Robotics-driven gait analysis: Assessing Azure Kinect's performance in in-lab versus in-corridor environments

Guffanti Martínez, Diego ORCID: https://orcid.org/0000-0002-1244-291X, Brunete González, Alberto ORCID: https://orcid.org/0000-0001-9873-232X, Hernando Gutiérrez, Miguel ORCID: https://orcid.org/0000-0001-9997-0266, Álvarez Sánchez, David ORCID: https://orcid.org/0000-0003-2056-640X, Gambao Galán, Ernesto ORCID: https://orcid.org/0000-0003-1705-1800, Chamarro, William, Fernández Vázquez, Diego, Navarro López, Víctor, Carratalá Tejada, María and Miangolarra Page, Juan Carlos (2024). Robotics-driven gait analysis: Assessing Azure Kinect's performance in in-lab versus in-corridor environments. "Journal of Field Robotics", v. 41 (n. 4); pp. 1133-1145. ISSN 1556-4959. https://doi.org/10.1002/rob.22313.

Descripción

Título: Robotics-driven gait analysis: Assessing Azure Kinect's performance in in-lab versus in-corridor environments
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Field Robotics
Fecha: 13 Marzo 2024
ISSN: 1556-4959
Volumen: 41
Número: 4
Materias:
ODS:
Palabras Clave Informales: Mobile robot, gait analysis, depth sensors
Escuela: E.T.S.I. Diseño Industrial (UPM)
Departamento: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Gait analysis offers vital insights into human movement, aiding in the diagnosis, treatment, and rehabilitation of various conditions. Analyzing gait in corridors, rather than in lab, provides unique advantages for a more comprehensive understanding of human locomotion. However, limited dedicated technologies constrain gait data analysis in this context. In this study, a markerless gait analysis system using an Azure Kinect sensor mounted on a mobile robot is proposed and validated as a potential solution for gait analysis in corridors. Ten healthy participants (4 males and 6 females) underwent two tests. The first test (5 trials per participant) took place in the laboratory. Here, Azure Kinect performance was validated against a Vicon system, assessing eight gait signals and 22 gait parameters. The second test (2 trials per participant) was performed in the corridors over a 32-m walking distance to compare this gait pattern with the one developed within the laboratory. The intrasession Intraclass Correlation Coefficient (ICC) reliability for in-lab experiments was assessed by calculating the ICC between gait cycles captured in each session per participant. Notably, knee flexion/extension (ICC-0.95), hip flexion/extension (ICC-0.96), pelvis rotation (ICC-0.88), and interankle distance (ICC-0.98) demonstrated excellent reliability with high confidence. Similarly, hip adduction/abduction showed good reliability (ICC-0.79), while trunk rotation exhibited moderate reliability (ICC-0.72). In contrast, both trunk tilt (ICC-0.24) and pelvis tilt (ICC-0.41) consistently displayed lower reliability. This was observed for both the Vicon and the Azure systems, highlighting the intricate nature of capturing precise data for these specific signals in both systems. Validity outcomes indicated comparable error rates to literature standards (12.68 degrees $12.68<^>\circ $ knee flexion/extension, 5.54 degrees $5.54<^>\circ $ hip flexion/extension, and 3.45 degrees $3.45<^>\circ $ hip adduction/abduction), with 11 parameters having no significant differences from Vicon. Comparison of in-lab and in-corridor experiments show that individuals exhibit significantly longer stride time (1.10 s vs. 1.05 s), lower pelvis tilt (6.83 degrees $6.83<^>\circ $ vs. 9.39 degrees $9.39<^>\circ $), and lower minimum pelvis rotation (- 5.82 degrees $-5.82<^>\circ $ vs. - 14.61 degrees $-14.61<^>\circ $) when walking in the laboratory. This study demonstrates promising outcomes in outdoor gait analysis with a robot-mounted camera, revealing significant distinctions from controlled laboratory evaluations

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Gobierno de España
PID2020-118299RB-I00
Sin especificar
Sin especificar
SISTEMA ROBOTICO NO INVASIVO PARA EL ANALISIS BIOMECANICO DE LA MARCHA HUMANA

Más información

ID de Registro: 90395
Identificador DC: https://oa.upm.es/90395/
Identificador OAI: oai:oa.upm.es:90395
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/10207153
Identificador DOI: 10.1002/rob.22313
URL Oficial: https://onlinelibrary.wiley.com/doi/full/10.1002/r...
Depositado por: iMarina Portal Científico
Depositado el: 15 Sep 2025 10:55
Ultima Modificación: 15 Sep 2025 10:55