skip to main content


Title: Deep-learning-assisted printed liquid metal sensory system for wearable applications and boxing training
Abstract

Liquid metal (LM) exhibits a distinct combination of high electrical conductivity comparable to that of metals and exceptional deformability derived from its liquid state, thus it is considered a promising material for high-performance soft electronics. However, rapid patterning LM to achieve a sensory system with high sensitivity remains a challenge, mainly attributed to the poor rheological property and wettability. Here, we report a rheological modification strategy of LM and strain redistribution mechanics to simultaneously simplify the scalable manufacturing process and significantly enhance the sensitivity of LM sensors. By incorporating SiO2particles into LM, the modulus, yield stress, and viscosity of the LM-SiO2composite are drastically enhanced, enabling 3D printability on soft materials for stretchable electronics. The sensors based on printed LM-SiO2composite show excellent mechanical flexibility, robustness, strain, and pressure sensing performances. Such sensors are integrated onto different locations of the human body for wearable applications. Furthermore, by integrating onto a tactile glove, the synergistic effect of strain and pressure sensing can decode the clenching posture and hitting strength in boxing training. When assisted by a deep-learning algorithm, this tactile glove can achieve recognition of the technical execution of boxing punches, such as jab, swing, uppercut, and combination punches, with 90.5% accuracy. This integrated multifunctional sensory system can find wide applications in smart sport-training, intelligent soft robotics, and human-machine interfaces.

 
more » « less
NSF-PAR ID:
10439613
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
npj Flexible Electronics
Volume:
7
Issue:
1
ISSN:
2397-4621
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Madden, John D. ; Anderson, Iain A. ; Shea, Herbert R. (Ed.)
    Ras Labs makes Synthetic Muscle™, which is a class of electroactive polymer (EAP) based materials and actuators that sense pressure (gentle touch to high impact), controllably contract and expand at low voltage (1.5 V to 50 V, including use of batteries), and attenuate force. We are in the robotics era, but robots do have their challenges. Currently, robotic sensing is mainly visual, which is useful up until the point of contact. To understand how an object is being gripped, tactile feedback is needed. For handling fragile objects, if the grip is too tight, breakage occurs, and if the grip is too loose, the object will slip out of the grasp, also leading to breakage. Rigid robotic grippers using a visual feedback loop can struggle to determine the exact point and quality of contact. Robotic grippers can also get a stuttering effect in the visual feedback loop. By using soft Synthetic Muscle™ based EAP pads as the sensors, immediate feedback was generated at the first point of contact. Because these pads provided a soft, compliant interface, the first point of contact did not apply excessive force, allowing the force applied to the object to be controlled. The EAP sensor could also detect a change in pressure location on its surface, making it possible to detect and prevent slippage by then adjusting the grip strength. In other words, directional glide provided feedback for the presence of possible slippage to then be able to control a slightly tighter grip, without stutter, due to both the feedback and the soft gentleness of the fingertip-like EAP pads themselves. The soft nature of the EAP fingertip pad also naturally held the gripped object, improving the gripping quality over rigid grippers without an increase in applied force. Analogous to finger-like tactile touch, the EAPs with appropriate coatings and electronics were positioned as pressure sensors in the fingertip or end effector regions of robotic grippers. This development of using Synthetic Muscle™ based EAPs as soft sensors provided for sensors that feel like the pads of human fingertips. Basic pressure position and magnitude tests have been successful, with pressure sensitivity down to 0.05 N. Most automation and robots are very strong, very fast, and usually need to be partitioned away from humans for safety reasons. For many repetitive tasks that humans do with delicate or fragile objects, it would be beneficial to use robotics; whether it is for agriculture, medical surgery, therapeutic or personal care, or in extreme environments where humans cannot enter, including with contagions that have no cure. Synthetic Muscle™ was also retrofitted as actuator systems into off-the-shelf robotic grippers and is being considered in novel biomimetic gripper designs, operating at low voltages (less than 50 V). This offers biomimetic movement by contracting like human muscles, but also exceeds natural biological capabilities by expanding under reversed electric polarity. Human grasp is gentle yet firm, with tactile touch feedback. In conjunction with shape-morphing abilities, these EAPs also are being explored to intrinsically sense pressure due to the correlation between mechanical force applied to the EAP and its electronic signature. The robotic field is experiencing phenomenal growth in this fourth phase of the industrial revolution, the robotics era. The combination of Ras Labs’ EAP shape-morphing and sensing features promises the potential for robotic grippers with human hand-like control and tactile sensing. This work is expected to advance both robotics and prosthetics, particularly for collaborative robotics to allow humans and robots to intuitively work safely and effectively together. 
    more » « less
  2. null (Ed.)
    Development of highly stretchable and sensitive soft strain sensors is of great importance for broad applications in artificial intelligence, wearable devices, and soft robotics, but it proved to be a profound challenge to integrate the two seemingly opposite properties of high stretchability and sensitivity into a single material. Herein, we designed and synthesized a new fully polymeric conductive hydrogel with an interpenetrating polymer network (IPN) structure made of conductive PEDOT:PSS polymers and zwitterionic poly(HEAA- co -SBAA) polymers to achieve a combination of high mechanical, biocompatible, and sensing properties. The presence of hydrogen bonding, electrostatic interactions, and IPN structures enabled poly(HEAA- co -SBAA)/PEDOT:PSS hydrogels to achieve an ultra-high stretchability of 4000–5000%, a tensile strength of ∼0.5 MPa, a rapid mechanical recovery of 70–80% within 5 min, fast self-healing in 3 min, and a strong surface adhesion of ∼1700 J m −2 on different hard and soft substrates. Moreover, the integration of zwitterionic polySBAA and conductive PEDOT:PSS facilitated charge transfer via optimal conductive pathways. Due to the unique combination of superior stretchable, self-adhesive, and conductive properties, the hydrogels were further designed into strain sensors with high sensing stability and robustness for rapidly and accurately detecting subtle strain- and pressure-induced deformation and human motions. Moreover, an in-house mechanosensing platform provides a new tool to real-time explore the changes and relationship between network structures, tensile stress, and electronic resistance. This new fully polymeric hydrogel strain sensor, without any conductive fillers, holds great promise for broad human-machine interface applications. 
    more » « less
  3. Abstract

    Progress in soft and stretchable electronics depends on energy sources that are mechanically compliant, elastically deformable, and renewable. Energy harvesting using triboelectric nanogenerators (TENGs) made from soft materials provides a promising approach to address this critical need. Here, an elastomeric composite is introduced with sedimented liquid metal (LM) droplets for TENG‐based energy harvesting that relies on assembly of the LM to form phase‐separated conductive and insulating regions. The sedimented LM elastomer TENG (SLM‐TENG) exhibits ultrahigh stretchability (strain limit>500% strain), skin‐like compliance (modulus<60 kPa), reliable device stability (>10 000 cycles), and appreciable electrical output performance (max peak power density=1 mW cm−2). SLM‐TENGs can be integrated with highly elastic stretchable fabrics, thereby enabling broad integration with wearable electronics. A stretchable and wearable SLM‐TENG is demonstrated that harvests energy from human motion through a patch attached to the knee or integrated into exercise clothing. This body‐mounted TENG device can generate enough electricity to fully power a wearable computing device (hygro‐thermometer with digital display) after 2.2 min of running on a treadmill.

     
    more » « less
  4. Possessing a unique combination of properties that are traditionally contradictory in other natural or synthetical materials, Ga-based liquid metals (LMs) exhibit low mechanical stiffness and flowability like a liquid, with good electrical and thermal conductivity like metal, as well as good biocompatibility and room-temperature phase transformation. These remarkable properties have paved the way for the development of novel reconfigurable or stretchable electronics and devices. Despite these outstanding properties, the easy oxidation, high surface tension, and low rheological viscosity of LMs have presented formidable challenges in high-resolution patterning. To address this challenge, various surface modifications or additives have been employed to tailor the oxidation state, viscosity, and patterning capability of LMs. One effective approach for LM patterning is breaking down LMs into microparticles known as liquid metal particles (LMPs). This facilitates LM patterning using conventional techniques such as stencil, screening, or inkjet printing. Judiciously formulated photo-curable LMP inks or the introduction of an adhesive seed layer combined with a modified lift-off process further provide the micrometer-level LM patterns. Incorporating porous and adhesive substrates in LM-based electronics allows direct interfacing with the skin for robust and long-term monitoring of physiological signals. Combined with self-healing polymers in the form of substrates or composites, LM-based electronics can provide mechanical-robust devices to heal after damage for working in harsh environments. This review provides the latest advances in LM-based composites, fabrication methods, and their novel and unique applications in stretchable or reconfigurable sensors and resulting integrated systems. It is believed that the advancements in LM-based material preparation and high-resolution techniques have opened up opportunities for customized designs of LM-based stretchable sensors, as well as multifunctional, reconfigurable, highly integrated, and even standalone systems. 
    more » « less
  5. Madden, John D. ; Anderson, Iain A. ; Shea, Herbert R. (Ed.)
    Current robotic sensing is mainly visual, which is useful up until the point of contact. To understand how an object is being gripped, tactile feedback is needed. Human grasp is gentle yet firm, with integrated tactile touch feedback. Ras Labs makes Synthetic Muscle™, which is a class of electroactive polymer (EAP) based materials and actuators that sense pressure from gentle touch to high impact, controllably contract and expand at low voltage (battery levels), and attenuate force. The development of this technology towards sensing has provided for fingertip-like sensors that were able to detect very light pressures down to 0.01 N and even 0.005 N, with a wide pressure range to 25 N and more and with high linearity. By using these soft yet robust Tactile Fingertip™ sensors, immediate feedback was generated at the first point of contact. Because these elastomeric pads provided a soft compliant interface, the first point of contact did not apply excessive force, allowing for gentle object handling and control of the force applied to the object. The Tactile Fingertip could also detect a change in pressure location on its surface, i.e., directional glide provided real time feedback, making it possible to detect and prevent slippage by then adjusting the grip strength. Machine learning (ML) and artificial intelligence (AI) were integrated into these sensors for object identification along with the determination of good grip (position, grip force, no slip, no wobble) for pick-and-place and other applications. Synthetic Muscle™ is also being retrofitted as actuators into a human hand-like biomimetic gripper. The combination of EAP shape-morphing and sensing promises the potential for robotic grippers with human hand-like control and tactile sensing. This is expected to advance robotics, whether it is for agriculture, medical surgery, therapeutic or personal care, or in extreme environments where humans cannot enter, including with contagions that have no cure, as well as for collaborative robotics to allow humans and robots to intuitively work safely and effectively together. 
    more » « less