A wearable motion capture device able to detect dynamic motion of human limbs

Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.

Current wearable motion capture technologies are unable to accurately detect dynamic motion of human limbs due to drift and instability problems. Here, the authors report a wearable motion capture device combining tri-axis velocity sensor and inertial sensors for accurate 3D limb motion capture.

Wireless Ti3C2Tx MXene Strain Sensor with Ultrahigh Sensitivity and Designated Working Windows for Soft Exoskeletons

Emerging soft exoskeletons pose urgent needs for high-performance strain sensors with tunable linear working windows to achieve a high-precision control loop. Still, the state-of-the-art strain sensors require further advances to simultaneously satisfy multiple sensing parameters, including high sensitivity, reliable linearity, and tunable strain ranges. Besides, a wireless sensing system is highly desired to enable facile monitoring of soft exoskeleton in real time, but is rarely investigated. Herein, wireless Ti₃C₂T_x MXene strain sensing systems were fabricated by developing hierarchical morphologies on piezoresistive layers and incorporating regulatory resistors into circuit designs as well as integrating the sensing circuit with near-field communication (NFC) technology. The wireless MXene sensor system can simultaneously achieve an ultrahigh sensitivity (gauge factor ≥ 14,000) and reliable linearity (R² ≈ 0.99) within multiple user-designated high-strain working windows (130% to ≥900%). Additionally, the wireless sensing system can collectively monitor the multisegment exoskeleton actuations through a single database channel, largely reducing the data processing loading. We finally integrate the wireless, battery-free MXene e-skin with various soft exoskeletons to monitor the complex actuations that assist hand/leg rehabilitation.

A review of soft wearable robots that provide active assistance: Trends, common actuation methods, fabrication, and applications

This review meta-analysis combines and compares the findings of previously published works in the field of soft wearable robots (SWRs) that provide active methods of actuation for assistive and augmentative purposes. A thorough investigation of major contributions in the field of an SWR is made to analyze trends in the field focused on fluidic and cable-driven systems, prevalent and successful approaches, and identify the future direction of SWRs and active actuation strategies. Types of soft actuators used in wearables are outlined, as well as general practices for fabrication methods of soft actuators and considerations for human-robot interface designs of garment-like exosuits. An overview of well-known and emerging upper body (UB)- and lower body (LB)-assistive technologies is categorized by the specific joints and degree of freedom (DoF) assisted and which actuator methodology is provided. Different use cases for SWRs are addressed, as well as implementation strategies and design applications.

A fluidic demultiplexer for controlling large arrays of soft actuators

The field of soft robotics endeavors to create robots that are mostly, if not entirely, soft. While there have been significant advances in both soft actuators and soft sensors, there has been relatively little work done in the development of soft control systems. This work proposes a soft microfluidic demultiplexer as a potential control system for soft robotics. Demultiplexers enable the control of many outputs with just a few inputs, increasing a soft robot's complexity while minimizing its reliance on external valves and other off-board components. The demultiplexer in this work improves upon earlier microfluidic demultiplexers with its nearly two-fold reduction of inputs, a design feature that simplifies control and increases efficiency. Additionally, the demultiplexer in this work is designed to accommodate the high pressures and flow rates that soft robotics demands. The demultiplexer is characterized from the level of individual valves to full system parameters, and its functionality is demonstrated by controlling an array of individually addressable soft actuators.

Directed Assembly of Liquid Metal-Elastomer Conductors for Stretchable and Self-Healing Electronics

Growing interest in soft robotics, stretchable electronics, and electronic skins has created demand for soft, compliant, and stretchable electrodes and interconnects. Here, dielectrophoresis (DEP) is used to assemble, align, and sinter eutectic gallium indium (EGaIn) microdroplets in uncured poly(dimethylsiloxane) (PDMS) to form electrically conducting microwires. There are several noteworthy aspects of this approach. 1) Generally, EGaIn droplets in silicone at loadings approaching 90 wt% remain insulating and form a conductive network only when subjected to sintering. Here, DEP facilitates assembly of EGaIn droplets into conductive microwires at loadings as low as 10 wt%. 2) DEP is done in silicone for the first time, enabling the microwires to be cured in a stretchable matrix. 3) Liquid EGaIn droplets sinter during DEP to form a stretchable metallic microwire that retains its shape after curing the silicone. 4) Use of liquid metal eliminates the issue of compliance mismatch observed in soft polymers with solid fillers. 5) The silicone-EGaIn "ink" can be assembled by DEP within the crevices of severely damaged wires to create stretchable interconnects that heal the damage mechanically and electrically. The DEP process of this unique set of materials is characterized and the interesting attributes enabled by such liquid microwires are demonstrated.

Advances in Materials for Soft Stretchable Conductors and Their Behavior under Mechanical Deformation

Soft stretchable sensors rely on polymers that not only withstand large deformations while retaining functionality but also allow for ease of application to couple with the body to capture subtle physiological signals. They have been applied towards motion detection and healthcare monitoring and can be integrated into multifunctional sensing platforms for enhanced human machine interface. Most advances in sensor development, however, have been aimed towards active materials where nearly all approaches rely on a silicone-based substrate for mechanical stability and stretchability. While silicone use has been advantageous in academic settings, conventional silicones cannot offer self-healing capability and can suffer from manufacturing limitations. This review aims to cover recent advances made in polymer materials for soft stretchable conductors. New developments in substrate materials that are compliant and stretchable but also contain self-healing properties and self-adhesive capabilities are desirable for the mechanical improvement of stretchable electronics. We focus on materials for stretchable conductors and explore how mechanical deformation impacts their performance, summarizing active and substrate materials, sensor performance criteria, and applications.

Highly Stretchable and Sensitive Pressure Sensor Array Based on Icicle-Shaped Liquid Metal Film Electrodes

Flexible pressure sensors emerge for important applications in wearable electronics, with increasing requirements for high sensitivity, fast response, and low detection limit. However, there is still a challenge in this field, that is, how to maximize both the electrical performance and mechanical stretchability simultaneously. Here, we report a straightforward and cost-effective method to fabricate highly stretchable and sensitive capacitive pressure sensor arrays. It features a unique design of integrating the icicle-shaped liquid metal film electrode and reliable processing of the liquid metal and elastomer. Under an external load, the deformation of the elastic bump structure dramatically results in an increase in the overlapping area of the electrodes and a decrease in the separation distance, offering a new capacitive sensing scheme with an enhanced sensitivity. Our sensor has been demonstrated with a high sensitivity of 39% kPa^-1 in the range of 0-1 kPa, limit of detection as low as 12 Pa, hysteresis error of 8.46% at a maximum pressure of 25 kPa, and stretchability up to 94% strain without any failure. The arrayed sensor has been successfully applied to force measurements on a curved surface, contour mapping of external loads, and monitoring of contact pressures under various cervical postures.

Attributes, Fabrication, and Applications of Gallium-Based Liquid Metal Particles

This work discusses the attributes, fabrication methods, and applications of gallium-based liquid metal particles. Gallium-based liquid metals combine metallic and fluidic properties at room temperature. Unlike mercury, which is toxic and has a finite vapor pressure, gallium possesses low toxicity and effectively zero vapor pressure at room temperature, which makes it amenable to many applications. A variety of fabrication methods produce liquid metal particles with variable sizes, ranging from nm to mm (which is the upper limit set by the capillary length). The liquid nature of gallium enables fabrication methods-such as microfluidics and sonication-that are not possible with solid materials. Gallium-based liquid metal particles possess several notable attributes, including a metal-metal oxide (liquid-solid) core-shell structure as well as the ability to self-heal, merge, and change shape. They also have unusual phase behavior that depends on the size of the particles. The particles have no known commercial applications, but they show promise for drug delivery, soft electronics, microfluidics, catalysis, batteries, energy harvesting, and composites. Existing challenges and future opportunities are discussed herein.

Flexible Capacitive Curvature Sensor with One-Time Calibration for Amphibious Gait Monitoring

Wearable devices developed with flexible electronics have great potential applications for human health monitoring and motion sensing. Although material softness and structural flexibility provide a deformable human-machine interface to adapt to joint bending or tissue stretching/compression, flexible sensors are inconvenient in practical uses as they usually require calibration every time they are installed. This article presents an approach to design and fabricate a flexible curvature sensor to measure human articular movements for amphibious applications. This flexible sensor employs the capacitive sensing principle, where the dielectric layer and electrodes are made from the polyurethane resin and eutectic gallium-indium (EGaIn) liquid metal; and the fabrication process is implemented with shape deposition molding for batch production. The sensing method for articular rotation angles employs the Euler beam model to make the sensor reusable after one-time calibration by compensating for the unpredicted manual installation error. The illustrative application to ankle sensing in amphibious gaits shows that the root-mean-square error is within 5° for different walking speeds (0.7-1.1 m/s) in treadmill tests and the maximum error is within 3° for underwater sensing with quasi-static measurements. It is expected that the proposed waterproof flexible sensor can push the boundaries of wearable robotics, human locomotion, as well as their related applications.

Stretchable Biaxial and Shear Strain Sensors Using Diffractive Structural Colors

Structural colors that can be changed dynamically, using either plasmonic nanostructures or photonic crystals, are rapidly emerging research areas for stretchable sensors. Despite the wide applications of various techniques to achieve strain-responsive structural colors, important factors in the feasibility of strain sensors-such as their sensing mechanism, stability, and reproducibility-have not yet been explored. Here, we introduce a stretchable, diffractive, color-based wireless strain sensor that can measure strain using the entire visible spectrum, based on an array of cone-shaped nanostructures on the surface of an elastomeric substrate. By stretching or compressing the substrate, the diffractive color can be tuned according to the changing grating pitch. Using the proposed method, we designed three types of strain-sensing modes: large-deformation (maximum 100%) tensile strain, biaxial 2D strain, and shear strain (maximum 78%). The strain sensors were fabricated, and applicability to strain-sensing was evaluated.

Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics

Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

Soft Miniaturized Actuation and Sensing Units for Dynamic Force Control of Cardiac Ablation Catheters

Recently, there has been active research in finding robotized solutions for the treatment of atrial fibrillation (AF) by augmenting catheter systems through the integration of force sensors at the tip. However, limited research has been aimed at providing automatic force control by also integrating actuation of the catheter tip, which can significantly enhance safety in such procedures. This article solves the demanding challenge of miniaturizing both actuation and sensing for integration into flexible catheters. Fabrication strategies are presented for a series of novel soft thick-walled cylindrical actuators, with embedded sensing using eutectic gallium-indium. The functional catheter tips have a diameter in the range of 2.6-3.6 mm and can both generate and detect forces in the range of < 0.4 N, with a bandwidth of 1-2 Hz. The deformation modeling of thick-walled cylinders with fiber reinforcement is presented in the article. An experimental setup developed for static and dynamic characterization of these units is presented. The prototyped units were validated with respect to the design specifications. The preliminary force control results indicate that these units can be used in tracking and control of contact force, which has the potential to make AF procedures much safer and more accurate.

Electronic skins and machine learning for intelligent soft robots

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network

Soft sensors are attracting significant attention in human–machine interaction due to their high flexibility and adaptability. However, estimating motion state from these sensors is difficult due to their nonlinearity and noise. In this paper, we propose a deep learning network for a smart glove system to predict the moving state of a piezoelectric soft sensor. We implemented the network using Long-Short Term Memory (LSTM) units and demonstrated its performance in a real-time system based on two experiments. The sensor’s moving state was estimated and the joint angles were calculated. Since we use moving state in the sensor offset calculation and the offset value is used to estimate the angle value, the accurate moving state estimation results in good performance for angle value estimation. The proposed network performed better than the conventional heuristic method in estimating the moving state. It was also confirmed that the calculated values successfully mimic the joint angles measured using a leap motion controller.

Ag flake/silicone rubber composite with high stability and stretching speed insensitive resistance via conductive bridge formation

High stability, stretchable speed insensitive properties, high stretchability, and electrical conductivity are key characteristics for the realisation of wearable devices. However, conventional research is mainly focused on achieving only high stretchability and electrical conductivity. Studies on the stability and stretching speed insensitive properties generally require complex fabrication processes, which are in need of further improvement. In this study, we propose a facile formation of a conductive bridge in composites by using surface damage and the viscoelastic property of the polymer. Surface cracks due to repeated stretching cycles formed conductive bridges via stress relaxation of the viscoelastic polymer matrix. The conductive bridge resulted in the conductor having highly stable resistance values at target strains and stretching speed insensitive resistance, even at stretching speeds that were 20 times faster than the minimum.

A Layered Manufacturing Approach for Soft and Soft-Rigid Hybrid Robots

We present a manufacturing process for creating centimeter-scale multichambered inflatable robots and structures that can include both soft and rigid components. Our process uses a thermoplastic polyurethane (TPU) adhesive film to bond together layers of textiles, plastics, or other materials. The structures are heated and compressed a few layers at a time with a heat press machine or bonded in an oven all at once. We present two methods for arranging textiles and thermal adhesive film to achieve airtight structures and perform modeling and measurements on the resulting inflatable chambers. We characterize the set of textiles and rigid materials that will work with this process, measuring how strongly the TPU film bonds with them. We also describe how to include corners, where several pieces of material come together at a point, and determine which corner constructions are airtight. We characterize how different seam widths behave, determine the maximum pressure chambers fabricated with this process can support, and determine the cycle life of actuators built with this process. Finally, we present an actuator with an embedded sensor and three examples of robots constructed with textiles and TPU film, including a hybrid soft/rigid robotic arm, a soft robot that can roll along the ground, and a robot that can climb inside tubes or other confined spaces.

Mechanical Sensing for Lower Limb Soft Exoskeletons: Recent Progress and Challenges

Soft exoskeletons hold promise for facilitating monitoring and assistance in case of light impairment and for prolonging independent living. In contrast to rigid material-based exoskeletons, they strongly demand for new approaches of soft sensing and actuation solutions. This chapter overviews soft exoskeletons in contrast to rigid exoskeletons and focuses on the recent advancements on the movement monitoring in lower limb soft exoskeletons. Compliant materials and soft tactile sensing approaches can be utilized to build smart sensorized garments for joint angle measurements (needed for both control and monitoring). However, currently there are still several open challenges derived from the needed close interaction between the human body and the soft exoskeleton itself, especially related to how sensing function and robustness are strongly affected by wearability, which will need to be overcome in the near future.

Wearable Fiber Optic Sensors for Biomechanical Sensing via Joint Angle Detection

Monitoring human biomechanical movement is necessary for the analysis and development of kinesthetic exercise techniques in physical rehabilitation, professional sports, and performance arts. Optical fiber technology offers an attractive solution to motion capture sensing in terms of size, robustness, signal fidelity, and efficiency. We report on the development of PDMS-based fiber optic strain sensors for biomechanical sensing in real-time via the evaluation of skeletal joint angles. The fibers were fabricated using an elastomer and gel combination in a 3:2 ratio. The elasticity and optical loss of this novel fiber material was experimentally characterized for two fiber diameters of 3 mm and 5 mm. The experimental stress-strain behavior was fitted to a 3D hyperelastic Mooney-Rivlin model to obtain C01 and C10 material constants of 0.022 MPa and 0.0308 MPa respectively. Transmission monotonically decreased in response to a stress applied in both the longitudinal (elongation) and lateral (bending) directions. The sensors were demonstrated in a motion sensing implementation by monitoring the joint angle at the elbow in real-time. Measurements indicated a consistent performance of both fiber diameters over the range of motion of the elbow corresponding to flexion and extension. The optical loss increased by 0.1784 dB and 0.1147 dB for each degree of flexion with standard deviation error in measurement of 3.525° and 4.672° for the 3 mm and 5 mm fiber diameters, respectively. The results demonstrate the potential of this system for real-time, wearable biomechanical sensing, motion capture systems, and as a feedback mechanism in prosthetics and robotics.

MakeSense: Automated Sensor Design for Proprioceptive Soft Robots

Soft robots have applications in safe human-robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Carbon fibre based flexible piezoresistive composites to empower inherent sensing capabilities for soft actuators

New materials and technologies in sensing and actuation have led to the development of soft actuators and robots for biomedical systems, assistive devices, exploration and rescue. The use of integrated actuation-sensing materials in such systems is gaining interest, but there are few examples where the body of the actuator or soft robot acts as the sensing element. The development of smart soft actuators that have inherent sensing capabilities can provide advantages of high sensitivity, ease of manufacture and cost efficiency, without impairing actuator dynamics. To achieve this goal, we have prepared soft actuators using piezoresistive composites based on a silicone matrix impregnated with short conductive carbon fibres. The optimum carbon fibre volume fraction to achieve a frequency independent conductivity and piezoresistive response was determined, with in situ mechanical and electrical testing to quantify the piezoresistive properties. The frequency dependent electrical properties and sensitivity of the composites with deformation was explained on the basis of a microstructural resistor-capacitor network model. The piezoresistive composites were used to successfully manufacture a pneumatic soft finger actuator where the resistance change of the actuator body was able to monitor deformation with applied pressure. The creation of soft actuators with an inherent sensing capability is a promising approach for control and operation of future soft robots.

Mechanoresponsive Polymerized Liquid Metal Networks

Room-temperature liquid metals, such as nontoxic gallium alloys, show enormous promise to revolutionize stretchable electronics for next-generation soft robotic, e-skin, and wearable technologies. Core-shell particles of liquid metal with surface-bound acrylate ligands are synthesized and polymerized together to create cross-linked particle networks comprising >99.9% liquid metal by weight. When stretched, particles within these polymerized liquid metal networks (Poly-LMNs) rupture and release their liquid metal payload, resulting in a rapid 10⁸ -fold increase in the network's conductivity. These networks autonomously form hierarchical structures that mitigate the deleterious effects of strain on electronic performance and give rise to emergent properties. Notable characteristics include nearly constant resistances over large strains, electronic strain memory, and increasing volumetric conductivity with strain to over 20 000 S cm^-1 at >700% elongation. Furthermore, these Poly-LMNs exhibit exceptional performance as stretchable heaters, retaining 96% of their areal power across relevant physiological strains. Remarkable electromechanical properties, responsive behaviors, and facile processing make Poly-LMNs ideal for stretchable power delivery, sensing, and circuitry.

Fiber Optic Shape Sensing for Soft Robotics

While soft material actuators can undergo large deformations to execute very complex motions, what is critically lacking in soft material robotic systems is the ability to collect high-resolution shape information for sophisticated functions such as environmental mapping, collision detection, and full state feedback control. This work explores the potential of a nearly commercial fiber optic shape sensor (FOSS) and presents the first demonstrations of a monolithic, multicore FOSS integrated into the structure of a fiber-reinforced soft actuator. In this pilot study, we report an open loop sensorized soft actuator capable of submillimeter position feedback that can detect the soft actuator's shape, environmental shapes, collision locations, and material stiffness properties.

Soft and Deformable Sensors Based on Liquid Metals

Liquid metals are one of the most interesting and promising materials due to their electrical, fluidic, and thermophysical properties. With the aid of their exceptional deformable natures, liquid metals are now considered to be electrically conductive materials for sensors and actuators, major constituent transducers in soft robotics, that can experience and withstand significant levels of mechanical deformation. For the upcoming era of wearable electronics and soft robotics, we would like to offer an up-to-date overview of liquid metal-based soft (thus significantly deformable) sensors mainly but not limited to researchers in relevant fields. This paper will thoroughly highlight and critically review recent literature on design, fabrication, characterization, and application of liquid metal devices and suggest scientific and engineering routes towards liquid metal sensing devices of tomorrow.

Closing the Wearable Gap-Part II: Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements

The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot–ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements—plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)—were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type. PF, INV, and EVR each had three POCs identified based on bony landmarks of the foot and ankle while the DF location was only tested for one POC. Each participant wore a specialized compression sock where the SRS could be consistently tested from all POCs for each participant. The movement data collected from each sensor was then compared against 3D motion capture data. R-squared and root-mean-squared error averages were used to assess relative and absolute measures of fit to motion capture output. Participant robustness, opposing movements, and gender were also used to identify good SRS POC placement for foot–ankle movement capture.

Buckled Structures: Fabrication and Applications in Wearable Electronics

Wearable electronics have attracted a tremendous amount of attention due to their many potential applications, such as personalized health monitoring, motion detection, and smart clothing, where electronic devices must conformably form contacts with curvilinear surfaces and undergo large deformations. Structural design and material selection have been the key factors for the development of wearable electronics in the recent decades. As one of the most widely used geometries, buckling structures endow high stretchability, high mechanical durability, and comfortable contact for human-machine interaction via wearable devices. In addition, buckling structures that are derived from natural biosurfaces have high potential for use in cost-effective and high-grade wearable electronics. This review provides fundamental insights into buckling fabrication and discusses recent advancements for practical applications of buckled electronics, such as interconnects, sensors, transistors, energy storage, and conversion devices. In addition to the incorporation of desired functions, the simple and consecutive manipulation and advanced structural design of the buckled structures are discussed, which are important for advancing the field of wearable electronics. The remaining challenges and future perspectives for buckled electronics are briefly discussed in the final section.

Articular Geometry Reconstruction for Knee Joint with a Wearable Compliant Device

Nonlinear articular geometries of biological joints have contributed to highly agile and adaptable human-body motions. However, human–machine interaction could potentially distort natural human motions if the artificial mechanisms overload the articular surfaces and constrain biological joint kinematics. It is desired to better understand the deformable articular geometries of biological joints in vivo during movements for design and control of wearable robotics. An articular geometry reconstruction method is proposed to measure the effective articular profile with a wearable compliant device and illustrated with its application to knee-joint kinematic analysis. Regarding the joint articulation as boundary constraints for the compliant mechanism, the equivalent articular geometry is constructed from the beam deformations driven by knee motions, where the continuous deformations are estimated with strain data from the embedded sensors. Both simulated analysis and experimental validation are presented to justify the proposed method.

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots

Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user–robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user.

Direct Wiring of Eutectic Gallium-Indium to a Metal Electrode for Soft Sensor Systems

For wider applications of liquid metal-based stretchable electronics, electrical interface has remained a crucial issue due to its fragile electromechanical stability and complex fabrication steps. In this study, a direct writing-based technique is introduced to form the writing paths of conductive liquid metal (eutectic gallium-indium, eGaIn) and electrical connections to off-the-shelf metal electrodes in a single process. Specifically, by extending eGaIn wires written on a silicone substrate, the eGaIn wires were physically connected to five different metal electrodes, of which stability as an electrical connection was investigated. Among the five different surface materials, the metal electrode finished by electroless nickel immersion gold (ENIG) was reproducible and had low contact resistance without time-dependent variation. In our experiments, it was verified that the electrode part made by an ENIG-finished flexible flat cable (FFC) was mechanically (strain, ≤100%; pressure, ≤600 kPa) and thermally (temperature, ≤180 °C) durable. By modifying the trajectories of eGaIn wires, soft sensor systems composed of 10 sensing units were fabricated and tested to measure finger joint angles and ground reaction forces, respectively. The proposed method enables eGaIn-based soft sensors or circuits to be connected to typical electronic components through FFCs or weldable surfaces, using only off-the-shelf materials without additional mechanical or chemical treatments.

A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors

There is a growing need for flexible stretch sensors to monitor real time stress and strain in wearable technology. However, developing stretch sensors with linear responses is difficult due to viscoelastic and strain rate dependent effects. Instead of trying to engineer the perfect linear sensor we take a deep learning approach which can cope with non-linearity and yet still deliver reliable results. We present a general method for calibrating highly hysteretic resistive stretch sensors. We show results for textile and elastomeric stretch sensors however we believe the method is directly applicable to any physical choice of sensor material and fabrication, and easily adaptable to other sensing methods, such as those based on capacitance. Our algorithm does not require any a priori knowledge of the physical attributes or geometry of the sensor to be calibrated, which is a key advantage as stretchable sensors are generally applicable to highly complex geometries with integrated electronics requiring bespoke manufacture. The method involves three-stages. The first stage requires a calibration step in which the strain of the sensor material is measured using a webcam while the electrical response is measured via a set of arduino-based electronics. During this data collection stage, the strain is applied manually by pulling the sensor over a range of strains and strain rates corresponding to the realistic in-use strain and strain rates. The correlated data between electrical resistance and measured strain and strain rate are stored. In the second stage the data is passed to a Long Short Term Memory Neural Network (LSTM) which is trained using part of the data set. The ability of the LSTM to predict the strain state given a stream of unseen electrical resistance data is then assessed and the maximum errors established. In the third stage the sensor is removed from the webcam calibration set-up and embedded in the wearable application where the live stream of electrical resistance is the only measure of strain-this corresponds to the proposed use case. Highly accurate stretch topology mapping is achieved for the three commercially available flexible sensor materials tested.

Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics

Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g., geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors.

Recent Developments in Printing Flexible and Wearable Sensing Electronics for Healthcare Applications

Wearable biosensors attract significant interest for their capabilities in real-time monitoring of wearers' health status, as well as the surrounding environment. Sensor patches are embedded onto the human epidermis accompanied by data readout and signal conditioning circuits with wireless communication modules for transmitting data to the computing devices. Wearable sensors designed for recognition of various biomarkers in human epidermis fluids, such as glucose, lactate, pH, cholesterol, etc., as well as physiological indicators, i.e., pulse rate, temperature, breath rate, respiration, alcohol, activity monitoring, etc., have potential applications both in medical diagnostics and fitness monitoring. The rapid developments in solution-based nanomaterials offered a promising perspective to the field of wearable sensors by enabling their cost-efficient manufacturing through printing on a wide range of flexible polymeric substrates. This review highlights the latest key developments made in the field of wearable sensors involving advanced nanomaterials, manufacturing processes, substrates, sensor type, sensing mechanism, and readout circuits, and ends with challenges in the future scope of the field. Sensors are categorized as biological and fluidic, mounted directly on the human body, or physiological, integrated onto wearable substrates/gadgets separately for monitoring of human-body-related analytes, as well as external stimuli. Special focus is given to printable materials and sensors, which are key enablers for wearable electronics.

Biocompatible Soft Fluidic Strain and Force Sensors for Wearable Devices

Fluidic soft sensors have been widely used in wearable devices for human motion capturing. However, thus far, the biocompatibility of the conductive liquid, the linearity of the sensing signal, and the hysteresis between the loading and release processes have limited the sensing quality as well as the applications of these sensors. In this paper, silicone based strain and force sensors composed of a novel biocompatible conductive liquid (potassium iodide and glycerol solution) are introduced. The strain sensors exhibit negligible hysteresis up to 5 Hz, with a gauge factor of 2.2 at 1 Hz. The force sensors feature a novel multi-functional layered structure, with micro-cylinder-filled channels to achieve high linearity, low hysteresis (5.3% hysteresis at 1 Hz), and good sensitivity (100% resistance increase at a 5 N load). The sensors' gauge factors are stable at various temperatures and humidity levels. These bio-compatible, low hysteresis, and high linearity sensors are promising for safe and reliable diagnostic devices, wearable motion capture, and compliant human-computer interfaces.

Soft robot perception using embedded soft sensors and recurrent neural networks

Recent work has begun to explore the design of biologically inspired soft robots composed of soft, stretchable materials for applications including the handling of delicate materials and safe interaction with humans. However, the solid-state sensors traditionally used in robotics are unable to capture the high-dimensional deformations of soft systems. Embedded soft resistive sensors have the potential to address this challenge. However, both the soft sensors—and the encasing dynamical system—often exhibit nonlinear time-variant behavior, which makes them difficult to model. In addition, the problems of sensor design, placement, and fabrication require a great deal of human input and previous knowledge. Drawing inspiration from the human perceptive system, we created a synthetic analog. Our synthetic system builds models using a redundant and unstructured sensor topology embedded in a soft actuator, a vision-based motion capture system for ground truth, and a general machine learning approach. This allows us to model an unknown soft actuated system. We demonstrate that the proposed approach is able to model the kinematics of a soft continuum actuator in real time while being robust to sensor nonlinearities and drift. In addition, we show how the same system can estimate the applied forces while interacting with external objects. The role of action in perception is also presented. This approach enables the development of force and deformation models for soft robotic systems, which can be useful for a variety of applications, including human-robot interaction, soft orthotics, and wearable robotics.

Morphological/nanostructural control toward intrinsically stretchable organic electronics

The development of intrinsically stretchable electronics poses great challenges in synthesizing elastomeric conductors, semiconductors and dielectric materials.

Patterning and Reversible Actuation of Liquid Gallium Alloys by Preventing Adhesion on Rough Surfaces

This work reports a simple approach to form, study, and utilize rough coatings that prevent the adhesion of gallium-based liquid metal alloys. Typically, liquids with large interfacial tension do not wet nonreactive surfaces, regardless of surface topography. However, these alloys form a surface oxide "skin" that adheres to many substrates, even those with low surface energy. This work reports a simple approach to render closed channels and surfaces, including soft materials, to be "oxide-phobic" via spray-coating (NeverWet, which is commercially available and inexpensive). Surface spectroscopic techniques and metrology tools elucidate the coatings to comprise silica nanoparticles grafted with silicones that exhibit dual length scales of roughness. Although prior work shows the importance of surface roughness in preventing adhesion, the present work confirms that both hydrophobic and hydrophilic rough surfaces prevent oxide adhesion. Furthermore, the coating enables reversible actuation through submillimeter closed channels to form a reconfigurable antenna in the gigahertz range without the need for corrosive acids or bases that remove the oxide. In addition, the coating enables open surface patterning of conductive traces of liquid metal. This shows it is possible to actuate liquid metals in air without leaving neither metal nor oxide residue on surfaces to enable reconfigurable electronics, microfluidics, and soft electrodes.

Multi-Functional Soft Strain Sensors for Wearable Physiological Monitoring

Wearable devices which monitor physiological measurements are of significant research interest for a wide number of applications including medicine, entertainment, and wellness monitoring. However, many wearable sensing systems are highly rigid and thus restrict the movement of the wearer, and are not modular or customizable for a specific application. Typically, one sensor is designed to model one physiological indicator which is not a scalable approach. This work aims to address these limitations, by developing soft sensors and including conductive particles into a silicone matrix which allows sheets of soft strain sensors to be developed rapidly using a rapid manufacturing process. By varying the morphology of the sensor sheets and electrode placement the response can be varied. To demonstrate the versatility and range of sensitivity of this base sensing material, two wearable sensors have been developed which show the detection of different physiological parameters. These include a pressure-sensitive insole sensor which can detect ground reaction forces and a strain sensor which can be worn over clothes to allow the measurements of heart rate, breathing rate, and gait.

Ultra-sensitive microfluidic wearable strain sensor for intraocular pressure monitoring

Wearable technologies have potential to transform healthcare by providing continuous measurements of physiological parameters. Sensors that passively monitor physiological pressure without using electronic components are ideal for wearable contact lenses because they are easy to interface with the cornea and the external environment. Here, we report a passive integrated microfluidic sensor with a novel transduction mechanism that converts small strain changes into a large fluidic volume expansion, detectable by a smart-phone camera. The optimization of the sensor architecture and material properties results in a linear and stable sensor response. We have shown that the sensor has a detection limit of <0.06% for uniaxial and <0.004% for biaxial strain. We embedded our sensor in silicone contact lenses and measured the intraocular pressure induced strain in porcine eyes in the physiological range. The sensor's continuous operation capability for >19 hours and a lifetime reaching >7 months demonstrate its potential for long-term ophthalmic monitoring applications.

Shape-Changing Materials Using Variable Stiffness and Distributed Control

We describe a robotic material that tightly integrates sensing, actuation, computation, and communication to perform autonomous shape change. The composite consists of multiple cells, each with the ability to control their local stiffness (by Joule heating of a thermoplastic) and communicate with their local neighbors. We also present a distributed algorithm for calculating the inverse kinematic solution of the resulting N-body system by iteratively solving a series of problems with reduced kinematics. We describe material design choices, mechanism design, algorithm, and manufacturing, emphasizing the interdisciplinary codesign problem that robotic materials pose, and demonstrate the results from a series of shape-changing experiments.