Anne-Marie Strauch

Anne-Marie Strauch

Master's Thesis

Identification of signal features leading to reduced validity of IMU-based gait (variability) parameters


Malte Ollenschläge (M.Sc.), Arne Küderle (M.Sc.), Dr. Felix Kluge, Prof. Dr. Björn Eskofier, 




The way we walk can tell a lot about us. Gait analysis can be used for example in diagnosing
pathologies [1], monitoring therapy success [2], or assessing fall risk [3]. However, gold standard
methods for obtaining gait parameters, like step width or time, require expensive equipment and
are often constrained to laboratory conditions [4].
Body worn inertial measurement units (IMUs), which consist of accelerometers and gyroscopes,
allow for ubiquitous gait analysis. They are cheap, unobtrusive, do not require laboratory
conditions and therefore allow for the analysis of gait parameters in more situations and contexts
[4]. Thus, they can complement more clinical approaches of disease diagnosis and monitoring
treatments, like for example the Unied Parkinsons’s Disease Rating Scale [5, 6].
IMU-based gait parameters are typically obtained using double-integration [7] or complementary/
Kalman lters [8]. Some recent approaches also use neural networks [9]. These methods
achieve a mean error in stride length estimation below one centimeter on average over multiple
strides, but the standard deviation of this error is higher and seldom below six centimeters [9].
This means that IMU-derived gait parameters show a good validity for mean parameters, but
poor validity for individual strides and measures of gait variability.
In contrast to mean parameters, the validity for variability parameters is less often reported
and partly delivers contradicting results. The variability measures which are most often examined
include standard deviation or root mean squares and the coecient of variation calculated over
multiple consecutive strides. Rebula et al. reported that the root mean square of stride width and
stride length were within 4% of the gold standard method [10]. In a study by Allseits et al. [11] the
coecient of variation of gait cycle time diers by 7% from the gold standard. The variability of
single limb stance time shows a dierence of 26% relative to the gold standard in the same study.
Similarly, a further study found root-mean-squared coecients of variation percentage between
31% and 56% for various variability parameters when compared to motion capture gold standard
A possible reason for these errors in gait parameter calculation are random uctuations in
the angular velocity during swing phase [12]. However, no thorough analysis of possible reasons
for high errors in gait parameter estimation has been conducted, especially not for foot-mounted
We hypothesize that specic signal features can be found which predict under- or overestimation
of gait parameters. Therefore, this work aims to investigate the correlation between the
occurrence of specic signal features and errors in the stride length estimation regarding single
strides. Possibly this allows to identify the underlying physical reason for the occurrence of these
signal features. Further on, this can help to tune algorithms to cope with the issue and to design
sensors and sensor attachments that are better suited to prevent those errors.
In order to allow for a more in-depth analysis of one specic aspect in this eld, this thesis will
be limited to examining spatial gait parameters. The algorithm used for calculating these spatial
parameters from raw sensor data is taken from [7] and the time stamps for stride segmentation
are given as gold standard.



[1] Gillain, S. et al.: The value of instrumental gait analysis in elderly healthy, MCI or Alz-
heimer’s disease subjects and a comparison with other clinical tests used in single and
dual-task conditions. Annals of physical and rehabilitation medicine, 52 (6), 453-474, 2009.
[2] Block, J. A. and Shakoor, N.: The Biomechanics of Osteoarthritis: Implications for Therapy.
Current rheumatology reports, 11 (1), 15-22, 2009.
[3] Rispens, S. M. et al.: Identication of Fall Risk Predictors in Daily Life Measurements:
Gait Characteristics’ Reliability and Association With Self-reported Fall History. Neurorehabilitation
and neural repair, 29 (1), 54-61, 2015.
[4] Muro-De-La-Herran, A. et al.: Gait analysis methods: An overview of wearable and non-
wearable systems, highlighting clinical applications. Sensors, 14 (2), 3362-3394, 2014.
[5] Goetz, C. G. et al.: Movement Disorder Society-sponsored revision of the Unied Par-
kinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing
results. Movement Disorders, 23 (15), 2129-2170, 2008.
[6] Hannink, J. et al.: Quantifying postural instability in Parkinsonian gait from inertial sensor
data during standardised clinical gait tests. 2017 IEEE 14th International Conference on
Wearable and Implantable Body Sensor Networks (BSN), 129-132, 2017.
[7] Rampp, A. et al.: Inertial sensor-based stride parameter calculation from gait sequences in
geriatric patients. IEEE transactions on biomedical engineering, 62 (4), 1089-1097, 2014.
[8] Ferrari, A. et al.: A mobile Kalman-lter based solution for the real-time estimation of
spatio-temporal gait parameters. IEEE transactions on neural systems and rehabilitation
engineering, 24(7), 764-773, 2015.
[9] Hannink, J. et al.: Mobile Stride Length Estimation with Deep Convolutional Neural Net-
works. IEEE Journal of Biomedical and Health Informatics, 22 (2), 354-362, 2018.
[10] Rebula, R. J. et al.: Measurement of foot placement and its variability with inertial sensors.
Gait & Posture, 38 (4), 974-980, 2013.
[11] Allseits, J. et al.: The development and concurrent validity of a real-time algorithm for
temporal gait analysis using inertial measurement units. Journal of Biomechanics, 55, 27-
33, 2017.
[12] Rantalainen, T. et al.: Reliability and concurrent validity of spatiotemporal stride charac-
teristics measured with