8,184 research outputs found
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
A unified methodology for heartbeats detection in seismocardiogram and ballistocardiogram signals
This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets (p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found (p < 0.01) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors
Sensor-based datasets for human activity recognition - a systematic review of literature
The research area of ambient assisted living has led to the development of activity recognition
systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and
the health care of the elderly and dependent people. However, before making them available to end users, it is
necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks
in experimental scenarios. For that reason, the scientific community has developed and provided a huge
amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which
techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and
is key to further progress in this area of research. This work presents a systematic review of the literature
of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables
taken from indexed publications related to this field was performed. The sources of information are journals,
proceedings, and books located in specialized databases. The analyzed variables characterize publications
by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed
identification of the data set most used by researchers. On the other hand, the descriptive and functional
variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation,
representation, feature selection, balancing and addition of instances, and classifier used for recognition.
This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most
appropriate dataset to evaluate ARS and the classification techniques that generate better results
Sensor-based datasets for human activity recognition - a systematic review of literature
The research area of ambient assisted living has led to the development of activity recognition
systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and
the health care of the elderly and dependent people. However, before making them available to end users, it is
necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks
in experimental scenarios. For that reason, the scientific community has developed and provided a huge
amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which
techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and
is key to further progress in this area of research. This work presents a systematic review of the literature
of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables
taken from indexed publications related to this field was performed. The sources of information are journals,
proceedings, and books located in specialized databases. The analyzed variables characterize publications
by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed
identification of the data set most used by researchers. On the other hand, the descriptive and functional
variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation,
representation, feature selection, balancing and addition of instances, and classifier used for recognition.
This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most
appropriate dataset to evaluate ARS and the classification techniques that generate better results
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