جستجوی مقالات مرتبط با کلیدواژه « activity recognition » در نشریات گروه « برق »

Human activity recognition (HAR) has the potential to significantly impact applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Because of the prevalence of wireless devices, the Wi-Fi-based approach has attracted a lot of attention among other existing methods such as sensor-based and vision-based HAR. Wi-Fi devices can be used to distinguish between daily activities such as "walking," "running," and "sleeping," which affect Wi-Fi signal propagation. This paper proposes a Deep Learning method for HAR tasks that makes use of channel state information (CSI). We convert the CSI data to RGB images and classify the activity recognition using a 2D-Convolutional Neural Network (CNN). We evaluate the performance of the proposed method on two publicly available datasets for CSI data. Our experiments show that converting data into RGB images improves performance and accuracy compared to our previous method by at least 5%.

Mobile technologies have deployed a variety of Internet–based services via location based services. The adoption of these services by users has led to mammoth amounts of trajectory data. To use these services effectively, analysis of these kinds of data across different application domains is required in order to identify the activities that users might need to do in different places. Researchers from different communities have developed models and techniques to extract activity types from such data, but they mainly have focused on the geometric properties of trajectories and do not consider the semantic aspect of moving objects. This work proposes a new ontology-based approach so as to recognize human activity from GPS data for understanding and interpreting mobility data. The performance of the approach was tested and evaluated using a dataset, which was acquired by a user over a year within the urban area in the City of Calgary in 2010. It was observed that the accuracy of the results was related to the availability of the points of interest around the places that the user had stopped. Moreover, an evaluation experiment was done, which revealed the effectiveness of the proposed method with an improvement of 50 % performance with complexity trend of an O(n).

This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using beta probability distribution function. The frames of opinion owners are derived automatically for activities, to achieve more flexibility and extensibility. Our method is verified via two experiments. In the first experiment, it is compared to a naïve Bayes approach and three ontology based methods. In this experiment our method outperforms the naïve Bayes classifier, having 88.9% accuracy. However, it is comparable and similar to the ontology based schemes, but since no manual ontology definition is needed, our method is more flexible and extensible than the previous ones. In the second experiment, a larger dataset is used and our method is compared to three approaches which are based on naïve Bayes classifiers, hidden Markov models, and hidden semi Markov models. Three features are extracted from sensors’ data and incorporated in the benchmark methods, making nine implementations. In this experiment our method shows an accuracy of 94.2% that in most of the cases outperforms the benchmark methods, or is comparable to them.