Comparative study on classifying human activities with miniature inertial and magnetic sensors

DOI10.1016/j.patcog.2010.04.019zbMath1213.68513OpenAlexW2123504417WikidataQ56594424 ScholiaQ56594424MaRDI QIDQ991988

Billur Barshan, Orkun Tunçel, Kerem Altun

Publication date: 8 September 2010

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11693/22200

zbMATH Keywords

least-squares methodfeature extractionartificial neural networksdecision treesupport vector machinesdynamic time warping\(k\)-nearest neighborgyroscopeinertial sensorsBayesian decision makingaccelerometermagnetometeractivity recognition and classificationfeature reductionrule-based algorithm

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (7)

Batch self-organizing maps for distributional data with an automatic weighting of variables and components ⋮ Uncovering block structures in large rectangular matrices ⋮ On convergence of iterative methods for maximal correlation problems ⋮ Modal clustering of matrix-variate data ⋮ Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data ⋮ Sparse wavelet regression with multiple predictive curves ⋮ Unnamed Item

Uses Software

• LIBSVM

Cites Work

• On-line signature recognition based on VQ-DTW
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Gesture spotting with body-worn inertial sensors to detect user activities
• A Survey of Computer Vision-Based Human Motion Capture
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