The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion

Beshara, Peter; Chen, Judy F.; Read, Andrew C.; Lagadec, Pierre; Wang, Tian; Walsh, William R.

doi:10.3390/s20247238

Cited by 25 publications

(17 citation statements)

References 66 publications

(80 reference statements)

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“…Ryselis reported an overall increase in accuracy of 15.7%. A combination of a Kinect and several Inertial Measurement Units (IMUs) could also be used to reduce the upper limb position error by up to 20%, according to Jatesiktat et al [ 50 , 51 , 52 ]. Finally, a device-independent approach is to incorporate body constraints (such as human skeleton length conservation and temporal constraints) to enhance the continuity of the estimated skeleton [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Faity¹,

Mottet²,

Froger

2022

Sensors

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Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the tracking limitations of the Kinect.

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Section: Discussionmentioning

confidence: 99%

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Faity¹,

Mottet²,

Froger

2022

Sensors

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“…Several studies have shown that upper limb angle measurements based on the Kinect sensor and other reference instruments, such as goniometers or wearable sensors, have a high degree of validity [ 16 , 22 , 40 ]. The authors Beshara et al [ 22 ] also compared measurements with a goniometer and claimed that the use of wearable inertial sensors in conjunction with Kinect v2 is a reliable and valid way to assess active shoulder flexion and abduction, with minimal measurement errors (2–4°), which represents high reliability. The results of this study show a significant validity of the measurements with the Kinect v2 sensor with an error of ±1° compared to the specialist’s measurements.…”

Section: Discussionmentioning

confidence: 99%

“…Several researchers have evaluated the reliability of the Kinect v2 system in the analysis of upper limb functionality and found significant measurement accuracy [ 16 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. The authors Cai et al [ 21 ] found that the mean square error of the shoulder joint angle (adduction and abduction) was less than six degrees between the Kinect v2 (Microsoft, Redmond, WA, USA) and Vicon (Oxford Metrics Group, Vicon Motion Systems Ltd., Oxford, UK).…”

Section: Introductionmentioning

confidence: 99%

Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children

Francisco-Martínez¹,

Padilla-Medina²,

Prado-Olivarez³

et al. 2022

Sensors

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The interruption of rehabilitation activities caused by the COVID-19 lockdown has significant health negative consequences for the population with physical disabilities. Thus, measuring the range of motion (ROM) using remotely taken photographs, which are then sent to specialists for formal assessment, has been recommended. Currently, low-cost Kinect motion capture sensors with a natural user interface are the most feasible implementations for upper limb motion analysis. An active range of motion (AROM) measuring system based on a Kinect v2 sensor for upper limb motion analysis using Fugl-Meyer Assessment (FMA) scoring is described in this paper. Two test groups of children, each having eighteen participants, were analyzed in the experimental stage, where upper limbs’ AROM and motor performance were assessed using FMA. Participants in the control group (mean age of 7.83 ± 2.54 years) had no cognitive impairment or upper limb musculoskeletal problems. The study test group comprised children aged 8.28 ± 2.32 years with spastic hemiparesis. A total of 30 samples of elbow flexion and 30 samples of shoulder abduction of both limbs for each participant were analyzed using the Kinect v2 sensor at 30 Hz. In both upper limbs, no significant differences (p < 0.05) in the measured angles and FMA assessments were observed between those obtained using the described Kinect v2-based system and those obtained directly using a universal goniometer. The measurement error achieved by the proposed system was less than ±1° compared to the specialist’s measurements. According to the obtained results, the developed measuring system is a good alternative and an effective tool for FMA assessment of AROM and motor performance of upper limbs, while avoiding direct contact in both healthy children and children with spastic hemiparesis.

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“…Ryselis reported an overall increase in accuracy of 15.7%. A combination of a Kinect and several Inertial Measurement Units (IMUs) could also be used to reduce the upper limb position error up to 20% according to Jatesiktat et al [48][49][50]. Finally, a device-independent approach is to incorporate body constraints (such as human skeleton length conservation and temporal constraints) to enhance the continuity of the estimated skeleton [51].…”

Section: Smoothing Out Kinect Errorsmentioning

confidence: 99%

Validity and reliability of Kinect v2 for quantifying upper body kinematics during seated reaching

Faity¹,

Mottet²,

Froger³

2022

Preprint

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Background: Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the assessment of 17 kinematic variables commonly used in the analysis of upper limb reaching in stroke. Methods: 26 healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce a behaviour similar to that of a person with a stroke. 3D upper limb and trunk motion were simultaneously recorded with the Kinect v2 (Microsoft, USA) and with the VICON (OxfordMetrics, UK), the latter being the reference system. For each kinematic outcome, the validity of the Kinect was assessed with ICC, linear regression and Bland & Altman plots. Results: The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability, indicating that these variables should be interpreted with caution. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess Cartesian and angular kinematics over time, rendering variables such as the number of velocity peaks and the peak hand velocity unusable. Conclusions: Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the limitations of the Kinect for the instantaneous tracking of the hand and elbow.

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The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion

Cited by 25 publications

References 66 publications

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children

Validity and reliability of Kinect v2 for quantifying upper body kinematics during seated reaching

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