0000000000335494

AUTHOR

Sebastian Lapuschkin

showing 3 related works from this author

Revealing the unique features of each individual’s muscle activation signatures

2020

AbstractThere is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each i…

Computer sciencebusiness.industryRelevance (information retrieval)Muscle activationPattern recognitionArtificial intelligencebusinessLower limbSignature (logic)
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Revealing the unique features of each individual's muscle activation signatures

2021

International audience; There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance …

Movement patternsComputer science[SDV]Life Sciences [q-bio]MovementBiomedical EngineeringBiophysicsBioengineeringWalkingElectromyographyBiochemistryLower limbMachine LearningBiomaterials03 medical and health sciences0302 clinical medicine[SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO]medicineHumansRelevance (information retrieval)Muscle SkeletalElectromyographic (EMG)030304 developmental biology0303 health sciencesmedicine.diagnostic_testElectromyographybusiness.industryMusclesMotor controlLife Sciences–Physics interfacePattern recognitionMuscle activationSignature (logic)Support vector machineStatistical classificationArtificial intelligencebusiness030217 neurology & neurosurgeryBiotechnologyJournal of The Royal Society Interface
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Explaining the unique nature of individual gait patterns with deep learning

2019

Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input …

FOS: Computer and information sciencesAdultMaleComputer Science - Machine Learninglcsh:Rlcsh:MedicineMachine Learning (stat.ML)Healthy VolunteersArticleMachine Learning (cs.LG)Biomechanical PhenomenaYoung AdultDeep LearningStatistics - Machine LearningHumanslcsh:QFemale000 Allgemeineslcsh:ScienceGait000 Generalities
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