9,026 research outputs found
Human activity recognition using tag-based radio frequency localization
This article provides a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization. A publicly available dataset is used where the position data of multiple RF tags worn on different parts of the human body are acquired asynchronously and nonuniformly. In this study, curves fitted to the data are resampled uniformly and then segmented. We investigate the effect on system accuracy of varying the relevant system parameters. We compare various curve-fitting, segmentation, and classification techniques and present the combination resulting in the best performance. The classifiers are validated using 5-fold and subject-based leave-one-out cross validation, and for the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30%, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%, respectively. The results indicate that the system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. © 2016 Taylor & Francis
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
Human Sensing via Passive Spectrum Monitoring
Human sensing is significantly improving our lifestyle in many fields such as
elderly healthcare and public safety. Research has demonstrated that human
activity can alter the passive radio frequency (PRF) spectrum, which represents
the passive reception of RF signals in the surrounding environment without
actively transmitting a target signal. This paper proposes a novel passive
human sensing method that utilizes PRF spectrum alteration as a biometrics
modality for human authentication, localization, and activity recognition. The
proposed method uses software-defined radio (SDR) technology to acquire the PRF
in the frequency band sensitive to human signature. Additionally, the PRF
spectrum signatures are classified and regressed by five machine learning (ML)
algorithms based on different human sensing tasks. The proposed Sensing Humans
among Passive Radio Frequency (SHAPR) method was tested in several environments
and scenarios, including a laboratory, a living room, a classroom, and a
vehicle, to verify its extensiveness. The experimental results show that the
SHAPR method achieved more than 95% accuracy in the four scenarios for the
three human sensing tasks, with a localization error of less than 0.8 m. These
results indicate that the SHAPR technique can be considered a new human
signature modality with high accuracy, robustness, and general applicability
Location estimation in smart homes setting with RFID systems
Indoor localisation technologies are a core component of Smart Homes. Many applications within Smart Homes benefit from localisation technologies to determine the locations of things, objects and people. The tremendous characteristics of the Radio Frequency Identification (RFID) systems have become one of the enabler technologies in the Internet of Things (IOT) that connect objects and things wirelessly. RFID is a promising technology in indoor positioning that not only uniquely identifies entities but also locates affixed RFID tags on objects or subjects in stationary and real-time. The rapid advancement in RFID-based systems has sparked the interest of researchers in Smart Homes to employ RFID technologies and potentials to assist with optimising (non-) pervasive healthcare systems in automated homes.
In this research localisation techniques and enabled positioning sensors are investigated. Passive RFID sensors are used to localise passive tags that are affixed to Smart Home objects and track the movement of individuals in stationary and real-time settings. In this study, we develop an affordable passive localisation platform using inexpensive passive RFID sensors. To fillful this aim, a passive localisation framework using minimum tracking resources (RFID sensors) has been designed. A localisation prototype and localisation application that examined the affixed RFID tag on objects to evaluate our proposed locaisation framework was then developed. Localising algorithms were utilised to achieve enhanced accuracy of localising one particular passive tag which that affixed to target objects.
This thesis uses a general enough approach so that it could be applied more widely to other applications in addition to Health Smart Homes. A passive RFID localising framework is designed and developed through systematic procedures. A localising platform is built to test the proposed framework, along with developing a RFID tracking application using Java programming language and further data analysis in MATLAB. This project applies localisation procedures and evaluates them experimentally. The experimental study positively confirms that our proposed localisation framework is capable of enhancing the accuracy of the location of the tracked individual. The low-cost design uses only one passive RFID target tag, one RFID reader and three to four antennas
Sensing motion using spectral and spatial analysis of WLAN RSSI
In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis
confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of
WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements
in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same
data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the
state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support
Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment
In the last decade, integrated logistics has become an important challenge in
the development of wireless communication, identification and sensing
technology, due to the growing complexity of logistics processes and the
increasing demand for adapting systems to new requirements. The advancement of
wireless technology provides a wide range of options for the maritime container
terminals. Electronic devices employed in container terminals reduce the manual
effort, facilitating timely information flow and enhancing control and quality
of service and decision made. In this paper, we examine the technology that can
be used to support integration in harbor's logistics. In the literature, most
systems have been developed to address specific needs of particular harbors,
but a systematic study is missing. The purpose is to provide an overview to the
reader about which technology of integrated logistics can be implemented and
what remains to be addressed in the future
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