Abstract Summary
The use of signal classification techniques to accurately classify¶ activities of daily living has gained importance in assistive¶ technologies. The aim of this paper was to implement different¶ classification techniques to identify user intent from inertial¶ measurement units (IMUs) attached to different segments of the¶ body. In addition, the performance of these classifiers was¶ compared when selecting the 15 most significant features and¶ the significant segment. Decision tree and Bayes showed the¶ highest and lowest accuracy, respectively
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