Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function

Otte, Karen; Kayser, Bastian; Mansow-Model, Sebastian; Verrel, Julius; Paul, Friedemann; Brandt, Alexander U.; Schmitz‐Hübsch, Tanja

doi:10.1371/journal.pone.0166532

Cited by 208 publications

(197 citation statements)

References 20 publications

(37 reference statements)

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“…Regarding accuracy for motion tracking, the Kinect is adequate for most scientific purposes, including medical studies on posture and rehabilitation (Clark et al, 2012;Clark et al, 2015;Zhao et al, 2014). In particular, Otte et al (2016) have shown that the Kinect V2 can derive clinical motion parameters with comparable performance to that obtained by a gold standard motion capture system.…”

Section: Virtual Reality Technologiesmentioning

confidence: 92%

A method for creating interactive, user-resembling avatars

Silva

Moioli²

2017

PeerJ Computer Science

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Virtual reality (VR) applications have disseminated throughout several fields, with a special quest for immersion. The avatar is one of the key constituents of immersive applications, and avatar resemblance can provoke diverse emotional responses from the user. Yet a lot a virtual reality systems struggle to implement real life-like avatars. In this work, we propose a novel method for creating interactive, user-resembling avatars using available commercial hardware and software. Avatar visualization is possible with a point-cloud or a contiguous polygon surface, and avatar interactions with the virtual scenario happens through a body joint-approximation for contact. In addition, the implementation could be easily extended to other systems and its modular architecture admits improvement both on visualization and physical interactions. The code is under Apache License 2.0 and is freely available as Supplemental Information 1 in this article.

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Section: Virtual Reality Technologiesmentioning

confidence: 92%

A method for creating interactive, user-resembling avatars

Silva

Moioli²

2017

PeerJ Computer Science

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“…Comparable validation results were reported by Capecci et al [34], where for a set of three rehabilitation exercises an average positional RMSE of 3.3 cm (1.3 inch) and an average orientational RMSE of 12.7 degrees were recorded. In [88], Otte et al…”

Section: Vision/depth Camerasmentioning

confidence: 99%

A review of computational approaches for evaluation of rehabilitation exercises

Liao

Vakanski

Xian

et al. 2020

Computers in Biology and Medicine

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Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.

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“…These limitations could be reduced with the Kinect V2 since it presents higher‐specification cameras, an improved field of view, image resolution, body tracking algorithms, noise reduction filters and the ability to track more joints and more bodies than its predecessor. A previous study claims that Kinect V2 could be used for clinical measurement of motor function, but there is still the need for outlier detection before the calculation of clinical parameters . Therefore, even though Kinect V2 improves the V1 specifications, it is necessary to apply a model correction to the measurements.…”

Section: Introductionmentioning

confidence: 99%

“…A previous study claims that Kinect V2 could be used for clinical measurement of motor function, but there is still the need for outlier detection before the calculation of clinical parameters. 17 Therefore, even though Kinect V2 improves the V1 specifications, it is necessary to apply a model correction to the measurements.…”

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confidence: 99%

Assessment of Kinect V2 for elbow range of motion estimation in people with haemophilia using an angle correction model

et al. 2019

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Introduction The joint range of motion (ROM) is an important clinical parameter used to assess the loss of functionality resulting from joint bleedings in people with haemophilia. These episodes require a close follow‐up and, to decrease patients' hospital dependence, telemedicine tools are needed. Therefore, this study is aimed to analyse the validity of the Microsoft Kinect V2 sensor with corrected angle measurement to be used in the monitoring of elbow ROM in people with haemophilia. Methods A convenience sample of 10 healthy controls (CG) and 10 patients with haemophilia with elbow arthropathy (HG) participated in this study. Full ROM of elbow joints was measured in the frontal view with a 10‐degree sweep using: (a) a clinical goniometer; (b) the Kinect V2; (c) the Kinect V2 with angle correction; and (d) using a photograph. Bland‐Altman graphs (mean and 95% Limits of Agreement [LOA]) and Wilcoxon test were used to determine differences between measurements and groups. Results The angle‐corrected Kinect V2 measurement removed the skew in the original data, reducing the average errors from 7.9° (LoA = −10.3°; 26.0°; CG) and 9.5° (LoA = −7.9°; 26.9°; HG) to −0.1° (LoA = −8.1°; 7.9°; CG) and −0.7° (LoA = −10.7°; 9.3°; HG). Conclusions These error levels allow the use of Kinect V2 in the clinical practice. Kinect V2 with angle correction can complement the classical goniometry allowing an efficient and touchless measurement of ROM.

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Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function

Cited by 208 publications

References 20 publications

A method for creating interactive, user-resembling avatars

A method for creating interactive, user-resembling avatars

A review of computational approaches for evaluation of rehabilitation exercises

Assessment of Kinect V2 for elbow range of motion estimation in people with haemophilia using an angle correction model

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