Comparison of Azure Kinect overground gait spatiotemporal parameters to marker based optical motion capture

Guess, Trent M.; Bliss, Becky; Hall, John F.; Kiselica, Andrew M.

doi:10.1016/j.gaitpost.2022.05.021

Cited by 28 publications

(17 citation statements)

References 33 publications

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“…HFUT20210110001). Kinect is used as the motion capture device to record the position coordinates of the subject's left hip, left knee and left ankle in real time [23]. Noted that walking is actually a 3-dimensional motion in the sagittal plane, coronal plane and horizontal plane, but compared with the sagittal plane, the range of motion very small in other two planes which could be ignored [24].…”

Section: Acquisition and Normalization Of Gait Trajectory Datamentioning

confidence: 99%

Data-Driven Design of a Six-Bar Lower-Limb Rehabilitation Mechanism Based on Gait Trajectory Prediction

Song

Zhao

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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For patients who need lower-limb kinetism rehabilitation training, this paper proposes an effective datadriven approach seeking the design of 1-degree-of-freedom (DOF) six-bar rehab mechanism through gait prediction by body parameters. First, gait trajectories from 79 healthy volunteers are collected along with their body parameters. Then, the normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithmoptimized support vector machine method is adopted to establish a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, the suitable gait trajectory can be predicted for the specific patient. A GA-BFGS algorithm is developed for 1-DOF six-bar mechanism synthesis and a GUI design software is presented that shows how the data-driven design process is realized. The novelty of this paper is using clustering and prediction technique to accomplish the patient-mechanism matching, so that simple, low-priced 1-DOF mechanisms could be adopted for large number of various patients without expensive customized design for each individual. In the end, a gait rehab device design example is provided, and a prototype device driven by a constant speed motor is presented, which illustrates the feasibility of the proposed method.

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Section: Acquisition and Normalization Of Gait Trajectory Datamentioning

confidence: 99%

Data-Driven Design of a Six-Bar Lower-Limb Rehabilitation Mechanism Based on Gait Trajectory Prediction

Song

Zhao

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…A frequently used depth camera in research is the Microsoft Azure Kinect DK RGB-D-camera with its Azure Kinect Body Tracking SDK. Among other things, it is used for movement analysis [ 1 , 2 ] as well as posture analysis [ 3 , 4 ]. The Azure Kinect has a 1-megapixel time-of-flight (ToF) camera installed for depth measurement, which has a typical systematic error of <11 mm + 0.1% of the distance to the object and a random error with a standard deviation of ≤17 mm according to its manufacturer [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

How the Processing Mode Influences Azure Kinect Body Tracking Results

Büker¹,

Quinten²,

Hackbarth³

et al. 2023

Sensors

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The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.

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“…Pilot studies on gait analysis [81,82] highlight higher accuracy of the device in the estimation of spatial gait parameters and kinematics compared to the previous models. Recent studies found that Azure Kinect had good agreement with a traditional motion capture system setup, indicating that the sensor could provide clinically relevant measurement of spatiotemporal parameters during gait [83], postural control [84], and sit-to-stand movement strategies, allowing for improved precision in clinical decision-making across multiple clinical populations [85]. The results showed high levels of agreement in evaluating spatiotemporal and kinematic variables during walking, sit-to-stand, and functional balance tasks, indicating that this technology is capable of accurate, and clinically relevant, assessment of motion data while performing these tasks.…”

Section: Introductionmentioning

confidence: 99%

Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

Cerfoglio

Ferraris

Vismara

et al. 2022

Sensors

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The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.

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Comparison of Azure Kinect overground gait spatiotemporal parameters to marker based optical motion capture

Cited by 28 publications

References 33 publications

Data-Driven Design of a Six-Bar Lower-Limb Rehabilitation Mechanism Based on Gait Trajectory Prediction

Data-Driven Design of a Six-Bar Lower-Limb Rehabilitation Mechanism Based on Gait Trajectory Prediction

How the Processing Mode Influences Azure Kinect Body Tracking Results

Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

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