Wavelet-derived features as indicators of physiological changes induced by bed rest

作者：

Highlights：

• Longitudinal algorithmic classification based on sEMG is demonstrated.

• Successful via SVM and k-NN algorithms despite intrinsic hurdles to sEMG measurement.

• Wavelet-derived features add to classifier performance.

摘要

•Longitudinal algorithmic classification based on sEMG is demonstrated.•Successful via SVM and k-NN algorithms despite intrinsic hurdles to sEMG measurement.•Wavelet-derived features add to classifier performance.

论文关键词：Wavelets,Classification algorithms,Electromyography

论文评审过程：Received 9 December 2016, Revised 11 August 2017, Accepted 12 August 2017, Available online 17 August 2017, Version of Record 23 August 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.08.024