4,000 research outputs found
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders
Machine Learning (ML) has been applied to enable many life-assisting
appli-cations, such as abnormality detection and emdergency request for the
soli-tary elderly. However, in most cases machine learning algorithms depend on
the layout of the target Internet of Things (IoT) sensor network. Hence, to
deploy an application across Heterogeneous Sensor Networks (HSNs), i.e. sensor
networks with different sensors type or layouts, it is required to repeat the
process of data collection and ML algorithm training. In this paper, we
introduce a novel framework leveraging deep learning for graphs to enable using
the same activity recognition system across HSNs deployed in differ-ent smart
homes. Using our framework, we were able to transfer activity classifiers
trained with activity labels on a source HSN to a target HSN, reaching about
75% of the baseline accuracy on the target HSN without us-ing target activity
labels. Moreover, our model can quickly adapt to unseen sensor layouts, which
makes it highly suitable for the gradual deployment of real-world ML-based
applications. In addition, we show that our framework is resilient to
suboptimal graph representations of HSNs
NILM techniques for intelligent home energy management and ambient assisted living: a review
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora:
Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve
01/SAICT/2018/39578
Fundação para a Ciência e Tecnologia through IDMEC, under LAETA:
SFRH/BSAB/142998/2018
SFRH/BSAB/142997/2018
UID/EMS/50022/2019
Junta de Comunidades de Castilla-La-Mancha, Spain:
SBPLY/17/180501/000392
Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project):
TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Using supervised machine learning approaches to recognize human activities
from on-body wearable accelerometers generally requires a large amount of
labelled data. When ground truth information is not available, too expensive,
time consuming or difficult to collect, one has to rely on unsupervised
approaches. This paper presents a new unsupervised approach for human activity
recognition from raw acceleration data measured using inertial wearable
sensors. The proposed method is based upon joint segmentation of
multidimensional time series using a Hidden Markov Model (HMM) in a multiple
regression context. The model is learned in an unsupervised framework using the
Expectation-Maximization (EM) algorithm where no activity labels are needed.
The proposed method takes into account the sequential appearance of the data.
It is therefore adapted for the temporal acceleration data to accurately detect
the activities. It allows both segmentation and classification of the human
activities. Experimental results are provided to demonstrate the efficiency of
the proposed approach with respect to standard supervised and unsupervised
classification approache
A Survey on Multi-Resident Activity Recognition in Smart Environments
Human activity recognition (HAR) is a rapidly growing field that utilizes
smart devices, sensors, and algorithms to automatically classify and identify
the actions of individuals within a given environment. These systems have a
wide range of applications, including assisting with caring tasks, increasing
security, and improving energy efficiency. However, there are several
challenges that must be addressed in order to effectively utilize HAR systems
in multi-resident environments. One of the key challenges is accurately
associating sensor observations with the identities of the individuals
involved, which can be particularly difficult when residents are engaging in
complex and collaborative activities. This paper provides a brief overview of
the design and implementation of HAR systems, including a summary of the
various data collection devices and approaches used for human activity
identification. It also reviews previous research on the use of these systems
in multi-resident environments and offers conclusions on the current state of
the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and
Computing Systems (EICCS) Book Serie
A Modified KNN Algorithm for Activity Recognition in Smart Home
Nowadays, more and more elderly people cannot take care of themselves, and feel uncomfortable in daily activities. Smart home systems can help to improve daily life of elderly people. A smart home can bring residents a more comfortable living environment by recognizing the daily activities automatically. In this paper, in order to improve the accuracy of activity recognition in smart homes, we conduct some improvements in data preprocess and recognition phase, and more importantly, a novel sensor segmentation method and a modified KNN algorithm are proposed. The segmentation algorithm employs segment sensor data into fragments based on predefined activity knowledge, and then the proposed modified KNN algorithm uses center distances as a measure for classification. We also conduct comprehensive experiments, and the results demonstrate that the proposed method outperforms the other classifiers
GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces
We introduce GeXSe (Generative Explanatory Sensor System), a novel framework
designed to extract interpretable sensor-based and vision domain features from
non-invasive smart space sensors. We combine these to provide a comprehensive
explanation of sensor-activation patterns in activity recognition tasks. This
system leverages advanced machine learning architectures, including transformer
blocks, Fast Fourier Convolution (FFC), and diffusion models, to provide a more
detailed understanding of sensor-based human activity data. A standout feature
of GeXSe is our unique Multi-Layer Perceptron (MLP) with linear, ReLU, and
normalization layers, specially devised for optimal performance on small
datasets. It also yields meaningful activation maps to explain sensor-based
activation patterns. The standard approach is based on a CNN model, which our
MLP model outperforms.GeXSe offers two types of explanations: sensor-based
activation maps and visual domain explanations using short videos. These
methods offer a comprehensive interpretation of the output from
non-interpretable sensor data, thereby augmenting the interpretability of our
model. Utilizing the Frechet Inception Distance (FID) for evaluation, it
outperforms established methods, improving baseline performance by about 6\%.
GeXSe also achieves a high F1 score of up to 0.85, demonstrating precision,
recall, and noise resistance, marking significant progress in reliable and
explainable smart space sensing systems.Comment: 29 pages,17 figure
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