Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks.

Journal article
(Original article)

Publication Details

Author(s): Hannink J, Kautz T, Pasluosta CF, Gaßmann KG, Klucken J, Eskofier B
Journal: IEEE Journal of Biomedical and Health Informatics
Publication year: 2017
Volume: 21
Journal issue: 1
Pages range: 85--93
ISSN: 2168-2194


Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of eight spatio-temporal stride parameters. To this end, two modelling approaches are compared: A combined network estimating all parameters of interest and an ensemble approach that spawns less complex networks for each parameter individually. The ensemble approach is outperforming the combined modelling in the current application. On a clinically relevant and publicly available benchmark dataset, we estimate stride length, width and medio-lateral change in foot angle up to -0.15 ±6.09 cm, -0.09 ±4.22 cm and 0.13 ±3.78 ° respectively. Stride, swing and stance time as well as heel and toe contact times are estimated up to ±0.07, ±0.05, ±0.07, ±0.07 and ±0.12 s respectively. This is comparable to and in parts outperforming or defining stateof- the-art. Our results further indicate that the proposed change in methodology could substitute assumption-driven doubleintegration methods and enable mobile assessment of spatiotemporal stride parameters in clinically critical situations as e.g. in the case of spastic gait impairments.

FAU Authors / FAU Editors

Eskofier, Björn Prof. Dr.
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Hannink, Julius
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Kautz, Thomas
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Klucken, Jochen Prof. Dr.
Molekular-Neurologische Abteilung in der Neurologischen Klinik
Pasluosta, Cristian Federico
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)

Additional Organisation
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)

How to cite

Hannink, J., Kautz, T., Pasluosta, C.F., Gaßmann, K.-G., Klucken, J., & Eskofier, B. (2017). Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 21(1), 85--93.

Hannink, Julius, et al. "Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks." IEEE Journal of Biomedical and Health Informatics 21.1 (2017): 85--93.


Last updated on 2018-10-08 at 23:08