Sensors For Soft Robotics Research Articles

Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies

A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle.

Closing the Wearable Gap-Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors.

The purpose of this study was to evaluate the use of compressible soft robotic sensors (C-SRS) in determining plantar pressure to infer vertical and shear forces in wearable technology: A ground reaction pressure sock (GRPS). To assess pressure relationships between C-SRS, pressure cells on a BodiTrakTM Vector Plate, and KistlerTM Force Plates, thirteen volunteers performed three repetitions of three different movements: squats, shifting center-of-pressure right to left foot, and shifting toes to heels with C-SRS in both anterior–posterior (A/P) and medial–lateral (M/L) sensor orientations. Pearson correlation coefficient of C-SRS to BodiTrakTM Vector Plate resulted in an average R-value greater than 0.70 in 618/780 (79%) of sensor to cell comparisons. An average R-value greater than 0.90 was seen in C-SRS comparison to KistlerTM Force Plates during shifting right to left. An autoregressive integrated moving average (ARIMA) was conducted to identify and estimate future C-SRS data. No significant differences were seen in sensor orientation. Sensors in the A/P orientation reported a mean R2 value of 0.952 and 0.945 in the M/L sensor orientation, reducing the effectiveness to infer shear forces. Given the high R values, the use of C-SRSs to infer normal pressures appears to make the development of the GRPS feasible.

Closing the Wearable Gap—Part IV: 3D Motion Capture Cameras Versus Soft Robotic Sensors Comparison of Gait Movement Assessment

The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R2 (R2; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R2) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait- based tasks with the accuracy of a 3D motion capture system.

Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.

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.

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.

The Development of Highly Flexible Stretch Sensors for a Robotic Hand

Demand for highly compliant mechanical sensors for use in the fields of robotics and wearable electronics has been constantly rising in recent times. Carbon based materials, and especially, carbon nanotubes, have been widely studied as a candidate piezoresistive sensing medium in these devices due to their favorable structural morphology. In this paper three different carbon based materials, namely carbon black, graphene nano-platelets, and multi-walled carbon nanotubes, were utilized as large stretch sensors capable of measuring stretches over 250%. These stretch sensors can be used in robotic hands/arms to determine the angular position of joints. Analysis was also carried out to understand the effect of the morphologies of the carbon particles on the electromechanical response of the sensors. Sensors with gauge factors ranging from one to 1.75 for strain up to 200% were obtained. Among these sensors, the stretch sensors with carbon black/silicone composite were found to have the highest gauge factor while demonstrating acceptable hysteresis in most robotic hand applications. The highly flexible stretch sensors demonstrated in this work show high levels of compliance and conformance making them ideal candidates as sensors for soft robotics.

The effect of dual-scale carbon fibre network on sensitivity and stretchability of wearable sensors

Sensitivity and stretchability are two key characteristics of wearable sensors and tactile sensors for soft robotics. Here, we present a new technique to increase the sensitivity of wearable sensors by creating a conductive network of dual-scale carbon fibres, i.e., carbon nanofibres (CNFs) and short carbon fibres (SCFs), in a polydimethylsiloxane (PDMS) matrix. To quantify the effects of this dual-scale carbon fibre network on the stretchability, sensitivity, and stability under repeated loading, comprehensive experiments were conducted to characterise the electrical conductivity, mechanical properties, and piezoresistivity of the resultant PDMS composites with varying concentrations of carbon fibres. The Prony series model of viscoelasticity was adapted to model the strain-rate dependent behaviour of the new sensors. The results reveal that this dual-scale network is able to significantly lower the percolation threshold below that of either of the single-scale composites containing only SCFs or CNFs, indicating a strong synergistic effect. Furthermore, the dual-scale carbon network exhibits higher piezoresistive sensitivity than the CNF-reinforced composite while retaining similar stretchability, thus offering a new technique for creating highly sensitive wearable sensors and tactile sensors for soft robotics.

Highly Elastic, Transparent, and Conductive 3D‐Printed Ionic Composite Hydrogels

Despite extensive progress to engineer hydrogels for a broad range of technologies, practical applications have remained elusive due to their (until recently) poor mechanical properties and lack of fabrication approaches, which constrain active structures to simple geometries. This study demonstrates a family of ionic composite hydrogels with excellent mechanical properties that can be rapidly 3D‐printed at high resolution using commercial stereolithography technology. The new material design leverages the dynamic and reversible nature of ionic interactions present in the system with the reinforcement ability of nanoparticles. The composite hydrogels combine within a single platform tunable stiffness, toughness, extensibility, and resiliency behavior not reported previously in other engineered hydrogels. In addition to their excellent mechanical performance, the ionic composites exhibit fast gelling under near‐UV exposure, remarkable conductivity, and fast osmotically driven actuation. The design of such ionic composites, which combine a range of tunable properties and can be readily 3D‐printed into complex architectures, provides opportunities for a variety of practical applications such as artificial tissue, soft actuators, compliant conductors, and sensors for soft robotics.

Stretchable and Soft Electronics using Liquid Metals.

The use of liquid metals based on gallium for soft and stretchable electronics is discussed. This emerging class of electronics is motivated, in part, by the new opportunities that arise from devices that have mechanical properties similar to those encountered in the human experience, such as skin, tissue, textiles, and clothing. These types of electronics (e.g., wearable or implantable electronics, sensors for soft robotics, e-skin) must operate during deformation. Liquid metals are compelling materials for these applications because, in principle, they are infinitely deformable while retaining metallic conductivity. Liquid metals have been used for stretchable wires and interconnects, reconfigurable antennas, soft sensors, self-healing circuits, and conformal electrodes. In contrast to Hg, liquid metals based on gallium have low toxicity and essentially no vapor pressure and are therefore considered safe to handle. Whereas most liquids bead up to minimize surface energy, the presence of a surface oxide on these metals makes it possible to pattern them into useful shapes using a variety of techniques, including fluidic injection and 3D printing. In addition to forming excellent conductors, these metals can be used actively to form memory devices, sensors, and diodes that are completely built from soft materials. The properties of these materials, their applications within soft and stretchable electronics, and future opportunities and challenges are considered.