26,625 research outputs found
Inferring Room Semantics Using Acoustic Monitoring
Having knowledge of the environmental context of the user i.e. the knowledge
of the users' indoor location and the semantics of their environment, can
facilitate the development of many of location-aware applications. In this
paper, we propose an acoustic monitoring technique that infers semantic
knowledge about an indoor space \emph{over time,} using audio recordings from
it. Our technique uses the impulse response of these spaces as well as the
ambient sounds produced in them in order to determine a semantic label for
them. As we process more recordings, we update our \emph{confidence} in the
assigned label. We evaluate our technique on a dataset of single-speaker human
speech recordings obtained in different types of rooms at three university
buildings. In our evaluation, the confidence\emph{ }for the true label
generally outstripped the confidence for all other labels and in some cases
converged to 100\% with less than 30 samples.Comment: 2017 IEEE International Workshop on Machine Learning for Signal
Processing, Sept.\ 25--28, 2017, Tokyo, Japa
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
Modeling an ontology on accessible evacuation routes for emergencies
Providing alert communication in emergency situations is vital to reduce the number of victims. However, this is a challenging goal for researchers and professionals due to the diverse pool of prospective users, e.g. people with disabilities as well as other vulnerable groups. Moreover, in the event of an emergency situation, many people could become vulnerable because of exceptional circumstances such as stress, an unknown environment or even visual impairment (e.g. fire causing smoke). Within this scope, a crucial activity is to notify affected people about safe places and available evacuation routes. In order to address this need, we propose to extend an ontology, called SEMA4A (Simple EMergency Alert 4 [for] All), developed in a previous work for managing knowledge about accessibility guidelines, emergency situations and communication technologies. In this paper, we introduce a semi-automatic technique for knowledge acquisition and modeling on accessible evacuation routes. We introduce a use case to show applications of the ontology and conclude with an evaluation involving several experts in evacuation procedures. © 2014 Elsevier Ltd. All rights reserved
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Optimizing Radiant Systems for Energy Efficiency and Comfort
Radiant cooling and heating systems provide an opportunity to achieve significant energy savings, peak demand reduction, load shifting, and thermal comfort improvements compared to conventional all-air systems. As a result, application of these systems has increased in recent years, particularly in zero-net-energy (ZNE) and other advanced low-energy buildings. Despite this growth, completed installations to date have demonstrated that controls and operation of radiant systems can be challenging due to a lack of familiarity within the heating, ventilation, and air-conditioning (HVAC) design and operations professions, often involving new concepts (particularly related to the slow response in high thermal mass radiant systems). To achieve the significant reductions in building energy use proposed by California Public Utilities Commission’s (CPUC’s) Energy Efficiency Strategic Plan that all new non-residential buildings be ZNE by 2030, it is critical that new technologies that will play a major role in reaching this goal be applied in an effective manner. This final report describes the results of a comprehensive multi-faceted research project that was undertaken to address these needed enhancements to radiant technology by developing the following: (1) sizing and operation tools (currently unavailable on the market) to provide reliable methods to take full advantage of the radiant systems to provide improved energy performance while maintaining comfortable conditions, (2) energy, cost, and occupant comfort data to provide real world examples of energy efficient, affordable, and comfortable buildings using radiant systems, and (3) Title-24 and ASHRAE Standards advancements to enhance the building industry’s ability to achieve significant energy efficiency goals in California with radiant systems. The research team used a combination of full-scale fundamental laboratory experiments, whole-building energy simulations and simplified tool development, and detailed field studies and control demonstrations to assemble the new information, guidance and tools necessary to help the building industry achieve significant energy efficiency goals for radiant systems in California
Deep Room Recognition Using Inaudible Echos
Recent years have seen the increasing need of location awareness by mobile
applications. This paper presents a room-level indoor localization approach
based on the measured room's echos in response to a two-millisecond single-tone
inaudible chirp emitted by a smartphone's loudspeaker. Different from other
acoustics-based room recognition systems that record full-spectrum audio for up
to ten seconds, our approach records audio in a narrow inaudible band for 0.1
seconds only to preserve the user's privacy. However, the short-time and
narrowband audio signal carries limited information about the room's
characteristics, presenting challenges to accurate room recognition. This paper
applies deep learning to effectively capture the subtle fingerprints in the
rooms' acoustic responses. Our extensive experiments show that a two-layer
convolutional neural network fed with the spectrogram of the inaudible echos
achieve the best performance, compared with alternative designs using other raw
data formats and deep models. Based on this result, we design a RoomRecognize
cloud service and its mobile client library that enable the mobile application
developers to readily implement the room recognition functionality without
resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
the state-of-the-art approaches based on support vector machine, RoomRecognize
significantly improves the Pareto frontier of recognition accuracy versus
robustness against interfering sounds (e.g., ambient music).Comment: 29 page
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
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