Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 9; p. 1940
• Main Authors: Hannink, Julius, Ollenschläger, Malte, Kluge, Felix, Roth, Nils, Klucken, Jochen, Eskofier, Bjoern M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.08.2017; MDPI
• Subjects: benchmark dataset; clinical gait analysis; Datasets; double integration; Gait; human gait; Identification methods; Motion capture; orientation estimation; Parkinson's disease; Pipelines; Sensors; Trajectory analysis; wearable sensors; orientation estimation; human gait; wearable sensors; benchmark dataset; double integration; clinical gait analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17091940

Mobile gait analysis systems based on inertial sensing on the shoe are applied in a wide range of applications. Especially for medical applications, they can give new insights into motor impairment in, e.g., neurodegenerative disease and help objectify patient assessment. One key component in these systems is the reconstruction of the foot trajectories from inertial data. In literature, various methods for this task have been proposed. However, performance is evaluated on a variety of datasets due to the lack of large, generally accepted benchmark datasets. This hinders a fair comparison of methods. In this work, we implement three orientation estimation and three double integration schemes for use in a foot trajectory estimation pipeline. All methods are drawn from literature and evaluated against a marker-based motion capture reference. We provide a fair comparison on the same dataset consisting of 735 strides from 16 healthy subjects. As a result, the implemented methods are ranked and we identify the most suitable processing pipeline for foot trajectory estimation in the context of mobile gait analysis.