Next Article in Journal
Measuring Vehicle Profile Size: Lidar-Based System and K-Frame-Based Methodology
Next Article in Special Issue
Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda
Previous Article in Journal
Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour
Previous Article in Special Issue
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning
Article

Contactless Gait Assessment in Home-like Environments

Gerontechnology & Rehabilitation Group, University of Bern, 3008 Bern, Switzerland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Wenjin Wang, Steffen Leonhardt, Daqing Zhang and Bert den Brinker
Sensors 2021, 21(18), 6205; https://doi.org/10.3390/s21186205
Received: 31 July 2021 / Revised: 25 August 2021 / Accepted: 13 September 2021 / Published: 16 September 2021
(This article belongs to the Special Issue Contactless Sensors for Healthcare)
Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (r>0.95) with the gold standards across all assessed gait parameters except for cycle time (r=0.91). Similarly, the coefficient of determination was high (R2>0.9) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (r0.98,R20.95). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments. View Full-Text
Keywords: gait analysis; gait abnormalities; contactless sensors; LiDAR; home-based measurements; health monitoring gait analysis; gait abnormalities; contactless sensors; LiDAR; home-based measurements; health monitoring
Show Figures

Figure 1

MDPI and ACS Style

Botros, A.; Gyger, N.; Schütz, N.; Single, M.; Nef, T.; Gerber, S.M. Contactless Gait Assessment in Home-like Environments. Sensors 2021, 21, 6205. https://doi.org/10.3390/s21186205

AMA Style

Botros A, Gyger N, Schütz N, Single M, Nef T, Gerber SM. Contactless Gait Assessment in Home-like Environments. Sensors. 2021; 21(18):6205. https://doi.org/10.3390/s21186205

Chicago/Turabian Style

Botros, Angela, Nathan Gyger, Narayan Schütz, Michael Single, Tobias Nef, and Stephan M. Gerber. 2021. "Contactless Gait Assessment in Home-like Environments" Sensors 21, no. 18: 6205. https://doi.org/10.3390/s21186205

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