A comprehensive analysis of the validity and reliability of the perception neuron studio for upper-body motion capture

(Eine umfassende Analyse der Gültigkeit und Zuverlässigkeit des Perception Neuron Studios für die Bewegungserfassung des Oberkörpers)

The Perception Neuron Studio (PNS) is a cost-effective and widely used inertial motion capture system. However, a comprehensive analysis of its upper-body motion capture accuracy is still lacking, before it is being applied to biomechanical research. Therefore, this study first evaluated the validity and reliability of this system in upper-body capturing and then quantified the system`s accuracy for different task complexities and movement speeds. Seven participants performed simple (eight single-DOF upper-body movements) and complex tasks (lifting a 2.5 kg box over the shoulder) at fast and slow speeds with the PNS and OptiTrack (gold-standard optical system) collecting kinematics data simultaneously. Statistical metrics such as CMC, RMSE, Pearson`s r, R2, and Bland-Altman analysis were utilized to assess the similarity between the two systems. Test-retest reliability included intra- and intersession relations, which were assessed by the intraclass correlation coefficient (ICC) as well as CMC. All upper-body kinematics were highly consistent between the two systems, with CMC values 0.73-0.99, RMSE 1.9-12.5°, Pearson`s r 0.84-0.99, R2 0.75-0.99, and Bland-Altman analysis demonstrating a bias of 0.2-27.8° as well as all the points within 95% limits of agreement (LOA). The relative reliability of intra- and intersessions was good to excellent (i.e., ICC and CMC were 0.77-0.99 and 0.75-0.98, respectively). The paired t-test revealed that faster speeds resulted in greater bias, while more complex tasks led to lower consistencies. Our results showed that the PNS could provide accurate enough upper-body kinematics for further biomechanical performance analysis.
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Bibliographische Detailangaben
Schlagworte:
Notationen:Naturwissenschaften und Technik
Tagging:Oberkörper
Veröffentlicht in:Sensors
Sprache:Englisch
Veröffentlicht: 2022
Online-Zugang:https://doi.org/10.3390/s22186954
Jahrgang:22
Heft:18
Seiten:6954
Dokumentenarten:Artikel
Level:hoch