7 research outputs found
Improving Wearable-Based Activity Recognition Using Image Representations
Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today’s state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to
be used in next-generations wireless systems. This led to a large body of
research work that applies ML techniques to solve problems in different layers
of the wireless transmission link. However, most of these applications rely on
supervised learning which assumes that the source (training) and target (test)
data are independent and identically distributed (i.i.d). This assumption is
often violated in the real world due to domain or distribution shifts between
the source and the target data. Thus, it is important to ensure that these
algorithms generalize to out-of-distribution (OOD) data. In this context,
domain generalization (DG) tackles the OOD-related issues by learning models on
different and distinct source domains/datasets with generalization capabilities
to unseen new domains without additional finetuning. Motivated by the
importance of DG requirements for wireless applications, we present a
comprehensive overview of the recent developments in DG and the different
sources of domain shift. We also summarize the existing DG methods and review
their applications in selected wireless communication problems, and conclude
with insights and open questions
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review
The Human Activity Recognition (HAR) tasks automatically identify human
activities using the sensor data, which has numerous applications in
healthcare, sports, security, and human-computer interaction. Despite
significant advances in HAR, critical challenges still exist. Game theory has
emerged as a promising solution to address these challenges in machine learning
problems including HAR. However, there is a lack of research work on applying
game theory solutions to the HAR problems. This review paper explores the
potential of game theory as a solution for HAR tasks, and bridges the gap
between game theory and HAR research work by suggesting novel game-theoretic
approaches for HAR problems. The contributions of this work include exploring
how game theory can improve the accuracy and robustness of HAR models,
investigating how game-theoretic concepts can optimize recognition algorithms,
and discussing the game-theoretic approaches against the existing HAR methods.
The objective is to provide insights into the potential of game theory as a
solution for sensor-based HAR, and contribute to develop a more accurate and
efficient recognition system in the future research directions
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
The vast proliferation of sensor devices and Internet of Things enables the
applications of sensor-based activity recognition. However, there exist
substantial challenges that could influence the performance of the recognition
system in practical scenarios. Recently, as deep learning has demonstrated its
effectiveness in many areas, plenty of deep methods have been investigated to
address the challenges in activity recognition. In this study, we present a
survey of the state-of-the-art deep learning methods for sensor-based human
activity recognition. We first introduce the multi-modality of the sensory data
and provide information for public datasets that can be used for evaluation in
different challenge tasks. We then propose a new taxonomy to structure the deep
methods by challenges. Challenges and challenge-related deep methods are
summarized and analyzed to form an overview of the current research progress.
At the end of this work, we discuss the open issues and provide some insights
for future directions
A novel distribution-embedded neural network for sensor-based activity recognition
Feature-engineering-based machine learning models and deep learning models have been explored for wearable-sensor-based human activity recognition. For both types of methods, one crucial research issue is how to extract proper features from the partitioned segments of multivariate sensor readings. Existing methods have different drawbacks: 1) feature-engineering-based methods are able to extract meaningful features, such as statistical or structural information underlying the segments, but usually require manual designs of features for different applications, which is time consuming, and 2) deep learning models are able to learn temporal and/or spatial features from the sensor data automatically, but fail to capture statistical information. In this paper, we propose a novel deep learning model to automatically learn meaningful features including statistical features, temporal features and spatial correlation features for activity recognition in a unified framework. Extensive experiments are conducted on four datasets to demonstrate the effectiveness of our proposed method compared with state-of-the-art baselines.MOE (Min. of Education, S’pore)MOH (Min. of Health, S’pore)Accepted versio