4,339 research outputs found
Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data
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 hierarchical hidden Markov model to detect an activity on the stream
of sensor data which can predict the activity in the environment with any
sensor event. The activity recognition samples were labeled by the statistical
features such as the duration of activity. The results of our proposed method
test on two different datasets of smart homes in the real world showed that one
dataset has improved 4% and reached (59%) while the results reached 64.6% for
the other data by using the best methods
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
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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