Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry

Bonnechère, Bruno; Jansen, Bart; Salvia, Patrick; Bouzahouene, Hakim; Omelina, Luboš; Moiseev, Fédor; Sholukha, Victor; Cornelis, Jan; Rooze, Marcel; Jan, Serge Van Sint

doi:10.1016/j.gaitpost.2013.09.018

Cited by 232 publications

(173 citation statements)

References 11 publications

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“…Specifically, each study was rated on 10 specific criteria which were derived from items 1, 3,6,8,11,14,18,19,20 and 22 of the original checklist. In cases where the authors completing paper rating (MOR or CD)…”

Section: Assessment Of Study Qualitymentioning

confidence: 99%

“…Each method is associated with a number of limitations. For instance, 3-D motion capture systems are expensive (> €100,000) and the application of skin-mounted markers may hinder normal movement [5,6]. Furthermore, data processing can be time intensive and specific expertise is often required to interpret the processed data and to make recommendations on the observed results.…”

Section: Introductionmentioning

confidence: 99%

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Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

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Section: Assessment Of Study Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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“…These points are the positions of the center of the major joints of the human body and, in some cases (head, spine, hands and feet), estimations of the center of the major limbs. Pelvic girdle center and shoulder girdle center are called Hip Center and Shoulder Center in the SDK (Bonnechère et al, 2013a) (see Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…1). The joint centers are obtained from a randomized decision forest algorithm and the data seem to be accurate enough to be used for postural assessment (Bonnechère et al, 2013a;Clark et al, 2012;Dutta, 2012;Martin et al, 2012;Raptis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Using Kinect™ sensor in observational methods for assessing postures at work

2014

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a b s t r a c tThis paper examines the potential use of KinectÔ range sensor in observational methods for assessing postural loads. Range sensors can detect the position of the joints at high sampling rates without attaching sensors or markers directly to the subject under study. First, a computerized OWAS ergonomic assessment system was implemented to permit the data acquisition from KinectÔ and data processing in order to identify the risk level of each recorded postures. Output data were compared with the results provided by human observers, and were used to determine the influence of the sensor view angle relative to the worker. The tests show high inter-method agreement in the classification of risk categories (Proportion agreement index ¼ 0.89 k ¼ 0.83) when the tracked subject is facing the sensor. The camera's point of view relative to the position of the tracked subject significantly affects the correct classification of the postures. Although the results are promising, some aspects involved in the use of low-cost range sensors should be further studied for their use in real environments.

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“…Kinect is able to recognize and extract twenty joint data simultaneously without markers attached to body, and provides an interface with which to develop the program. Kinect-based systems have been previously used as rehabilitation tools to assist people with cerebral palsy, muscleatrophy, and balance problems [11][12][13]. Evidence shows that these systems can accurately measure the total centre of mass and lateral trunk lean angles during postural control tests, which is a typical movement of people with cerebral palsy and upper-extremity ailments [8,[14][15][16].…”

mentioning

confidence: 99%

Evaluation of Kinect as an Analysis Tool for Kinematic Variables of Shoulder and Spine Motions

Duan¹,

Xiao²,

Wang³

2015

JMRD

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Abstract-Specific kinematic actions of the spine and shoulder are useful for low back pain (LBP) assessment and treatment. However, the widespread application of motion capture systems in clinical settings is limited by the tedious process and expensive equipment. As a cost-effective and portable device, Kinect has the potential to be used in clinical analysis and rehabilitation for patients with LBP. Using Kinect and a motion capture system, 10healthysubjectsperformed left/right bending and extension tasks five times successively with and without simulated motion limitations. The mean range of motion (ROM)and maximum angular velocity (MAV) were then calculated. The results revealed that the Pearson's correlation coefficient was more than 0.7 with ROM. The root-meansquare error (RMSE) of the ROM difference, with and without motion limitation, was1.2  -3  (for MAV: 4.7  /s-14.6  /s). In conclusion, Kinect can be used as an analysis tool for kinematic variability of shoulder and trunk motion, which could help obtain low back information regarding movement patterns and strategies under various movement tasks.

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Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry

Cited by 232 publications

References 11 publications

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Using Kinect™ sensor in observational methods for assessing postures at work

Evaluation of Kinect as an Analysis Tool for Kinematic Variables of Shoulder and Spine Motions

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