This work builds a sensor graph from DC sensors for anomaly detection.
problem Anomaly detection in data centers with complex sensor relationships.
method Data-driven pipeline (ts2graph) to build a sensor graph from sensor time series.
result Graph neural network (GNN) outperforms existing methods by 2-3 times in anomaly detection.
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
AutoEncoder smooths noisy sensor data and interpolates missing values.
problem Noisy sensor data and missing timepoints require interpolation.
method Uses AutoEncoder to denoise and interpolate data.
result AutoEncoder improves data quality and reveals dynamics.
Optimizes seismic monitoring networks using Bayesian OED.
problem Improve seismic event identification and location.
method Bayesian optimal experimental design (OED) to configure sensor networks.
result Optimized sensor network improves seismic event identification and location.
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
Weather balloons deploy sensors to collect stratospheric data.
problem Limited data collection in the stratosphere.
method Modeling forecast deviation as a Gaussian process to determine sensor release times; novel hardware system for optimal sensor release.
result Data engineering framework effectively collects stratospheric data through real flights and simulations.
Deep RL optimizes sensor placement in digital twins for dynamic data acquisition.
problem Limited applicability of traditional sensor placement techniques for online applications.
method Formulates sensor placement as a Markov decision process and uses deep reinforcement learning.
result Improves predictive accuracy and reliability of digital twins through adaptive sensor repositioning.
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such s…
The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors m…
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Smartphones can estimate heart rate from other sensor data.
problem Gaps in heart rate data from wearable sensors.
method Regression, SVM, and random forest algorithms to estimate heart rate from smartphone data.
result Smartphone data can improve heart rate estimation from wearable sensors.
Paper uses low-dimensional sensor data analysis for better fault detection.
problem Fault detection in critical equipment using multivariate, nonlinear sensor data.
method Exploits t-SNE and KPCA for nonlinear dimension reduction and anomaly detection.
result Low-dimensional representations improve interpretability and edge processing in IoT.
Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concen…
Adversarial approach enhances sensor fusion for robust target detection.
problem Improving target detection and classification using multi-modal sensor fusion.
method Generative network learns latent space from various sensor modalities, then detects damaged sensors and safeguards performance.
result Automatic robustness against noisy/damaged sensors achieved.
Deep RL predicts equipment maintenance from sensor data.
problem Equipment downtime due to sensor data overload.
method Model-free Deep Reinforcement Learning for optimal maintenance policy.
result Automatic maintenance policy learning from sensor data.
Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approac…
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
Sensors are an integral part of modern Internet of Things (IoT) applications. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from the individual sensors of these systems. In this paper we particularly focus on the problem of the scarce amount of training data available pe…
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
SparseSense improves HAR from sparse sensor data, outperforming state-of-the-art models.
problem Learning activity recognition from highly sparse sensor data streams.
method Set-based neural networks for end-to-end learning from sparse data.
result Significant performance improvements in HAR from passive sensor datasets.
A novel sequence-to-sequence model predicts missing sensor data.
problem Missing sensor data in sequences.
method Formulated a novel sequence-to-sequence model using forward and backward RNNs.
result The model produces the lowest errors in 12% more cases than the current state-of-the-art.
Improved sleep apnea detection using sensor fusion and backward shortcut connections.
problem Untreated sleep apnea leads to severe health consequences; automated detection is needed.
method Late sensor fusion using backward shortcut connections to improve deep learning models.
result Significant improvement in predictive performance over single sensor methods.
Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduct…
Proposes a neural network for handling multi-sensor time series with varying input dimensions.
problem Handling multi-sensor time series with varying input dimensions.
method Graph neural network conditioning vectors for zero-shot transfer learning.
result Better generalization in activity recognition and equipment prognostics datasets.
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
Paper proposes a new neural machine translation method for wave data.
problem Limited real-world sensor data for continuous signal waves.
method Introduces window-based representation and iterative back-translation for wave data.
result Significant performance improvements in wave translation tasks.
A method for inferring ground-truth signals from degraded sensor data.
problem Inferring ground-truth signals from multiple degraded sensor signals.
method Iterative correction of degraded signals using a Bayesian multi-sensor data fusion method.
result The method effectively infers ground-truth signals from noisy and degraded sensor data.
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
Physics-informed denoising improves sensor data accuracy without needing clean data.
problem Noise in real-life sensor data affects system performance and reliability.
method Physics-informed denoising model that uses algebraic relationships between sensor measurements governed by physical laws.
result Achieved state-of-the-art performance in various real-world applications.
New approach uses secants to improve sensor placement and feature selection for nonlinear systems.
problem Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems.
method Data-driven approach using secant vectors to develop greedy algorithms for robust, near-minimal reconstruction guarantees.
result Demonstrated on two problems where linear techniques fail, secant-based approach provides robust solutions.
CNNs classify human activities from IMU data.
problem Automatic identification of physical activities using motion sensors.
method Used Convolutional Neural Networks (CNNs) with raw IMU data.
result CNNs perform well in classifying 16 lower-limb activities.
Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when em…
Proposes privacy-preserving sensor data transformations to prevent user re-identification and sensitive activity inference.
problem Privacy threats from shared sensor data and potential user re-identification.
method Mechanisms to transform sensor data to eliminate patterns for re-identification and sensitive activity inference, while maintaining minor utility loss.
result Reduced user re-identification accuracy to random guess level and prevented inference of sensitive activities.
MEx dataset benchmarks HAR and multi-modal fusion for exercise quality.
problem Recognizing and evaluating exercise quality for Musculoskeletal Disorders patients.
method Multi-sensor, multi-modal dataset with four sensors (pressure mat, depth camera, accelerometers) for HAR and exercise quality assessment.
result Reference performance for each sensor identified, exposing their strengths and weaknesses.
ConvGNP improves sensor placement for climate monitoring.
problem Maximizing informativeness of environmental sensor placements in remote regions.
method Convolutional Gaussian neural processes (ConvGNP) for non-stationary spatial predictions.
result ConvGNP outperforms traditional GP models in predicting sensor performance and reducing uncertainty.
Deep learning improves sensor performance optimization.
problem Optimizing sensor performance based on key metrics.
method Re-approach non-linear regression using deep learning with Keras and Tensorflow.
result Deep learning models improve sensor performance optimization.
The behaviors of patients with depression are usually difficult to predict because the patients demonstrate the symptoms of a depressive episode without a warning at unexpected times. The goal of this research is to build algorithms that detect signals of such unusual moments so that doctors can be proactive in approac…
In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements. Using a redundant wavelet decomposition on a graph constructed over the sensor locations, our algorithm is able to construct discriminative features that exploit the mutual information between the sensors…
Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovati…
Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hi…
TransFall uses transfer learning to improve activity recognition from mobile sensors.
problem Performance degradation due to platform and user movement differences.
method Two-tier data transformation, label estimation, and model generation layers.
result TransFall enhances activity recognition accuracy for new scenarios.
EmbraceNet fusion model for multi-sensor activity recognition.
problem Human activity recognition using multiple sensors is challenging.
method Independent processing of each sensor, feature fusion with EmbraceNet, post-processing, and additional processes.
result Improved performance in SHL recognition challenge.
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
problem Inaccurate AQI data from sparse sensors in developing countries.
method Spatio-temporal GNNs for fine-grained AQI forecasting.
result Significant improvement in AQI prediction accuracy (71.654 MSE reduction).
Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutiona…
As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In thi…
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robus…