Next Article in Journal
Compact Quantum Magnetometer System on an Agile Underwater Glider
Next Article in Special Issue
Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform
Previous Article in Journal
Extrinsic Camera Calibration with Line-Laser Projection
Previous Article in Special Issue
Personality-Based Affective Adaptation Methods for Intelligent Systems
 
 
Correction published on 29 June 2022, see Sensors 2022, 22(13), 4896.
Article

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

1
SensProtect GmbH, 85635 Höhenkirchen-Siegertsbrunn, Germany
2
Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany
3
Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Lübeck (P.E.R.L.), University of Lübeck, 23538 Lübeck, Germany
4
Geriatrics Research Group, Charité-Universitätsmedizin Berlin, 13347 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Maria de Fátima Domingues
Sensors 2021, 21(4), 1086; https://doi.org/10.3390/s21041086
Received: 31 December 2020 / Revised: 21 January 2021 / Accepted: 30 January 2021 / Published: 5 February 2021 / Corrected: 29 June 2022
(This article belongs to the Special Issue Multimodal Sensing for Understanding Behavior and Personality)
Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care. View Full-Text
Keywords: gait patterns; gait analysis; machine learning; feature learning; time series analysis; recurrent neural network; artificial neural network; sensor floor; SensFloor; long short-term memory; unilateral heel-rise test; dual-task gait patterns; gait analysis; machine learning; feature learning; time series analysis; recurrent neural network; artificial neural network; sensor floor; SensFloor; long short-term memory; unilateral heel-rise test; dual-task
Show Figures

Figure 1

MDPI and ACS Style

Hoffmann, R.; Brodowski, H.; Steinhage, A.; Grzegorzek, M. Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks. Sensors 2021, 21, 1086. https://doi.org/10.3390/s21041086

AMA Style

Hoffmann R, Brodowski H, Steinhage A, Grzegorzek M. Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks. Sensors. 2021; 21(4):1086. https://doi.org/10.3390/s21041086

Chicago/Turabian Style

Hoffmann, Raoul, Hanna Brodowski, Axel Steinhage, and Marcin Grzegorzek. 2021. "Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks" Sensors 21, no. 4: 1086. https://doi.org/10.3390/s21041086

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop