for the patients. Previously, a pipeline for automatic detection and labeling of clinical gait tests
was proposed, consisting of the following steps [5]: First, gait sequences are extracted by detecting
patterns of harmonics in the frequency domain of the gyroscope signal [6]. Afterwards, the gait
sequences are classified using subsequent dynamic time warping. It allows scaling and stretching
invariant template matching [7] and is thus suitable for gait tests carried out at different selfselected
speeds and under varying conditions regarding the exact length of the walking path. In a
postprocessing step, the results are verified by checking the amount of steps and turning sequences
within the detected gait tests.
For this thesis the existing pipeline (including gait sequence detection and template matching)
will be adapted for the applicability to home monitoring data as recorded in the FallRiskPD study.
One persisting problem is the detection of individual gait tests executed without any considerable
resting periods between them. Furthermore, the sensitivity of the algorithm for varying walking
speeds needs to be improved. Within this thesis, the pipeline is going to be adapted accordingly
and validated against the expert-labeling of the free-living gait data.

 

References:

[1] Allen, Natalie et al.: Recurrent Falls in Parkinson’s Disease: A Systematic Review. Parkinson’s
disease, 906274, 2013.
[2] Schlachetzki, Johannes et al.: Wearable sensors objectively measure gait parameters in
Parkinson’s disease. PLOS ONE 12.10, e0183989, 2017.
[3] Del Din, Silvia et al.:Analysis of Free-Living Gait in Older Adults With and Without
Parkinson’s Disease and With and Without a History of Falls: Identifying Generic and
Disease-Specific Characteristics. The Journals of Gerontology: Series A 74.4, 500-506, 2019.
[4] https://www.mad.tf.fau.de/research/projects/fallriskpd/, accessed: 2020-04-15.
[5] Fischer, Stefan et al.: Macro Analysis of free-living Gait in Parkinson’s Disease. Bachelors
Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019.
[6] Ullrich, Martin et al.: Detection of Gait From Continuous Inertial Sensor Data Using Harmonic
Frequencies. IEEE Journal of Biomedical and Health Informatics, 1-1, 2020.
[7] Barth, Jens et al.: Stride Segmentation during Free Walk Movements Using Multi-
Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data. Sensors 15.3,
6419-6440, 2005.