Bioinspired Co‐Design of Tactile Sensor and Deep Learning Algorithm for Human–Robot Interaction

Kong, Depeng; Yang, Geng; Pang, Gaoyang; Ye, Zhiqiu; Lv, Honghao; Zhao, Yu; Wang, Fei; Wang, Xi Vincent; Xu, Kaichen; Yang, Huayong

doi:10.1002/aisy.202200050

Cited by 19 publications

(26 citation statements)

References 46 publications

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“…In the past decade, burgeoning functional materials and advanced manufacturing techniques have accelerated the development of a variety of skin-like sensors, which are typically categorized into flexible physical, chemical and electrophysiological sensors [ 13 – 22 ]. These flexible and lightweight devices contribute to bridging the human, machines and environments [ 23 – 28 ]. In general, conventional flexible sensors rely on signals’ detection in direct contact between the device and targets of interest, such as tactile sensors, strain sensors and most chemical sensors.…”

Section: Introductionmentioning

confidence: 99%

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

et al. 2022

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In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human–machine interfaces, artificial intelligence and soft robotics. Among them, flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change. This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring. Four categories of humidity sensors are highlighted based on resistive, capacitive, impedance-type and voltage-type working mechanisms. Furthermore, typical strategies including chemical doping, structural design and Joule heating are introduced to enhance the performance of humidity sensors. Drawing on the noncontact perception capability, human/plant healthcare management, human–machine interactions as well as integrated humidity sensor-based feedback systems are presented. The burgeoning innovations in this research field will benefit human society, especially during the COVID-19 epidemic, where cross-infection should be averted and contactless sensation is highly desired.

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Section: Introductionmentioning

confidence: 99%

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

et al. 2022

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“…[36] In this work, we use deep static networks for drift and hysteresis compensation. In addition, these learning-based approaches are used to model multi-sensor electronic skins, [32] multielectrode piezoresistive sensors [37] and electrical impedance tomography (EIT) sensors, [38] having a complex relationship between their resistances/impedances and location of touch.…”

Section: Introductionmentioning

confidence: 99%

Learning‐Based Damage Recovery for Healable Soft Electronic Skins

Terryn

Hardman

Thuruthel

et al. 2022

Advanced Intelligent Systems

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Natural agents display various adaptation strategies to damages, including damage assessment, localization, healing, and recalibration. This work investigates strategies by which a soft electronic skin can similarly preserve its sensitivity after multiple damages, combining material-level healing with software-level adaptation. Being manufactured entirely from selfhealing Diels-Alder matrix and composite fibers, the skin is capable of physically recovering from macroscopic damages. However, the simultaneous shifts in sensor fiber signals cannot be modelled using analytical approaches, since the materials viscoelasticity and healing processes introduce significant nonlinearities and time-variance into the skin's response. We show that machine learning of 5-layer networks after 5000 probes leads to highly sensitive models for touch localization with 2.3mm position and 95% depth accuracy. Through health monitoring via probing, damage and partial recovery are localized. Although healing is often successful, insufficient recontact leads to limited recovery or complete loss of a fiber. In these cases, complete resampling and retraining recovers the networks' full performance, regaining sensitivity and further increasing the system's robustness. Transfer learning with a single frozen layer provides the ability to rapidly adapt with fewer than 200 probes.

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“…With the deepening research in the field of Artificial Intelligence (AI), it creates higher requirements for flexible sensors [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Among all these sensors, pressure sensors have been widely concerned because of their wide application [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Wide-Range Flexible Capacitive Pressure Sensors Based on Dielectrics with Various Porosity

Cheng-xi

et al. 2022

Micromachines

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Wide-range flexible pressure sensors are in difficulty in research while in demand in application. In this paper, a wide-range capacitive flexible pressure sensor is developed with the foaming agent ammonium bicarbonate (NH4HCO3). By controlling the concentration of NH4HCO3 doped in the polydimethylsiloxane (PDMS) and repeating the curing process, pressure-sensitive dielectrics with various porosity are fabricated to expand the detection range of the capacitive pressure sensor. The shape and the size of each dielectric is defined by the 3D printed mold. To improve the dielectric property of the dielectric, a 1% weight ratio of multi-walled carbon nanotubes (MWCNTs) are doped into PDMS liquid. Besides that, a 5% weight ratio of MWCNTs is dispersed into deionized water and then coated on the electrodes to improve the contact state between copper electrodes and the dielectric. The laminated dielectric layer and two electrodes are assembled and tested. In order to verify the effectiveness of this design, some reference devices are prepared, such as sensors based on the dielectric with uniform porosity and a sensor with common copper electrodes. According to the testing results of these sensors, it can be seen that the sensor based on the dielectric with various porosity has higher sensitivity and a wider pressure detection range, which can detect the pressure range from 0 kPa to 1200 kPa and is extended to 300 kPa compared with the dielectric with uniform porosity. Finally, the sensor is applied to the fingerprint, finger joint, and knee bending test. The results show that the sensor has the potential to be applied to human motion detection.

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Bioinspired Co‐Design of Tactile Sensor and Deep Learning Algorithm for Human–Robot Interaction

Cited by 19 publications

References 46 publications

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

Learning‐Based Damage Recovery for Healable Soft Electronic Skins

Wide-Range Flexible Capacitive Pressure Sensors Based on Dielectrics with Various Porosity

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