Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Type of interference</td><td class="align_center">Source of interference</td><td class="align_center">Countermeasures of this study</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">Electrode impedance</td><td class="align_center">Skin oil and sweat</td><td class="align_center">Antialiasing filtering and front-end amplifier circuit optimization</td></tr><tr><td class="align_left">Motion disturbance</td><td class="align_center">Contact impedance change, electrode displacement, etc.</td><td class="align_center">Designing flexible electrodes for improved attachments</td></tr><tr><td class="align_left">Electromagnetic interference</td><td class="align_center">50 Hz power frequency and surrounding electromagnetic environment interference</td><td class="align_center">Battery power, circuit board wiring isolation, etc.</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Major interferences in EMG detection and the countermeasures taken in this study.</div>

Journal of Sensors

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Table 1

Table 1: Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm 