Gait Type Analysis Using Dynamic Bayesian Networks

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Gait Type Analysis Using Dynamic Bayesian Networks is …
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scholarly articleQ13442814

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P8978DBLP publication IDjournals/sensors/KozlowAY18
P356DOI10.3390/S18103329
P932PMC publication ID6210198
P698PubMed publication ID30287787

P50authorSvetlana YanushkevichQ92046129
P2093author name stringNoor Abid
Patrick Kozlow
P2860cites workValidity of the Kinect for Gait Assessment: A Focused ReviewQ26767240
Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait AssessmentsQ27314807
A dynamic Bayesian network for estimating the risk of falls from real gait dataQ30573419
Implementation of machine learning for classifying prosthesis type through conventional gait analysis.Q40139633
Gait assessment using the Microsoft Xbox One Kinect: Concurrent validity and inter-day reliability of spatiotemporal and kinematic variablesQ40845684
Step accumulation per minute epoch is not the same as cadence for free-living adults.Q45737894
Surface-EMG analysis for the quantification of thigh muscle dynamic co-contractions during normal gait.Q51104882
Support vector machines for automated gait classification.Q51974282
A Microsoft Kinect-Based Point-of-Care Gait Assessment Framework for Multiple Sclerosis Patients.Q53055063
Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors.Q55235108
Concurrent related validity of the GAITRite walkway system for quantification of the spatial and temporal parameters of gaitQ78818359
How humans walk: bout duration, steps per bout, and rest durationQ83230105
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P433issue10
P407language of work or nameEnglishQ1860
P921main subjectBayesian networkQ812540
P577publication date2018-10-04
P1433published inSensorsQ3478643
P1476titleGait Type Analysis Using Dynamic Bayesian Networks
P478volume18

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