Long-term gait pattern assessment using a tri-axial accelerometer
Author:
Affiliation:
1. Complex Systems & Security Lab, Università Campus Bio-Medico di Roma, Rome, Italy
Funder
ERA-NET SAF€RA
Italian National Institute for Insurance
Basque Institute for Occupational Health and Safety
Publisher
Informa UK Limited
Subject
Biomedical Engineering,General Medicine
Link
https://www.tandfonline.com/doi/pdf/10.1080/03091902.2017.1293741
Reference32 articles.
1. Can Triaxial Accelerometry Accurately Recognize Inclined Walking Terrains?
2. Wang N, Ambikairajah E, Celler BG, et al. Accelerometry based classification of gait patterns using empirical mode decomposition. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 (ICASSP 2008). 2008. p. 617–620.
3. Zhang Q, Karunanithi M, Bradford D, et al. Activity of daily living assessment through wireless sensor data. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2014. p. 1752–1755.
4. Ibrahim R, Ambikairajah K, Celler EB, et al. Gait patterns classification using spectral features. IET Irish Signals and Systems Conference (ISSC 2008). 2008. p. 98–102.
5. De Cillis F. Inertial sensing for human activity recognition and personal indoor localization [PhD thesis]. Italy: Universita’Campus Bio-Medico di Roma; 2016.
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