Performance Analysis of Body Tracking with the Microsoft Azure Kinect

Romeo, Laura; Marani, Roberto; Malosio, Matteo; Perri, Anna Gina; D’Orazio, Tiziana

doi:10.1109/med51440.2021.9480177

Cited by 25 publications

(17 citation statements)

References 15 publications

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“…Many recent works have already investigated this new sensor through validation protocols against gold reference systems [ 68 , 69 , 70 ], agreeing on its higher accuracy and performance compared to predecessors and other commercial optical sensors. Other studies, instead, focused on its new body-tracking algorithm to verify its accuracy, robustness, and reliability in capturing 3D movements and poses [ 71 , 72 , 73 ]. The Azure Kinect has also recently been used in some preliminary clinical studies and for rehabilitation purposes [ 74 , 75 , 76 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect

Amprimo

Masi

Priano

et al. 2022

Sensors

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Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.

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Section: Introductionmentioning

confidence: 99%

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect

Amprimo

Masi

Priano

et al. 2022

Sensors

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“…In [16], an Azure Kinect is used to assess the possibility of anterior cruciate ligament rupture and osteoarthritis. In addition, the possibility of exploiting the Azure Kinect Software Development Kit (SDK), even for the extraction of skeletal joints with the Azure Kinect Body Tracking SDK, represents a further step beyond the previous Kinect versions [17]. The accuracy of skeletal joint extraction has been compared with a gold standard motion capture system and a high accuracy inertial measurement unit.…”

Section: Introductionmentioning

confidence: 99%

Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

Romeo

Marani

Perri

et al. 2022

Sensors

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Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory for enabling further applications that require high precision. This paper analyzes the available two-camera calibration approaches to propose a guideline for calibrating multiple Azure Kinect RGB-D sensors to achieve the best alignment of point clouds in both color and infrared resolutions, and skeletal joints returned by the Microsoft Azure Body Tracking library. Different calibration methodologies using 2D and 3D approaches, all exploiting the functionalities within the Azure Kinect devices, are presented. Experiments demonstrate that the best results are returned by applying 3D calibration procedures, which give an average distance between all couples of corresponding points of point clouds in color or an infrared resolution of 21.426 mm and 9.872 mm for a static experiment and of 20.868 mm and 7.429 mm while framing a dynamic scene. At the same time, the best results in body joint alignment are achieved by three-dimensional procedures on images captured by the infrared sensors, resulting in an average error of 35.410 mm.

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“…To do this, we used a 3D inverse kinematic and dynamic model based on a previously implemented biomechanical model [13], which allows the computation of kinematic and dynamic parameters. This model is integrated with data registered using Microsoft Azure Kinect cameras, which estimate human joint positions [14]. The presence of cooperation with a robotic architecture and, in general, with an assembly or working scenario may alter the normal execution of human movement, due to the speed of movements, displaced loads, and other factors.…”

Section: Introductionmentioning

confidence: 99%

Optimal Biomechanical Performance in Upper-Limb Gestures Depends on Velocity and Carried Load

Brambilla

Malosio

Reni

et al. 2022

Biology

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In the last few years, there has been increased interest in the preservation of physical and mental health of workers that cooperate with robots in industrial contexts, such as in the framework of the European H2020 Mindbot Project. Since biomechanical analysis contributes to the characterization of the subject interacting with a robotic setup and platform, we tested different speed and loading conditions in a simulated environment to determine upper-limb optimal performance. The simulations were performed starting from laboratory data of people executing upper-limb frontal reaching movements, by scaling the motion law and imposing various carried loads at the hand. The simulated velocity ranged from 20% to 200% of the original natural speed, with step increments of 10%, while the hand loads were 0, 0.5, 1, and 2 kg, simulating carried objects. A 3D inverse kinematic and dynamic model was used to compute upper-limb kinematics and dynamics, including shoulder flexion, shoulder abduction, and elbow flexion. An optimal range of velocities was found in which the expended energy was lower. Interestingly, the optimal speed corresponding to lower exerted torque and energy decreased when the load applied increased. Lastly, we introduced a preliminary movement inefficiency index to evaluate the deviation of the power and expended energy for the shoulder flexion degree of freedom when not coinciding with the minimum energy condition. These results can be useful in human–robot collaboration to design minimum-fatigue collaborative tasks, tune setup parameters and robot behavior, and support physical and mental health for workers.

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Performance Analysis of Body Tracking with the Microsoft Azure Kinect

Cited by 25 publications

References 15 publications

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect

Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

Optimal Biomechanical Performance in Upper-Limb Gestures Depends on Velocity and Carried Load

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