Gait Event Detection Algorithms for Free-Living Stair Ambulation

Bachelor's Thesis

Gait Event Detection Algorithms for Free-Living Stair Ambulation


Nils Roth (M.Sc.), Prof. Dr. B. Eskofier


01 / 2021 – 06 / 2021


Walking is an important part of a self-determined life. Especially for older people, gait is an indicator
of physical well-being [1]. Consequently, overground gait analysis is used in several studies
to assess overall health, fall risk, and disease progression, as well as other critical health issues
[2, 3]. However, considering gait parameters from stair climbing may help to paint a more accurate
picture [4], as stair climbing performance can differ significantly from level-ground walking
performance due to additional demands on the balance and control system, greater emphasis on
lower limb muscle strength, or even psychological factors including fear of falling [5, 6, 7].
Since there are obstacles using video-based motion-capture systems or computerized walkways for
gait analysis on stairs in real-world settings, it is preferable to use wearable inertial measurement
units (IMUs) instead. In addition to being comparably reliable [8], IMUs are less costly, small and
light weight, and therefore, offer the possibility to unobtrusively measure gait even in free-living
Previous studies concerning gait analysis on stairs have shown that conclusions about the health
of the subjects can be derived from stair ambulation parameters. The methods ranged from simple
measurement of the time needed to ascent or descent a given set of stairs [5] to more complicated
setups consisting of multiple IMUs attached to the lower back and ankle to evaluate fall risk [4]
or to the sternum to develop objective indices for clinical application to assess stair ascent in
neurologically-impaired patients [9]. Studies using IMUs attached to the shank have successfully
distinguished stair ascent from stair descent and level-walking [10, 11] and detected initial contact
and terminal contact gait events during stair walking [12]. These results suggest that further
investigation into relevant parameters during stair ambulation will contribute to an objective assessment
of patients’ gait. In particular, working with foot-worn IMUs instead of mounting them
to the anterior side of the shank – or other body parts even further away from the feet – provides
the opportunity to extract additional parameters such as foot angles from the data and thereby
achieve higher bio-mechanical resolution.
So far no algorithm specifically designed for gait event detection for stair ambulation with footworn
IMUs has been developed and due to the unique physical constraints implied by stair steps
and changing stride patterns, common stride event detection algorithms developed for level ground
walking approaches with foot-worn IMUs, as introduced by Rampp et. al. [13], may not produce
reliable results in stair negotiation. Nevertheless, a robust detection of standardized events like
initial contact as well as terminal contact is desirable to extract detailed stair stride parameters
including support times or swing duration. Such parameters can then help to gain a deeper insight
into a patient’s gait and motor impairments during stair negotiation or to identify different stair
ambulation strategies.
Currently there is no available dataset of stair climbing based on foot-worn IMUs together with
gait event references in free-living environments. Therefore, the aim of this thesis is first to conduct
a study to collect real world stair climbing data with video-based and pressure sensor based
stride event references and second to develop and evaluate respective event detection algorithms
based on the acquired dataset. The results and findings from this work could then be applied to
existing free-living patient data (FallRiskPD dataset), for example to better assess the risk of falls
in Parkinson’s Disease patients.



[1] Stephanie Studenski, Subashan Perera, Dennis Wallace, Julie M. Chandler, Pamela W. Duncan,
Earl Rooney, Michael Fox, and Jack M. Guralnik. Physical performance measures in the
clinical setting. Journal of the American Geriatrics Society, 51(3):314–322, 2003.
[2] Weijun Tao, Tao Liu, Rencheng Zheng, and Hutian Feng. Gait analysis using wearable
sensors. Sensors, 12(2):2255–2283, 2012.
[3] Anat Mirelman, Paolo Bonato, Richard Camicioli, Terry D Ellis, Nir Giladi, Jamie L Hamilton,
Chris J Hass, Jeffrey M Hausdorff, Elisa Pelosin, and Quincy J Almeida. Gait impairments
in parkinson’s disease. The Lancet Neurology, 18(7):697–708, 2019.
[4] Kejia Wang, Kim Delbaere, Matthew A. D. Brodie, Nigel H. Lovell, Lauren Kark, Stephen R.
Lord, and Stephen J. Redmond. Differences between gait on stairs and flat surfaces in relation
to fall risk and future falls. IEEE Journal of Biomedical and Health Informatics, 21(6):1479–
1486, 2017.
[5] Mooyeon Oh-Park, Cuiling Wang, and Joe Verghese. Stair negotiation time in communitydwelling
older adults: Normative values and association with functional decline. Archives of
Physical Medicine and Rehabilitation, 92(12):2006–2011, 2011.
[6] S Nadeau, B.J McFadyen, and F Malouin. Frontal and sagittal plane analyses of the stair
climbing task in healthy adults aged over 40 years: what are the challenges compared to level
walking? Clinical Biomechanics, 18(10):950–959, 2003.
[7] A. C. Tiedemann, C. Sherrington, and S. R. Lord. Physical and psychological factors associated
with stair negotiation performance in older people. The Journals of Gerontology Series
A: Biological Sciences and Medical Sciences, 62(11):1259–1265, 2007.
[8] Jeroen HM Bergmann, Ruth E Mayagoitia, and Ian CH Smith. A portable system for
collecting anatomical joint angles during stair ascent: a comparison with an optical tracking
device. Dynamic Medicine, 8(1), 2009.
[9] Ilaria Carpinella, Elisa Gervasoni, Denise Anastasi, Tiziana Lencioni, Davide Cattaneo, and
Maurizio Ferrarin. Instrumental assessment of stair ascent in people with multiple sclerosis,
stroke, and parkinson’s disease: A wearable-sensor-based approach. IEEE Transactions on
Neural Systems and Rehabilitation Engineering, 26(12):2324–2332, 2018.
[10] Brian Coley, other Bijan Najafi, Anisoara Paraschiv-Ionescu, and Kamiar Aminian. Stair
climbing detection during daily physical activity using a miniature gyroscope. Gait & Posture,
22(4):287–294, 2005.
[11] Rossana Muscillo, Silvia Conforto, Maurizio Schmid, Paolo Caselli, and Tommaso D’Alessio.
Classification of motor activities through derivative dynamic time warping applied on accelerometer
data. In 2007 29th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society. IEEE, 2007.
[12] Paola Formento, Ruben Acevedo, Salim Ghoussayni, and David Ewins. Gait event detection
during stair walking using a rate gyroscope. Sensors, 14(3):5470–5485, 2014.
[13] Alexander Rampp, Jens Barth, Samuel Schuelein, Karl-Gunter Gassmann, Jochen Klucken,
and Bjorn M. Eskofier. Inertial sensor-based stride parameter calculation from gait sequences
in geriatric patients. IEEE Transactions on Biomedical Engineering, 62(4):1089–1097, 2015