sift:curve_registration:curve_registration_for_biomechanical_waveforms
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| sift:curve_registration:curve_registration_for_biomechanical_waveforms [2024/07/17 15:10] – sgranger | sift:curve_registration:curve_registration_for_biomechanical_waveforms [2024/07/17 15:44] (current) – created sgranger | ||
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| Curve registration has been proven to aid in analysis of biomechanical signals, and can produce more accurate results in time continuous analysis including PCA, due to sensitivity towards time varying events. When looking at a signal in a dataset, we typically see variation in the amplitude and phase. When physical time may not impact or be relevant to real life systems, there is sometimes a need to align the curves on a system time rather than a clock time to correct phase variation. This is done by registering the curves to a specific landmark. In the world of biomechanics and gait analysis, we can register curves by certain events, such as the max or min of a signal to see a better representation of amplitude variation. To review supporting literature, see [[[https:// | Curve registration has been proven to aid in analysis of biomechanical signals, and can produce more accurate results in time continuous analysis including PCA, due to sensitivity towards time varying events. When looking at a signal in a dataset, we typically see variation in the amplitude and phase. When physical time may not impact or be relevant to real life systems, there is sometimes a need to align the curves on a system time rather than a clock time to correct phase variation. This is done by registering the curves to a specific landmark. In the world of biomechanics and gait analysis, we can register curves by certain events, such as the max or min of a signal to see a better representation of amplitude variation. To review supporting literature, see [[[https:// | ||
sift/curve_registration/curve_registration_for_biomechanical_waveforms.1721229007.txt.gz · Last modified: 2024/07/17 15:10 by sgranger