Affordable gait analysis using augmented reality markers

Abstract

A typical optical based gait analysis laboratory uses expensive stereophotogrammetric motion capture systems. The study aims to propose and validate an affordable gait analysis method using augmented reality (AR) markers with a single action camera. Image processing software calculates the position and orientation of the AR markers. Anatomical landmark calibration is applied on the subject to calibrate each of the anatomical points with respect to their corresponding AR markers. This way, anatomical points are tracked through AR markers using homogeneous coordinate transformations, and the further processing of gait analysis is identical with conventional solutions. The proposed system was validated on nine participants of varying age using a conventional motion capture system on simultaneously measured treadmill gait trials on 2, 3 and 4.5 km/h walking speeds. Coordinates of the virtual anatomical points were compared using the Bland-Altman analysis. Spatial-temporal gait parameters (step length, stride length, walking base, cadence, pelvis range of motion) and angular gait parameters (range of motion of knee, hip and pelvis angles) were compared between measurement systems by RMS error and Bland-Altman analysis. The proposed method shows some differences in the raw coordinates of virtually tracked anatomical landmarks and gait parameters compared to the reference system. RMS errors of spatial parameters were below 23 mm, while the angular range of motion RMS errors varies from 2.55° to 6.73°. Some of these differences (e.g. knee angle range of motion) is comparable to previously reported differences between commercial motion capture systems and gait variability. The proposed method can be a very cheap gait analysis solution, but precision is not guaranteed for every aspect of gait analysis using the currently exemplified implementation of the AR marker tracking approach.

Validación de los marcadores ArUco para el análisis de movimientos humanos

“…Estos resultados presentan errores mayores que los obtenidos por Parrilla et al (2013), si bien las diferencias pueden deberse al tipo de movimiento analizado en dicho trabajo, que estaba confinado en un espacio más pequeño, o a los algoritmos de cálculo que en nuestro estudio se realizan en tiempo real. Por el contrario, los errores obtenidos en nuestro trabajo son bastante menores que los descritos por Nagymate et al (2019) en un estudio sobre marcha. En nuestra opinión, los pobres resultados mostrados en dicho estudio se deben más a la presencia de artefactos por movimiento de tejidos blandos que a la precisión del equipo.…”

Feedback motion control of a spatial double pendulum manipulator relying on swept laser based pose estimation

“…[12] This sort of visual pose estimation is particularly typical in biomechanics, when the motion of humans or animals is captured. Single camera together with special pattern target objects such as augmented reality markers, [13] Parameters j R i Rotation matrix from frame i to j r position vector u, v, w unit vectors ϑ i angular position of the secondary cable winches, i = 1, 2, 3 ψ SU angular position of the Swinging Unit about the vertical axis F i thrust forces exerted by the fan actuators, i = 1...6 F SU,x , F SU,y resultant thrust forces in local x and y direction M SU,z resultant torque in local z direction exerted by the thrust forces M wi secondary cable winch torques, i = 1, 2, 3 m SU , m CC mass of the Swining Unit and the Cable Connector J mass moment of inertia matrix g gravitational acceleration vector q general coordinate vector M mass matrix C vector of velocity dependent inertial forces λ vector of Lagrange multipliers φ vector of geometric constraints H control input distribution matrix τ vector of control inputs n number of coordinates m number of constraints l number of independent control inputs α, β…”

Which Way Is ‘Yildiz Amfi̇’? Augmented Reality vs. Paper Map on Pedestrian Wayfinding

“…These expensive devices are difficult to use in outdoor environments as they restrict the free movement of the users. Second, outdoor environments are much more dynamic in which lighting and environmental conditions vary substantially compared to a carefully designed indoor environment, often including markers to facilitate image registration and tracking (Azuma et al, 1999;Nagymáté and Kiss, 2019). Nevertheless, with the decreasing costs of new generation smartphones possessing advanced software development kits such as ARCore or ARKit for Android and iOS devices respectively, it is now possible to abstract most of the aforementioned challenges from the developers (Nowacki and Woda, 2020).…”