1,637 research outputs found
Sensory augmentation with distal touch: The tactile helmet project
The Tactile Helmet is designed to augment a wearer's senses with a long range sense of touch. Tactile specialist animals such as rats and mice are capable of rapidly acquiring detailed information about their environment from their whiskers by using task-sensitive strategies. Providing similar information about the nearby environment, in tactile form, to a human operator could prove invaluable for search and rescue operations, or for partially-sighted people. Two key aspects of the Tactile Helmet are sensory augmentation, and active sensing. A haptic display is used to provide the user with ultrasonic range information. This can be interpreted in addition to, rather than instead of, visual or auditory information. Active sensing systems "are purposive and information-seeking sensory systems, involving task specific control of the sensory apparatus" [1]. The integration of an accelerometer allows the device to actively gate the delivery of sensory information to the user, depending on their movement. Here we describe the hardware, sensory transduction and characterisation of the Tactile Helmet device, before outlining potential use cases and benefits of the system. © 2013 Springer-Verlag Berlin Heidelberg
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors
Classifying brain signals collected by wearable Internet of Things (IoT)
sensors, especially brain-computer interfaces (BCIs), is one of the
fastest-growing areas of research. However, research has mostly ignored the
secure storage and privacy protection issues of collected personal
neurophysiological data. Therefore, in this article, we try to bridge this gap
and propose a secure privacy-preserving protocol for implementing BCI
applications. We first transformed brain signals into images and used
generative adversarial network to generate synthetic signals to protect data
privacy. Subsequently, we applied the paradigm of transfer learning for signal
classification. The proposed method was evaluated by a case study and results
indicate that real electroencephalogram data augmented with artificially
generated samples provide superior classification performance. In addition, we
proposed a blockchain-based scheme and developed a prototype on Ethereum, which
aims to make storing, querying and sharing personal neurophysiological data and
analysis reports secure and privacy-aware. The rights of three main transaction
bodies - construction workers, BCI service providers and project managers - are
described and the advantages of the proposed system are discussed. We believe
this paper provides a well-rounded solution to safeguard private data against
cyber-attacks, level the playing field for BCI application developers, and to
the end improve professional well-being in the industry
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
The vast proliferation of sensor devices and Internet of Things enables the
applications of sensor-based activity recognition. However, there exist
substantial challenges that could influence the performance of the recognition
system in practical scenarios. Recently, as deep learning has demonstrated its
effectiveness in many areas, plenty of deep methods have been investigated to
address the challenges in activity recognition. In this study, we present a
survey of the state-of-the-art deep learning methods for sensor-based human
activity recognition. We first introduce the multi-modality of the sensory data
and provide information for public datasets that can be used for evaluation in
different challenge tasks. We then propose a new taxonomy to structure the deep
methods by challenges. Challenges and challenge-related deep methods are
summarized and analyzed to form an overview of the current research progress.
At the end of this work, we discuss the open issues and provide some insights
for future directions
Physical Diagnosis and Rehabilitation Technologies
The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices
Facilitating Internet of Things on the Edge
The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment.
Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management.
The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved
Epidermal systems and virtual reality: Emerging disruptive technology for military applications
This review study, presented at the 2nd World Conference on Advanced Materials for Defense (AuxDefense 2020), focuses on skin as sensory interface and explores the latest discoveries in bioelectronic science. The work analyzes at what extent invisibility is possible by emulating nature, and if military applications can really benefit from technology that combines epidermal systems and virtual reality — and from next generation of wearable textile computing technologies.info:eu-repo/semantics/acceptedVersio
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