This paper investigates a new annotation technique that reduces significantly the amount of time to annotate training data for gesture recognition. Conventionally, the annotations comprise the start and end times, and the corresponding labels of gestures in sensor recordings. In this work, we propose a one-time point annotation in which labelers do not have to select the start and end time carefully, but just mark a one-time point within the time a gesture is happening. The technique gives more freedom and reduces significantly the burden for labelers. To make the one-time point annotations applicable, we propose a novel BoundarySearch algorithm to find automatically the correct temporal boundaries of gestures by discovering data patterns around their given one-time point annotations. The corrected annotations are then used to train gesture models. We evaluate the method on three applications from wearable gesture recognition with various gesture classes (10-17 classes) recorded with different sensor modalities. The results show that training on the corrected annotations can achieve performances close to a fully supervised training on clean annotations (lower by just up to 5 percent F1-score on average). Furthermore, the BoundarySearch algorithm is also evaluated on the ChaLearn 2014 multi-modal gesture recognition challenge recorded with Kinect sensors from computer vision and achieves similar results.

Activity annotation in videos is necessary to create a training dataset for most of activity recognition systems. This is a very time consuming and repetitive task. Crowdsourcing gains popularity to distribute annotation tasks to a large pool of taggers. We present for the first time an approach to achieve good quality for activity annotation in videos through crowdsourcing on the AmazonMechanical Turk platform (AMT). Taggers must annotate the start, end boundaries and the label of all occurrences of activities in videos. Two strategies to detect non-serious taggers according to temporal annotated results are presented. Individual filtering checks the consistence in the answers of each tagger with the characteristic of dataset to identify and remove nonserious taggers. Collaborative filtering checks the agreement in annotations among taggers. The filtering techniques detect and remove non-serious taggers and finally, the majority voting applied to AMT temporal tags to generate one final AMT activity annotation set. We conduct the experiments to get activity annotation from AMT on a subset of two rich datasets frequently used in activity recognition. The results show that our proposed filtering strategies can increase the accuracy by up to 40%. The final annotation set is of comparable quality of the annotation of experts with high accuracy (76% to 92%).

Human activity recognition systems traditionally require a manual annotation of massive training data, which is laborious and non-scalable. An alternative approach is mining existing online crowd-sourced repositories for open-ended, free annotated training data. However, differences across data sources or in observed contexts prevent a crowd-sourced based model reaching user-dependent recognition rates.

The continuous growth of social web applications along with the development of sensor capabilities in electronic devices is creating countless opportunities to analyze the enormous amounts of data that is continuously steaming from these applications and devices. To process large scale data on large scale computing clusters, MapReduce has been introduced as a framework for parallel computing. However, most of the current implementations of the MapReduce framework support only the execution of fixed-input jobs. Such restriction makes these implementations inapplicable for most streaming applications, in which queries are continuous in nature, and input data streams are continuously received at high arrival rates. In this demonstration, we showcase M3, a prototype implementation of the MapReduce framework in which continuous queries over streams of data can be efficiently answered. M3 extends Hadoop, the open source implementation of MapReduce, bypassing the Hadoop Distributed File System (HDFS) to support main-memory-only processing. Moreover, M3 supports continuous execution of the Map and Reduce phases where individual Mappers and Reducers never terminate.

Template matching methods using Dynamic Time Warping (DTW) have been used recently for online gesture recognition from body-worn motion sensors. However, DTW has been shown sensitive under the strong presence of noise in time series. In sensor readings, labeling temporal boundaries of daily gestures precisely is rarely achievable as they are often intertwined. Moreover, the variation in daily gesture execution always exists. Therefore, here we propose two template matching methods utilizing the Longest Common Subsequence (LCSS) to improve robustness against such noise for online gesture recognition. Segmented LCSS utilizes a sliding window to define the unknown boundaries of gestures in the continuous coming sensor readings and detects efficiently a possibly shorter gesture within it. WarpingLCSS is our novel variant of LCSS to determine occurrences of gestures without segmenting data and performs one order of magnitude faster than the Segmented LCSS. The WarpingLCSS requires low-resource settings to process new arriving samples, thus it is suitable for real-time gesture recognition implemented directly on the small wearable devices. We compare our methods with the existing template matching methods based on Dynamic Time Warping (DTW) on two real-world gesture datasets from arm-worn accelerometer data. The results demonstrate that the LCSS approaches outperform the existing template matching approaches (about 12% in accuracy) in the dataset that suffers from boundary noise and execution variation.