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Tytuł pozycji:

A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.

Tytuł:
A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.
Autorzy:
Guimarães V; Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal.; Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.
Sousa I; Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal.
Correia MV; Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.; INESC TEC (Institute for Systems and Computer Engineering, Technology and Science), 4200-465 Porto, Portugal.
Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Nov 12; Vol. 21 (22). Date of Electronic Publication: 2021 Nov 12.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: Basel, Switzerland : MDPI, c2000-
MeSH Terms:
Deep Learning*
Gait Analysis*
Acceleration ; Aged ; Foot ; Gait ; Humans ; Walking ; Young Adult
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Grant Information:
AAL-2017-066 European Commission
Contributed Indexing:
Keywords: deep learning; foot trajectory; gait analysis; inertial sensors; long short-term memory (LSTM) networks
Entry Date(s):
Date Created: 20211127 Date Completed: 20211130 Latest Revision: 20211130
Update Code:
20240104
PubMed Central ID:
PMC8624119
DOI:
10.3390/s21227517
PMID:
34833590
Czasopismo naukowe
Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.
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