Liv Herzer

Liv Herzer

Bachelor's Thesis

Gait Event Detection Algorithms for Free-Living Stair Ambulation

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

Duration
01 / 2021 – 06 / 2021

Abstract
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 environments.

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.

References:
[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 community dwelling 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