Using Kinect body joint detection system to predict energy expenditures during physical activities

Woldegiorgis, Bereket Haile; Lin, Chiuhsiang Joe; Sananta, Riotaro

doi:10.1016/j.apergo.2021.103540

Cited by 2 publications

(1 citation statement)

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“…As a result, a final skeleton can be used to recognize body postures and movements, ultimately used for motion analysis 5 and on physical therapies and rehabilitation monitoring process. [9][10][11] The average noise levels of the obtained depth data are reported to be of similar magnitude with soft-tissue artifacts in motion capture systems such as Vicon, 12 indicating that the Kinect is a viable tool for motion analysis. However, occlusion of body parts, 7 poor IRreflectivity in clothing and relative subject movements outside the field of view 4 are reported to decrease the Kinect's accuracy.…”

Section: Introductionmentioning

confidence: 81%

Testing the Microsoft kinect skeletal tracking accuracy under varying external factors

Díaz¹,

Boss²,

Kyberd³

et al. 2022

MOJABB

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Focusing on its possible use in motion analysis, the accuracy of the Microsoft Kinect was investigated under various external factors including relative position, external IR light, computational power and large nearby surfaces. Two different experiments were performed that either focused on a general situation in an open room or when seated at a table. Results indicated that a large number of factors signiﬁcantly affect the measurement error, but with only minor effect sizes, where the relative position and orientation have shown to be most inﬂuential. Additionally, body movement and increased depth contrast (i.e. isolation from surrounding objects) are believed to increase the accuracy of the skeletal tracking process.

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