107 research outputs found
Human Body Digital Twin: A Master Plan
The human body DT has the potential to revolutionize healthcare and wellness,
but its responsible and effective implementation requires consideration of
various factors. This article presents a comprehensive overview of the current
status and future prospects of the human body DT and proposes a five-level
roadmap for its development. The roadmap covers the development of various
components, such as wearable devices, data collection, data analysis, and
decision-making systems. The article also highlights the necessary support,
security, cost, and ethical considerations that must be addressed in order to
ensure responsible and effective implementation of the human body DT. The
proposed roadmap provides a framework for guiding future development and offers
a unique perspective on the future of the human body DT, facilitating new
interdisciplinary research and innovative solutions in this rapidly evolving
field.Comment: 3 figure
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Machine learning methods for automatic silent speech recognition using a wearable graphene strain gauge sensor
Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising grapheneâs unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.EP/S023046/
Intelligent machines work in unstructured environments by differential neuromorphic computing
Efficient operation of intelligent machines in the real world requires
methods that allow them to understand and predict the uncertainties presented
by the unstructured environments with good accuracy, scalability and
generalization, similar to humans. Current methods rely on pretrained networks
instead of continuously learning from the dynamic signal properties of working
environments and suffer inherent limitations, such as data-hungry procedures,
and limited generalization capabilities. Herein, we present a memristor-based
differential neuromorphic computing, perceptual signal processing and learning
method for intelligent machines. The main features of environmental information
such as amplification (>720%) and adaptation (<50%) of mechanical stimuli
encoded in memristors, are extracted to obtain human-like processing in
unstructured environments. The developed method takes advantage of the
intrinsic multi-state property of memristors and exhibits good scalability and
generalization, as confirmed by validation in two different application
scenarios: object grasping and autonomous driving. In the former, a robot hand
experimentally realizes safe and stable grasping through fast learning (in ~1
ms) the unknown object features (e.g., sharp corner and smooth surface) with a
single memristor. In the latter, the decision-making information of 10
unstructured environments in autonomous driving (e.g., overtaking cars,
pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By
mimicking the intrinsic nature of human low-level perception mechanisms, the
electronic memristive neuromorphic circuit-based method, presented here shows
the potential for adapting to diverse sensing technologies and helping
intelligent machines generate smart high-level decisions in the real world.Comment: 16 pages, 5 figure
Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor.
Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene's unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.EP/S023046/
Emerging Indoor Photovoltaic Technologies for Sustainable Internet of Things
Funder: Priority Academic Program Development of Jiangsu Higher Education Institutions; Id: http://dx.doi.org/10.13039/501100012246Funder: 111 Project; Id: http://dx.doi.org/10.13039/501100013314Funder: Joint International Research Laboratory of CarbonâBased Functional Materials and DevicesFunder: European Union; Id: http://dx.doi.org/10.13039/501100000780Abstract: The Internet of Things (IoT) provides everyday objects and environments with âintelligenceâ and data connectivity to improve quality of life and the efficiency of a wide range of human activities. However, the ongoing exponential growth of the IoT device ecosystemâup to tens of billions of units to dateâposes a challenge regarding how to power such devices. This Progress Report discusses how energy harvesting can address this challenge. It then discusses how indoor photovoltaics (IPV) constitutes an attractive energy harvesting solution, given its deployability, reliability, and power density. For IPV to provide an ecoâfriendly route to powering IoT devices, it is crucial that its underlying materials and fabrication processes are lowâtoxicity and not harmful to the environment over the product life cycle. A range of IPV technologiesâboth incumbent and emergingâdeveloped to date is discussed, with an emphasis on their environmental sustainability. Finally, IPV based on emerging leadâfree perovskiteâinspired absorbers are examined, highlighting their status and prospects for lowâcost, durable, and efficient energy harvesting that is not harmful to the end user and environment. By examining emerging avenues for ecoâfriendly IPV, timely insight is provided into promising directions toward IPV that can sustainably power the IoT revolution
SAT: a methodology to assess the social acceptance of innovative AI-based technologies
Purpose
The purpose of this paper is to present the conceptual model of an innovative methodology (SAT) to assess the social acceptance of technology, especially focusing on artificial intelligence (AI)-based technology.
Design/methodology/approach
After a review of the literature, this paper presents the main lines by which SAT stands out from current methods, namely, a four-bubble approach and a mix of qualitative and quantitative techniques that offer assessments that look at technology as a socio-technical system. Each bubble determines the social variability of a cluster of values: User-Experience Acceptance, Social Disruptiveness, Value Impact and Trust.
Findings
The methodology is still in development, requiring further developments, specifications and validation. Accordingly, the findings of this paper refer to the realm of the research discussion, that is, highlighting the importance of preventively assessing and forecasting the acceptance of technology and building the best design strategies to boost sustainable and ethical technology adoption.
Social implications
Once SAT method will be validated, it could constitute a useful tool, with societal implications, for helping users, markets and institutions to appraise and determine the co-implications of technology and socio-cultural contexts.
Originality/value
New AI applications flood todayâs users and markets, often without a clear understanding of risks and impacts. In the European context, regulations (EU AI Act) and rules (EU Ethics Guidelines for Trustworthy) try to fill this normative gap. The SAT method seeks to integrate the risk-based assessment of AI with an assessment of the perceptive-psychological and socio-behavioural aspects of its social acceptability
Double-Framed Thin Elastomer Devices
Elastomers and, in particular, polydimethylsiloxane (PDMS) are widely adopted as biocompatible mechanically compliant substrates for soft and flexible micro-nanosystems in medicine, biology, and engineering. However, several applications require such low thicknesses (e.g., <100 ÎŒm) that make peeling-off critical because very thin elastomers become delicate and tend to exhibit strong adhesion with carriers. Moreover, microfabrication techniques such as photolithography use solvents which swell PDMS, introducing complexity and possible contamination, thus limiting industrial scalability and preventing many biomedical applications. Here, we combine low-adhesion and rectangular carrier substrates, adhesive Kapton frames, micromilling-defined shadow masks, and adhesive-neutralizing paper frames for enabling fast, easy, green, contaminant-free, and scalable manufacturing of thin elastomer devices, with both simplified peeling and handling. The accurate alignment between the frame and shadow masks can be further facilitated by micromilled marking lines on the back side of the low-adhesion carrier. As a proof of concept, we show epidermal sensors on a 50 ÎŒm-thick PDMS substrate for measuring strain, the skin bioimpedance and the heart rate. The proposed approach paves the way to a straightforward, green, and scalable fabrication of contaminant-free thin devices on elastomers for a wide variety of applications.Elastomers and, in particular, polydimethylsiloxane (PDMS) are widely adopted as biocompatible mechanically compliant substrates for soft and flexible micro-nanosystems in medicine, biology, and engineering. However, several applications require such low thicknesses (e.g., <100 ÎŒm) that make peeling-off critical because very thin elastomers become delicate and tend to exhibit strong adhesion with carriers. Moreover, microfabrication techniques such as photolithography use solvents which swell PDMS, introducing complexity and possible contamination, thus limiting industrial scalability and preventing many biomedical applications. Here, we combine low-adhesion and rectangular carrier substrates, adhesive Kapton frames, micromilling-defined shadow masks, and adhesive-neutralizing paper frames for enabling fast, easy, green, contaminant-free, and scalable manufacturing of thin elastomer devices, with both simplified peeling and handling. The accurate alignment between the frame and shadow masks can be further facilitated by micromilled marking lines on the back side of the low-adhesion carrier. As a proof of concept, we show epidermal sensors on a 50 ÎŒm-thick PDMS substrate for measuring strain, the skin bioimpedance and the heart rate. The proposed approach paves the way to a straightforward, green, and scalable fabrication of contaminant-free thin devices on elastomers for a wide variety of applications
Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments
Formaldehyde, a known human carcinogen, is a common indoor air pollutant.
However, its real-time and selective recognition from interfering gases remains
challenging, especially for low-power sensors suffering from noise and baseline
drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor
for highly sensitive and real-time recognition of formaldehyde at room
temperature. By optimising the morphology and doping of the printed structures,
we achieve a record-high response of 15.23 percent for 1 parts-per-million
formaldehyde and an ultralow detection limit of 8.02 parts-per-billion
consuming only 130 uW power. Based on measured dynamic response snapshots, we
also develop an intelligent computational algorithm for robust and accurate
detection in real time despite simulated substantial noise and baseline drift,
hitherto unachievable for room-temperature sensors. Our framework in combining
materials engineering, structural design and computational algorithm to capture
dynamic response offers unprecedented real-time identification capabilities of
formaldehyde and other volatile organic compounds at room temperature.Comment: Main manuscript: 21 pages, 5 figure. Supplementary: 21 pages. 13
Figures, 2 tabl
Nanofabrication of Conductive Metallic Structures on Elastomeric Materials.
Existing techniques for patterning metallic structures on elastomers are limited in terms of resolution, yield and scalability. The primary constraint is the incompatibility of their physical properties with conventional cleanroom techniques. We demonstrate a reliable fabrication strategy to transfer high resolution metallic structures of <500ânm in dimension on elastomers. The proposed method consists of producing a metallic pattern using conventional lithographic techniques on silicon coated with a thin sacrificial aluminium layer. Subsequent wet etching of the sacrificial layer releases the elastomer with the embedded metallic pattern. Using this method, a nano-resistor with minimum feature size of 400ânm is fabricated on polydimethylsiloxane (PDMS) and applied in gas sensing. Adsorption of solvents in the PDMS causes swelling and increases the device resistance, which therefore enables the detection of volatile organic compounds (VOCs). Sensitivity to chloroform and toluene vapor with a rapid response (~30âs) and recovery (~200âs) is demonstrated using this PDMS nano-resistor at room temperature
Nano-to-microporous networks via inkjet printing of ZnO nanoparticles/graphene hybrid for ultraviolet photodetectors
Inkjet-printed photodetectors have gained enormous attention over the past decade. However, device performance is limited without postprocessing, such as annealing and UV exposure. In addition, it is difficult to manipulate the surface morphology of the printed film using an inkjet printer because of the limited options of low viscosity ink solutions. Here, we employ a concept involving the control of the inkjet-printed film morphology via modulation of cosolvent vapor pressure and surface tension for the creation of a high-performance ZnO-based photodetector on a flexible substrate. The solvent boiling point across different cosolvent systems is found to affect the film morphology, which results in not only distinct photoresponse time but also photodetectivity. ZnO-based photodetectors were printed using different solvents, which display a fast photoresponse in low-boiling point solvents because of the low carbon residue and larger photodetectivity in high-boiling point solvent systems due to the porous structure. The porous structure is obtained using both gasâliquid surface tension differences and solidâliquid surface differences, and the size of porosity is modulated from nanosize to microsize depending on the ratio between two solvents or two nanomaterials. Moreover, the conductive nature of graphene enhances the transport behavior of the photocarrier, which enables a high-performance photodetector with high photoresponsivity (7.5 Ă 102AWâ1) and fast photoresponse (0.18 s) to be achieved without the use of high-boiling point solvents
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