Abstract:As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human−machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybridflexible wearable system, consi… Show more
“…However, they become inefficient or incapable when datasets become large, , complex, , non-linear, high-dimensional, erroneous, , or with unclear correlations . Therefore, more powerful algorithms, predominantly machine learning (ML) methods have been proposed to overcome these challenges and shown promise in flexible sensor applications, such as sign language recognition, ,, electronic skins, ,, human-machine interfaces, and biosensing. , Besides handling challenging data, ML can also be used to compensate for sensor performance deficiency, such as signal noise, , drift, and limited range of detection . Additionally, ML allows for the fusion of multiple types of data for more accurate ,, and/or more insightful analyses .…”
Humans rely increasingly on sensors to address grand
challenges
and to improve quality of life in the era of digitalization and big
data. For ubiquitous sensing, flexible sensors are developed to overcome
the limitations of conventional rigid counterparts. Despite rapid
advancement in bench-side research over the last decade, the market
adoption of flexible sensors remains limited. To ease and to expedite
their deployment, here, we identify bottlenecks hindering the maturation
of flexible sensors and propose promising solutions. We first analyze
challenges in achieving satisfactory sensing performance for real-world
applications and then summarize issues in compatible sensor-biology
interfaces, followed by brief discussions on powering and connecting
sensor networks. Issues en route to commercialization and for sustainable
growth of the sector are also analyzed, highlighting environmental
concerns and emphasizing nontechnical issues such as business, regulatory,
and ethical considerations. Additionally, we look at future intelligent
flexible sensors. In proposing a comprehensive roadmap, we hope to
steer research efforts towards common goals and to guide coordinated
development strategies from disparate communities. Through such collaborative
efforts, scientific breakthroughs can be made sooner and capitalized
for the betterment of humanity.
“…However, they become inefficient or incapable when datasets become large, , complex, , non-linear, high-dimensional, erroneous, , or with unclear correlations . Therefore, more powerful algorithms, predominantly machine learning (ML) methods have been proposed to overcome these challenges and shown promise in flexible sensor applications, such as sign language recognition, ,, electronic skins, ,, human-machine interfaces, and biosensing. , Besides handling challenging data, ML can also be used to compensate for sensor performance deficiency, such as signal noise, , drift, and limited range of detection . Additionally, ML allows for the fusion of multiple types of data for more accurate ,, and/or more insightful analyses .…”
Humans rely increasingly on sensors to address grand
challenges
and to improve quality of life in the era of digitalization and big
data. For ubiquitous sensing, flexible sensors are developed to overcome
the limitations of conventional rigid counterparts. Despite rapid
advancement in bench-side research over the last decade, the market
adoption of flexible sensors remains limited. To ease and to expedite
their deployment, here, we identify bottlenecks hindering the maturation
of flexible sensors and propose promising solutions. We first analyze
challenges in achieving satisfactory sensing performance for real-world
applications and then summarize issues in compatible sensor-biology
interfaces, followed by brief discussions on powering and connecting
sensor networks. Issues en route to commercialization and for sustainable
growth of the sector are also analyzed, highlighting environmental
concerns and emphasizing nontechnical issues such as business, regulatory,
and ethical considerations. Additionally, we look at future intelligent
flexible sensors. In proposing a comprehensive roadmap, we hope to
steer research efforts towards common goals and to guide coordinated
development strategies from disparate communities. Through such collaborative
efforts, scientific breakthroughs can be made sooner and capitalized
for the betterment of humanity.
“…We used a micropipette to deposit liquid metal droplets (volume of ∼4 μL) with a diameter of less than 1 mm. In a crossbar structure integration for instance, the electrodes can be formed by utilizing approaches such as (1) printing , or (2) microfluidics channel, similar to that used in reports of flexible electronics fabrication. A schematic of the fabricated device sample is illustrated in Figure S12(c).…”
Herein, we report a solution-processable memristive device based on bismuth vanadate (BiVO 4 ) and titanium dioxide (TiO 2 ) with gallium-based eutectic gallium−indium (EGaIn) and gallium−indium-tin alloy (GaInSn) liquid metal as the top electrode. Scanning electron microscopy (SEM) shows the formation of a nonporous structure of BiVO 4 and TiO 2 for efficient resistive switching. Additionally, the gallium-based liquid metal (GLM)contacted memristors exhibit stable memristor behavior over a wide temperature range from −10 to +90 °C. Gallium atoms in the liquid metal play an important role in the conductive filament formation as well as the device's operation stability as elucidated by I−V characteristics. The synaptic behavior of the GLM-memristors was characterized, with excellent long-term potentiation (LTP) and longterm depression (LTD) linearity. Using the performance of our device in a multilayer perceptron (MLP) network, a ∼90% accuracy in the handwriting recognition of modified national institute of standards and technology database (MNIST) was achieved. Our findings pave a path for solution-processed/GLM-based memristors which can be used in neuromorphic applications on flexible substrates in a harsh environment.
“…Park et al fabricated 15 stretchable resistive strain sensors on finger joint regions and utilized ANN for translating hand sign language [ 176 ]. Thean et al developed 16 bimodal capacitance sensors distributed close to the joints of the human palm and integrated a LSTM network to achieve both static and dynamic hand gesture recognition [ 193 ]. Fig.…”
Section: Ml-assisted Data Interpretationmentioning
To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.
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