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. 2022 Dec 17;19(24):16989.
doi: 10.3390/ijerph192416989.

RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks

Hanna Marie Röhling  1   2   3   4 , Karen Otte  1   2   3   4 , Sophia Rekers  5   6 , Carsten Finke  5   6 , Rebekka Rust  1   2   3   7 , Eva-Maria Dorsch  1   2   3   5 , Behnoush Behnia  8 , Friedemann Paul  1   2   3   5   7 , Tanja Schmitz-Hübsch  1   2   3   7
Affiliations

RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks

Hanna Marie Röhling et al. Int J Environ Res Public Health. .

Abstract

Background: Instrumental motion analysis constitutes a promising development in the assessment of motor function in clinical populations affected by movement disorders. To foster implementation and facilitate interpretation of respective outcomes, we aimed to establish normative data of healthy subjects for a markerless RGB-Depth camera-based motion analysis system and to illustrate their use.

Methods: We recorded 133 healthy adults (56% female) aged 20 to 60 years with an RGB-Depth camera-based motion analysis system. Forty-three spatiotemporal parameters were extracted from six short, standardized motor tasks-including three gait tasks, stepping in place, standing-up and sitting down, and a postural control task. Associations with confounding factors, height, weight, age, and sex were modelled using a predictive linear regression approach. A z-score normalization approach was provided to improve usability of the data.

Results: We reported descriptive statistics for each spatiotemporal parameter (mean, standard deviation, coefficient of variation, quartiles). Robust confounding associations emerged for step length and step width in comfortable speed gait only. Accessible normative data usage was lastly exemplified with recordings from one randomly selected individual with multiple sclerosis.

Conclusion: We provided normative data for an RGB depth camera-based motion analysis system covering broad aspects of motor capacity.

Keywords: Microsoft Kinect v2; RGB-Depth camera; gait analysis; instrumental motion analysis; normative data; postural control; standing up and sitting down; stepping in place; tandem gait.

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Conflict of interest statement

K.O. is a stakeholder and H.M.R. a paid employee at Motognosis GmbH. All other authors state no conflict of interest.

Figures

Figure 1
Figure 1
Technical set-up of the Motognosis Labs motion analysis system with a single Microsoft Kinect v2 sensor with exemplary sketch of a gait task. The illustration was included by courtesy of Motognosis GmbH.
Figure 2
Figure 2
Distributions of raw spatiotemporal parameters color coded by motor task. Abbreviations: AP: anterior-posterior; n.u.: unitless; POCO: postural control; RR: Romberg ratio; SAS: standing up and sitting down; SCSW: short comfortable speed walk; SIP: stepping in place; SLW: short line walk; SMSW: short maximum speed walk.
Figure 3
Figure 3
Z-score transformed spatiotemporal parameters from recordings of a randomly picked person with multiple sclerosis from the Valkinect study. Abbreviations: AP: anterior-posterior; RR: Romberg ratio; zraw: z-score for raw values; zres: z-score for residual values.
See this image and copyright information in PMC

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