Robust Online Gesture Recognition with Crowdsourced Annotations

Abstract

Crowdsourcing is a promising way to reduce the effort of collecting annotations for training gesture recognition systems. Crowdsourced annotations suffer from 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 strings of characters and then apply variations of the longest common subsequence algorithm (LCSS) to spot gestures. We compare the noise robustness of our methods against baselines which use dynamic time warping (DTW) and support vector machines (SVM). The experiments are performed on data sets with various gesture classes (10-17 classes) recorded from accelerometers on arms, with both real and synthetic crowdsourced annotations. WarpingLCSS has similar or better performance than baselines in absence of noisy annotations. In presence of 60% mislabeled instances, WarpingLCSS outperformed SVM by 22% F1-score and outperformed DTW-based methods by 36% F1-score on average. SegmentedLCSS yields similar performance as WarpingLCSS, however it performs one order of magnitude slower. Additionally, we show to use our methods to filter out the noise in the crowdsourced annotation before training a traditional classifier. The filtering increases the performance of SVM by 20% F1-score and of DTW-based methods by 8% F1-score on average in the noisy real crowdsourced annotations.