Decoding Gait Signatures: Exploring Individual Patterns in Pathological Gait Using Explainable AI
This study explores the application of machine learning (ML) to derive and analyze individual gait patterns (i.e., gait signatures) from ground reaction force data. This study leverages three datasets...
Our latest research ๐๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ฎ๐ถ๐ ๐ฆ๐ถ๐ด๐ป๐ฎ๐๐๐ฟ๐ฒ๐: ๐๐
๐ฝ๐น๐ผ๐ฟ๐ถ๐ป๐ด ๐๐ป๐ฑ๐ถ๐๐ถ๐ฑ๐๐ฎ๐น ๐ฃ๐ฎ๐๐๐ฒ๐ฟ๐ป๐ ๐ถ๐ป ๐ฃ๐ฎ๐๐ต๐ผ๐น๐ผ๐ด๐ถ๐ฐ๐ฎ๐น ๐๐ฎ๐ถ๐ ๐๐๐ถ๐ป๐ด ๐๐
๐ฝ๐น๐ฎ๐ถ๐ป๐ฎ๐ฏ๐น๐ฒ ๐๐ has been published in IEEE Access @ @embs.org section ๐ doi.org/10.1109/ACCE...
#GaitAnalysis #Biomechanics #AI #ML #XAI #GaitSignature #BiomechSky
25.12.2024 22:17 โ
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Corrected link here: doi.org/10.1016/j.jb...
13.03.2024 10:56 โ
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โก๐ซ Caution: Be cautions when using variability-based metrics based on markerless data. The higher inter-trial variability could potentially lead to misleading results.
13.03.2024 10:55 โ
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๐ Mitigation Strategies: Do not trust single waveforms, use avareged ones and make sure you have enough data to calculate robust mean waveforms.
13.03.2024 10:55 โ
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Here are the key findings ๐ฏ ๐ :
โฌ ๐ Increased Inter-Trial Variability: the markerless system exhibited an increase in inter-trial variability of up to 22%. It seems that the markerless pose estimation pipelines introduce additional variability in kinematic data ontop of the natural variability.
13.03.2024 10:55 โ
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I am very happy to share our lastest study with #OpenCap, a markerless mocap solution based on smartphones. We evaluated the inter-trial variability between markerless and marker-based data. Our paper was just recently accepted and can be found here: lnkd.in/dsFufy8F
13.03.2024 10:53 โ
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07.02.2024 22:03 โ
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