Marker-based motion tracking using Microsoft Kinect

Bilesan, Alireza; Owlia, Mohammadhasan; Behzadipour, Saeed; Ogawa, Shuichi; Tsujita, Teppei; Komizunai, Shunsuke; Konno, Atsushi

doi:10.1016/j.ifacol.2018.11.575

Cited by 21 publications

(10 citation statements)

References 12 publications

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“…Neither any research suggests an optimum number of cameras for reliable motion capture. Literature, in general, is about 3D motion capture [24,25]. We have, therefore, opted for a multiple-camera system, along with spherical markers to ensure better visibility and reliable data reconstruction by the system.…”

Section: Equipment and Calibrationmentioning

confidence: 99%

Cartesian Control of Sit-to-Stand Motion Using Head Position Feedback

Rafique

Najam-ul-Islam

Shafique

et al. 2020

Applied Bionics and Biomechanics

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Sit-to-stand (STS) motion is an indicator of an individual’s physical independence and well-being. Determination of various variables that contribute to the execution and control of STS motion is an active area of research. In this study, we evaluate the clinical hypothesis that besides numerous other factors, the central nervous system (CNS) controls STS motion by tracking a prelearned head position trajectory. Motivated by the evidence for a task-oriented encoding of motion by the CNS, we adopt a robotic approach for the synthesis of STS motion and propose this scheme as a solution to this hypothesis. We propose an analytical biomechanical human CNS modeling framework where the head position trajectory defines the high-level task control variable. The motion control is divided into low-level task generation and motor execution phases. We model CNS as STS controller and its Estimator subsystem plans joint trajectories to perform the low-level task. The motor execution is done through the Cartesian controller subsystem that generates torque commands to the joints. We do extensive motion and force capture experiments on human subjects to validate our analytical modeling scheme. We first scale our biomechanical model to match the anthropometry of the subjects. We do dynamic motion reconstruction through the control of simulated custom human CNS models to follow the captured head position trajectories in real time. We perform kinematic and kinetic analyses and comparison of experimental and simulated motions. For head position trajectories, root mean square (RMS) errors are 0.0118 m in horizontal and 0.0315 m in vertical directions. Errors in angle estimates are 0.55 rad, 0.93 rad, 0.59 rad, and 0.0442 rad for ankle, knee, hip, and head orientation, respectively. RMS error of ground reaction force (GRF) is 50.26 N, and the correlation between ground reaction torque and the support moment is 0.72. Low errors in our results validate (1) the reliability of motion/force capture methods and anthropometric technique for customization of human models and (2) high-level task control framework and human CNS modeling as a solution to the hypothesis. Accurate modeling and detailed understanding of human motion can have significant scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level task control schemes.

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Section: Equipment and Calibrationmentioning

confidence: 99%

Cartesian Control of Sit-to-Stand Motion Using Head Position Feedback

Rafique

Najam-ul-Islam

Shafique

et al. 2020

Applied Bionics and Biomechanics

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“…More recently, Bilesan (2019) [ 28 ], in a comparison study between Kinect v2 and Vicon data for gait parameters, demonstrates one more time that ankle angle from Kinect v2 has low accuracy and it is not suitable as a measurement variable.…”

Section: Related Workmentioning

confidence: 99%

Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System

Martín

Reyes-González

Sainz-Ruiz

et al. 2021

Sensors

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Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device’s design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle’s precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson’s coefficient, r = 0.89 ± 0.04, a Spearman’s coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson’s coefficient, r = 0.74, a Spearman’s coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

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“…Con el fin de transmitir movimientos tradicionales del ser humano a los robots, se emplean sistemas de captura de movimiento tridimensionales de alta precisión, alta velocidad y resolución generando que el costo de estos estudios sea muy elevado; es por esto por lo que en [6] se presenta un método de bajo costo que permite la medición del ángulo de la articulación del tobillo mediante un Kinect de Microsoft®.…”

Section: Seguimiento De Movimiento Basado En Marcadores Usando Microsoft Kinect®unclassified