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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Guimaraes, V (Autor) Outra; Sousa, I (Autor) Outra; Correia, M. V. (Autor) FEUP

Revista

Título: Sensors

Vol. 21 Nº 2

Página Final: 7517

ISSN: 1424-3210

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00V-NPW

DOI: 10.3390/s21227517

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 22