The efficacy of the Microsoft Kinect TM to assess human bimanual coordination
The Microsoft Kinect has been used in studies examining posture and gait. Despite the advantages of portability and low cost, this device has not been used to assess interlimb coordination. Fundamental insights into movement control, variability, health, and functional status can be gained by examin...
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Veröffentlicht in: | Behavior research methods 2017-06, Vol.49 (3), p.1030 |
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description | The Microsoft Kinect has been used in studies examining posture and gait. Despite the advantages of portability and low cost, this device has not been used to assess interlimb coordination. Fundamental insights into movement control, variability, health, and functional status can be gained by examining coordination patterns. In this study, we investigated the efficacy of the Microsoft Kinect to capture bimanual coordination relative to a research-grade motion capture system. Twenty-four healthy adults performed coordinated hand movements in two patterns (in-phase and antiphase) at eight movement frequencies (1.00-3.33 Hz). Continuous relative phase (CRP) and discrete relative phase (DRP) were used to quantify the means (mCRP and mDRP) and variability (sdCRP and sdDRP) of coordination patterns. Between-device agreement was assessed using Bland-Altman bias with 95 % limits of agreement, concordance correlation coefficients (absolute agreement), and Pearson correlation coefficients (relative agreement). Modest-to-excellent relative and absolute agreements were found for mCRP in all conditions. However, mDRP showed poor agreement for the in-phase pattern at low frequencies, due to large between-device differences in a subset of participants. By contrast, poor absolute agreement was observed for both sdCRP and sdDRP, while relative agreement ranged from poor to excellent. Overall, the Kinect captures the macroscopic patterns of bimanual coordination better than coordination variability. |
doi_str_mv | 10.3758/s13428-016-0764-7 |
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Despite the advantages of portability and low cost, this device has not been used to assess interlimb coordination. Fundamental insights into movement control, variability, health, and functional status can be gained by examining coordination patterns. In this study, we investigated the efficacy of the Microsoft Kinect to capture bimanual coordination relative to a research-grade motion capture system. Twenty-four healthy adults performed coordinated hand movements in two patterns (in-phase and antiphase) at eight movement frequencies (1.00-3.33 Hz). Continuous relative phase (CRP) and discrete relative phase (DRP) were used to quantify the means (mCRP and mDRP) and variability (sdCRP and sdDRP) of coordination patterns. Between-device agreement was assessed using Bland-Altman bias with 95 % limits of agreement, concordance correlation coefficients (absolute agreement), and Pearson correlation coefficients (relative agreement). Modest-to-excellent relative and absolute agreements were found for mCRP in all conditions. However, mDRP showed poor agreement for the in-phase pattern at low frequencies, due to large between-device differences in a subset of participants. By contrast, poor absolute agreement was observed for both sdCRP and sdDRP, while relative agreement ranged from poor to excellent. 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Despite the advantages of portability and low cost, this device has not been used to assess interlimb coordination. Fundamental insights into movement control, variability, health, and functional status can be gained by examining coordination patterns. In this study, we investigated the efficacy of the Microsoft Kinect to capture bimanual coordination relative to a research-grade motion capture system. Twenty-four healthy adults performed coordinated hand movements in two patterns (in-phase and antiphase) at eight movement frequencies (1.00-3.33 Hz). Continuous relative phase (CRP) and discrete relative phase (DRP) were used to quantify the means (mCRP and mDRP) and variability (sdCRP and sdDRP) of coordination patterns. Between-device agreement was assessed using Bland-Altman bias with 95 % limits of agreement, concordance correlation coefficients (absolute agreement), and Pearson correlation coefficients (relative agreement). Modest-to-excellent relative and absolute agreements were found for mCRP in all conditions. However, mDRP showed poor agreement for the in-phase pattern at low frequencies, due to large between-device differences in a subset of participants. By contrast, poor absolute agreement was observed for both sdCRP and sdDRP, while relative agreement ranged from poor to excellent. 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Despite the advantages of portability and low cost, this device has not been used to assess interlimb coordination. Fundamental insights into movement control, variability, health, and functional status can be gained by examining coordination patterns. In this study, we investigated the efficacy of the Microsoft Kinect to capture bimanual coordination relative to a research-grade motion capture system. Twenty-four healthy adults performed coordinated hand movements in two patterns (in-phase and antiphase) at eight movement frequencies (1.00-3.33 Hz). Continuous relative phase (CRP) and discrete relative phase (DRP) were used to quantify the means (mCRP and mDRP) and variability (sdCRP and sdDRP) of coordination patterns. Between-device agreement was assessed using Bland-Altman bias with 95 % limits of agreement, concordance correlation coefficients (absolute agreement), and Pearson correlation coefficients (relative agreement). Modest-to-excellent relative and absolute agreements were found for mCRP in all conditions. However, mDRP showed poor agreement for the in-phase pattern at low frequencies, due to large between-device differences in a subset of participants. By contrast, poor absolute agreement was observed for both sdCRP and sdDRP, while relative agreement ranged from poor to excellent. Overall, the Kinect captures the macroscopic patterns of bimanual coordination better than coordination variability.</abstract><cop>United States</cop><pmid>27351987</pmid><doi>10.3758/s13428-016-0764-7</doi></addata></record> |
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subjects | Adult Behavioral Research - instrumentation Female Hand Humans Male Movement - physiology Psychomotor Performance - physiology |
title | The efficacy of the Microsoft Kinect TM to assess human bimanual coordination |
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