Quantitative evaluation of parameters affecting the accuracy of Microsoft Kinect in GAIT analysis

Jamali, Zahra; Behzadipour, Saeed

doi:10.1109/icbme.2016.7890977

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…Similar to this study, Preis et al 13) and Zahra et al 14) shows the accuracy of Kinect alone 13) and Kinect compared with commercial motion capture camera 14) . Preis et al used 13 biometric features such as the height, length of limbs, and step length which then are computed from the skeleton frames generated by Kinect.…”

Section: Study Of Literaturesupporting

confidence: 85%

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Gait Analysis Parameter Study Using Xbox Kinect Aimed at Medical Rehabilitation Tool

Basari¹,

Prasetyo²

2022

Evergreen

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There are various methods of rehabilitation to restore disabled gait, one of which is gait analysis. However, some gait analysis systems have shown drawbacks for clinical use, such as system complexity and the high cost of gait equipment. A Kinect sensor could be used as an alternative for gait analysis and is studied in this research. The accuracy of a Kinect in calculation of kinematic gait parameters was computed during normal gait. The gait analysis was processed with Matlab and data was acquired by recording multiple subjects' gait with different Kinect position. Subjects' the right knee was calculated and the results shown the best position of Kinect to capture gait is 45º towards motion path with errors of detecting gait parameters about 7%. Hence, Kinect sensor is capable of doing gait analysis for further pre-clinical applications.

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Section: Study Of Literaturesupporting

confidence: 85%

“…Kinect uses speckle pattern of infrared laser light which then combined with two classic computer techniques: depth from focus and depth from stereo. Subsequently, Kinect will implement machine learning to infer body position 14) .…”

Section: Study Of Literaturementioning

confidence: 99%

Gait Analysis Parameter Study Using Xbox Kinect Aimed at Medical Rehabilitation Tool

Basari¹,

Prasetyo²

2022

Evergreen

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“…Kharazi (2015) [ 22 ] and Jamali and Behzadipour (2016) [ 23 ] employed a Kinect v1 for gait parameters and demonstrated good accuracy for some joints like knee and hip positions and angles, but they obtained poor results for ankle parameters.…”

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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“…Kinect is inaccurate in recognizing the center of joints while measuring short links of the body, such as the foot [ 22 ], and [ 23 ] assessed the accuracy of the Kinect in lower-extremity gait analysis. In these studies, the accuracy of the Kinect was assessed using a commercial MMS as the gold standard.…”

Section: Related Workmentioning

confidence: 99%

“…In [21], the accuracy of the Kinect was evaluated in terms of detecting the human body center of mass using the length of body links in the Kinect skeleton model. Kinect is inaccurate in recognizing the center of joints while measuring short links of the body, such as the foot [22], and [23] assessed the accuracy of the Kinect in lower-extremity gait analysis. In these studies, the accuracy of the Kinect was assessed using a commercial MMS as the gold standard.…”

Section: Related Workmentioning

confidence: 99%

Fusion of Multiple Lidars and Inertial Sensors for the Real-Time Pose Tracking of Human Motion

Patil

Balasubramanyam

Ryu

et al. 2020

Sensors

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Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user’s height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user’s height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3–5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.

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Quantitative evaluation of parameters affecting the accuracy of Microsoft Kinect in GAIT analysis

Cited by 4 publications

References 16 publications

Gait Analysis Parameter Study Using Xbox Kinect Aimed at Medical Rehabilitation Tool

Gait Analysis Parameter Study Using Xbox Kinect Aimed at Medical Rehabilitation Tool

Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System

Fusion of Multiple Lidars and Inertial Sensors for the Real-Time Pose Tracking of Human Motion

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