Validity of the Microsoft Kinect for assessment of postural control

Clark, Ross; Pua, Yong-Hao; Fortin, Karine; Ritchie, Callan; Webster, Kate E.; Denehy, Linda; Bryant, Adam

doi:10.1016/j.gaitpost.2012.03.033

Cited by 590 publications

(373 citation statements)

References 22 publications

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“…Automatic load detection would be desirable if the load handled by the worker greatly varies over the course of his work. Future research may consider attaching markers to the load, and using the depth sensor and video camera data to track these additions and achieve automatic load detection (Clark et al, 2012;Ong et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…Its use has advantages over camera systems that define joint centers and anatomical landmarks based on markers placed on the skin (setup time required, uncomfortable exposure of areas of the body such as the thorax, hips and thighs.) (Clark et al, 2012).…”

Section: Discussionmentioning

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%

“…The use of low-cost range sensors like Microsoft's KinectÔ could be useful in the collection of data at very high frequencies, but it is necessary to check the accuracy and reliability of the data collected. Previous studies have analyzed the ability of KinectÔ for use as a 3D motion capture system in working environments, concluding that the sensor accuracy is only slightly lower than that of more expensive devices, but reliable enough for use in ergonomic assessment of work stations (Clark et al, 2012;Dutta, 2012).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2014

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“…We used linear interpolation to obtain a constant sampling interval (cf. [27]), using the function interp1 in Matlab. During the cognitive dual task participants listened to a list of pre-recorded words that were presented at a frequency of 0.5 Hz, resulting in a total number of fifteen different words per trial.…”

Section: Data Collectionmentioning

confidence: 99%

Attentional and sensory contributions to postural sway in children with autism spectrum disorder

et al. 2015

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Research on risk assessment model of subjective and objective integration in manual handling work

Ren

Zhou

Chen

2023

Hum Ftrs & Erg Mfg Svc

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The main hazards in the process of manual handling work are triggered by human factors and ergonomics. This study is intended to implement a valid approach to quantitatively evaluate the risk level during manual handling work. A risk assessment model for manual handling workers was proposed based on subjective and objective correlation. A simulation experiment of manual handling process was developed and an ergonomics evaluation method was carried out. The 33‐point human joint model of BlazePose neural network and the Rapid Upper Limb Analysis (RULA) method were utilized to determine the risk posture and risk index of the manual handling workers. This study brought together hemodynamic parameters and the score of Borg Rating of Perceived Exertion (RPE) scale and Physical Resources Scale (PRS) to obtain the final comprehensive index of work risk. The results showed that the risk indexes from the three stages of the experiment obtained by RULA method were 3, 4, and 7. And the scores of comprehensive indexes obtained by the risk assessment model were 1.841, 1.900, and 1.987, suggesting that the evaluation model based on subjective and objective correlation had the same ability to determine the risk levels of different handling tasks. Therefore, the risk assessment model proposed in this study verified the effectiveness of the comprehensive evaluation index integrating hemodynamic parameters and subjective evaluation scores.

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Validity of the Microsoft Kinect for assessment of postural control

Cited by 590 publications

References 22 publications

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

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

Attentional and sensory contributions to postural sway in children with autism spectrum disorder

Research on risk assessment model of subjective and objective integration in manual handling work

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