In the last few years there has been a growing interest in Human Activity Recognition (HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based applications such as online HAR, has emerged over the past decades. However, proposing sufficiently robust online activity recognition approach in smart environment setting is still considered as a remarkable challenge. This paper presents a novel online application of Hierarchical Hidden Markov Model in order to detect the current activity on the live streaming of sensor events. Our method consists of two phases. In the first phase, data stream is segmented based on the beginning and ending of the activity patterns. Also, on-going activity is reported with every receiving observation. This phase is implemented using Hierarchical Hidden Markov models. The second phase is devoted to the correction of the provided label for the segmented data stream based on statistical features. The proposed model can also discover the activities that happen during another activity - so-called interrupted activities. After detecting the activity pane, the predicted label will be corrected utilizing statistical features such as time of day at which the activity happened and the duration of the activity. We validated our proposed method by testing it against two different smart home datasets and demonstrated its effectiveness, which is competing with the state-of-the-art methods.
Moreover, the way that annotation has been done is often ignored while it makes bias on the data. In the most common approach of annotating, residents of the smart environment are asked to perform an activity and then annotation will be done based on activated sensors. Nonetheless, this approach may not be practical in all situations. A labeling mechanism presented in @cite_27 is an example of existing solutions for annotating sensor data automatically.
@cite_27
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Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning (Cohn, Ghahramani, and Jordan 1996) serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data. The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly labeled with the corresponding activity. Labeling, or annotating, sensor data with the corresponding activity can be time consuming, may require input from the smart home resident, and is often inaccurate. Therefore, in this paper we investigate four alternative mechanisms for annotating sensor data with a corresponding activity label. We evaluate the alternative methods along the dimensions of annotation time, resident burden, and accuracy using sensor data collected in a real smart apartment. A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6 and a class accuracy of 79.4 . Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems. In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation. In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network utilizes virtual image projections of the 3D point clouds for efficient inference. Further, we propose an automated process for large-scale cross-modal training data generation called Autolabeling, in order to boost semantic labeling performance while keeping the manual annotation effort low. The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset. LiLaNet is shown to significantly outperform current state-of-the-art CNN architectures for LiDAR data. Applying our automatically generated large-scale training data yields a boost of up to 14 percentage points compared to networks trained on manually annotated data only.