A review of analytical techniques for gait data. Part 2: neural network and wavelet methods.

By Tom Chau. Gait & Posture, 2001

341 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/eb050b327cc56829a56be57ee94402a7596495cd

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Citations in the corpus

Direct measurement of human movement by accelerometry. (2008, background): “tatistically, the inputs are the independent variables hile the outputs are the dependant variables [80].”
Explaining the unique nature of individual gait patterns with deep learning (2018, methodology): “Due to benefits of machine learning methods in comparison to conventional approaches in gait analysis [54, 47], LRP appears highly promising to increase their transparency and therefore make them applicable and reliable for clinical diagnoses [11, 12, 76].”
Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly (2003, background): “Several investigators have already shown the advantages of wavelet transform for the analysis of kinematic signals [23], [28]–[30].”
The use of artificial neural networks in decision support in cancer: A systematic review (2006, background): “While parallel studies have identified neural network methods among the most prevalent non-traditional methodologies for data analysis ( Chau, 2001 ), in realising their potential application needs to overcome obstacles including the need for expanded databases and the need to establish multidiscipl”
A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data (2009, neutral): “For further details, see [40].”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “PCA locates the directions of maximal variance and the data are projected onto those directions.”
Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications (2004, background): “The importance of the advanced signal processing is emphasized in several studies related to motion analysis.(39,40)”