644 research outputs found
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
Toward energy-efficient and trustworthy eHealth monitoring system
The rapid technological convergence between Internet of Things (IoT), Wireless Body Area Networks (WBANs) and cloud computing has made e-healthcare emerge as a promising application domain, which has significant potential to improve the quality of medical care. In particular, patient-centric health monitoring plays a vital role in e-healthcare service, involving a set of important operations ranging from medical data collection and aggregation, data transmission and segregation, to data analytics. This survey paper firstly presents an architectural framework to describe the entire monitoring life cycle and highlight the essential service components. More detailed discussions are then devoted to {em data collection} at patient side, which we argue that it serves as fundamental basis in achieving robust, efficient, and secure health monitoring. Subsequently, a profound discussion of the security threats targeting eHealth monitoring systems is presented, and the major limitations of the existing solutions are analyzed and extensively discussed. Finally, a set of design challenges is identified in order to achieve high quality and secure patient-centric monitoring schemes, along with some potential solutions
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
A 16-Channel Neural Recording System-on-Chip With CHT Feature Extraction Processor in 65-nm CMOS
Next-generation invasive neural interfaces require fully implantable wireless systems that can record from a large number of channels simultaneously. However, transferring the recorded data from the implant to an external receiver emerges as a significant challenge due to the high throughput. To address this challenge, this article presents a neural recording system-on-chip that achieves high resource and wireless bandwidth efficiency by employing on-chip feature extraction. Energy-area-efficient 10-bit 20-kS/s front end amplifies and digitizes the neural signals within the local field potential (LFP) and action potential (AP) bands. The raw data from each channel are decomposed into spectral features using a compressed Hadamard transform (CHT) processor. The selection of the features to be computed is tailored through a machine learning algorithm such that the overall data rate is reduced by 80% without compromising classification performance. Moreover, the CHT feature extractor allows waveform reconstruction on the receiver side for monitoring or additional post-processing. The proposed approach was validated through in vivo and off-line experiments. The prototype fabricated in 65-nm CMOS also includes wireless power and data receiver blocks to demonstrate the energy and area efficiency of the complete system. The overall signal chain consumes 2.6 μW and occupies 0.021 mm² per channel, pointing toward its feasibility for 1000-channel single-die neural recording systems
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G
Energy Efficient Heart Rate Sensing using a Painted Electrode ECG Wearable
© 2017 IEEE. Many countries are facing burdens on their health care systems due to ageing populations. A promising strategy to address the problem is to allow selected people to remain in their homes and be monitored using recent advances in wearable devices, saving in-hospital resources. With respect to heart monitoring, wearable devices to date have principally used optical techniques by shining light through the skin. However, these techniques are severely hampered by motion artifacts and are limited to heart rate detection. Further, these optical devices consume a large amount of power in order to receive a sufficient signal, resulting in the need for frequent battery recharging. To address these shortcomings we present a new wrist ECG wearable that is similar to the clinical approach for heart monitoring. Our device weighs less than 30 g, and is ultra low power, extending the battery lifetime to over a month to make the device more appropriate for in-home health care applications. The device uses two electrodes activated by the user to measure the voltage across the wrists. The electrodes are made from a flexible ink and can be painted on to the device casing, making it adaptable for different shapes and users. In this paper we show how the ECG sensor can be integrated into an existing IoT wearable and compare the device\u27s accuracy against other common commercial devices
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the
environment and thus imaging plays the pivotal role in mobile vision. Recent
explosive progresses in Artificial Intelligence (AI) have shown great potential
to develop advanced mobile platforms with new imaging devices. Traditional
imaging systems based on the "capturing images first and processing afterwards"
mechanism cannot meet this unprecedented demand. Differently, Computational
Imaging (CI) systems are designed to capture high-dimensional data in an
encoded manner to provide more information for mobile vision systems.Thanks to
AI, CI can now be used in real systems by integrating deep learning algorithms
into the mobile vision platform to achieve the closed loop of intelligent
acquisition, processing and decision making, thus leading to the next
revolution of mobile vision.Starting from the history of mobile vision using
digital cameras, this work first introduces the advances of CI in diverse
applications and then conducts a comprehensive review of current research
topics combining CI and AI. Motivated by the fact that most existing studies
only loosely connect CI and AI (usually using AI to improve the performance of
CI and only limited works have deeply connected them), in this work, we propose
a framework to deeply integrate CI and AI by using the example of self-driving
vehicles with high-speed communication, edge computing and traffic planning.
Finally, we outlook the future of CI plus AI by investigating new materials,
brain science and new computing techniques to shed light on new directions of
mobile vision systems
Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems
Objective: Diagnosis of sleep disorders is an expensive procedure that requires performing a sleep study, known as polysomnography (PSG), in a controlled environment. This study monitors the neural, eye and muscle activity of a patient using electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals which are then scored in to different sleep stages. Home PSG is often cited as an alternative of clinical PSG to make it more accessible, however it still requires patients to use a cumbersome system with multiple recording channels that need to be precisely placed. This thesis proposes a wearable sleep staging system using a single channel of EEG. For realisation of such a system, this thesis presents novel features for REM sleep detection from EEG (normally detected using EMG/EOG), a low-complexity automatic sleep staging algorithm using a single EEG channel and its complete integrated circuit implementation.
Methods: The difference between Spectral Edge Frequencies (SEF) at 95% and 50% in the 8-16 Hz frequency band is shown to have high discriminatory ability for detecting REM sleep stages. This feature, together with other spectral features from single-channel EEG are used with a set of decision trees controlled by a state machine for classification. The hardware for the complete algorithm is designed using low-power techniques and implemented on chip using 0.18μm process node technology.
Results: The use of SEF features from one channel of EEG resulted in 83% of REM sleep epochs being correctly detected. The automatic sleep staging algorithm, based on contextually aware decision trees, resulted in an accuracy of up to 79% on a large dataset. Its hardware implementation, which is also the very first complete circuit level implementation of any sleep staging algorithm, resulted in an accuracy of 98.7% with great potential for use in fully wearable sleep systems.Open Acces
New trends in 4D printing: A critical review
In a variety of industries, Additive Manufacturing has revolutionized the whole design-fabrication cycle. Traditional 3D printing is typically employed to produce static components, which are not able to fulfill the dynamic structures requirements and relevant applications such as soft grippers, self-assembly systems, and smart actuators. To address this limitation, an innovative technology has emerged and is called “4D printing”. It processes smart materials by using 3D printing for fabricating smart structures that can be reconfigured by applying different inputs such as heat, humidity, magnetic, electricity, light etc. At present, 4D printing is still a growing technology and it presents numerous challenges regarding materials, design, simulation, fabrication processes, applied strategies and reversibility. In this work a critical review about 4D printing technologies, materials and applications is discussed
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