Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors

verfasst von

Daniel Laidig, Andreas J. Jocham, Bernhard Guggenberger, Klemens Adamer, Martin Fischer, Thomas Seel

Abstract

Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.

Externe Organisation(en)

Technische Universität Berlin
FH JOANNEUM University of Applied Sciences
Rehazentrum Kitzbühel
Ludwig Boltzmann Institute for Rehabilitation Research
Medizinische Hochschule Hannover (MHH)
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen-Nürnberg)

Typ

Artikel

Journal

Frontiers in digital health

Band

3

ISSN

2673-253X

Publikationsdatum

04.11.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Medizin (sonstige), Biomedizintechnik, Gesundheitsinformatik, Angewandte Informatik

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.3389/fdgth.2021.736418 (Zugang: Offen)