A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf
.
Robust Online Gesture Recognition with Crowdsourced Annotations
[chapter]
2017
Gesture Recognition
Crowdsourcing is a promising way to reduce the effort of collecting annotations for training gesture recognition systems. Crowdsourced annotations suffer from "noise" such as mislabeling, or inaccurate identification of start and end time of gesture instances. In this paper we present SegmentedLCSS and WarpingLCSS, two template-matching methods offering robustness when trained with noisy crowdsourced annotations to spot gestures from wearable motion sensors. The methods quantize signals into
doi:10.1007/978-3-319-57021-1_18
fatcat:pka7r5k2ura5xfl3izygtijzme