TY  - JOUR
PY  - 2019//
TI  - Methods for gait analysis during obstacle avoidance task
JO  - Annals of biomedical engineering
A1  - Patashov, Dmitry
A1  - Menahem, Yakir
A1  - Ben-Haim, Ohad
A1  - Gazit, Eran
A1  - Maidan, Inbal
A1  - Mirelman, Anat
A1  - Sosnik, Ronen
A1  - Goldstein, Dmitry
A1  - Hausdorff, Jeffrey M.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - In this study, we present algorithms developed for gait analysis, but suitable for many other signal processing tasks. A novel general-purpose algorithm for extremum estimation of quasi-periodic noisy signals is proposed. This algorithm is both flexible and robust, and allows custom adjustments to detect a predetermined wave pattern while being immune to signal noise and variability. A method for signal segmentation was also developed for analyzing kinematic data recorded while performing on obstacle avoidance task. The segmentation allows detecting preparation and recovery phases related to obstacle avoidance. A simple kernel-based clustering method was used for classification of unsupervised data containing features of steps within the walking trial and discriminating abnormal from regular steps. Moreover, a novel algorithm for missing data approximation and adaptive signal filtering is also presented. This algorithm allows restoring faulty data with high accuracy based on the surrounding information. In addition, a predictive machine learning technique is proposed for supervised multiclass labeling with non-standard label structure.<p />  <p>Language: en</p>
LA  - en
SN  - 0090-6964
UR  - http://dx.doi.org/10.1007/s10439-019-02380-4
ID  - ref1
ER  -