Single-input single-output multi-touch soft sensor systems using band-pass filters

Carbon Black Sensor and Neural Network Model for Sensing Angle in Soft Pneumatic Actuators

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

When combined with diverse sensors, soft robots significantly improve their functionalities and intelligence levels. However, most of the existing soft sensors require complex signal analysis devices or algorithms, which severely increase the complexity of soft robot systems. Here, based on the unique fluidic property of liquid metal, we propose a magnet-controllable soft microswitch that can be well-integrated into a soft robot system, e.g., a soft gripper to help it facilely detect and precisely grab objects. The microswitch consists of a flexible soft beam electrode and a fixed electrode, forming a soft microsystem. By tuning the cohesion force of the liquid metal between the electrodes, the microswitch can convert its states between an individual and a self-locking state. The microswitch can achieve a reasonable rapid response (~12 ms) and high switching frequency (~95 Hz). Furthermore, soft microswitches can be customized into logic units and also coupled to control a digital tube showing various numbers. Our work provides a new simple soft sensor unit that may enhance the intelligence of soft systems.

Micromanipulation methods used for complicated tasks such as microrobot assembly and microvascular surgery often lack the force reflection and contact localization capability necessary to achieve robust grasps of micro-scale objects without applying excessive forces. This absence of haptic feedback is especially prohibitive in cases where visual evidence of force application, such as object surface deformation, is imperceptible and where unstructured, dynamically changing environments require force sensing and modulation for safe, atraumatic object manipulation. This paper describes the design, fabrication, and experimental validation of a soft tactile sensor array for sub-millimeter contact localization and contact force measurement during micromanipulation. The geometry and placement of conductive liquid embedded channels within the sensor array are optimized to provide adequate sensitivity for representative micro-manipulation tasks. Mechanical testing of the sensor demonstrates a sensitivity of less than 50mN and contact localization resolution on the order of 100's of microns.

In this study, a novel soil analysis method for fertilization recommendation was developed and validated with 161 soil samples taken from Turkey - Çarşamba plain for determination of potassium as a plant nutrient. In conventional soil analysis methods, available potassium (K) nutrient was determined by ammonium acetate extraction with flame photometer. In this study an alternative to existing method was proposed by developing extraction solutions suitable for interference dynamics of ion selective electrodes in a flow injection setup. Flow injection analysis system was optimized and K ion concentration of 161 soil samples taken from Turkey – Çarşamba plain was determined with potentiometrically. For the same soil samples, K+ ion concentration was determined with ammonium acetate extraction using flame photometer in parallel. Fertilization recommendations for potassium was calibrated on ammonium acetate extraction based measurements. In order to evaluate available potassium nutrient analysis results from new generation soil analysis method in fertilization recommendation process, a correlation model is required for relating new generation method results to conventional method results. An artificial neural network based soft sensor system was developed for this task. Potentiometric K+ ion measurement of soil sample in flow injection analysis system was presented as input to soft sensor system. Soft sensor predicted available K in soil sample based on artificial neural network model which can be used in fertilizer recommendation. Prediction performance of soft sensor was validated with experimental data and fitted with high correlation coefficient (R2= 0.902). Experimental studies have shown that K determined by potentiometric measurements can be used in fertilization recommendations in Çarşamba plain by using soft sensor approach.

Nanoscale torsion-free photonic crystal pressure sensor with ultra-high sensitivity based on side-coupled piston-type microcavity

This paper reports on the design, fabrication, and evaluation of a novel 3D printed vacuum based soft sensor and its application as hand motion sensor. Soft sensors are an integral part of soft robotics as an emerging field of science that enables safe and easy human to machine interaction. The conventional sensors used to interface with humans in robotic systems are mechanically incompliant. Because of this mechanical compliance mismatch, these sensors cause unsafe interactions with humans. Recent advancements in 3D printing technology have allowed fabrication of complex geometries with soft polymers, perfect for more delicate and user-friendly sensors. While the existing soft pneumatic sensors work based on the positive pressure due to deformation, the proposed sensor in this work works based on vacuum due to deformation. In comparison with the similar positive pressure sensor, our vacuum-based sensor is much softer and has minimum interference with hand motion. Finite element analysis (FEA) is used to analyze the design. Also, a kinematic model of the hand is developed using Matlab to visualize the hand motion based on the sensor data. This could show the potential application of the soft hand motion sensor in virtual reality and telehealth.

Ionic Elastomers for Electric Actuators and Sensors

Soft Sensor Model Derived from Wiener Model Structure: Modeling and Identification

This letter reports the design, fabrication, and evaluation of a flexible cross-bar pressure sensor array for real-time high-resolution force mapping, with a primary application in pressure monitoring. Traditional flexible pressure sensors are limited by low spatial resolution and wiring complexity, hindering their use in detailed biomechanical analysis. Here, we introduce a novel plain-weave cross-bar structure, in which copper wire electrodes are interlaced with multilayer Velostat sheets. The sensor array is integrated with an ESP32 microcontroller and a multiplexer to achieve scalable data acquisition and wireless transmission. Calibration tests demonstrate that the sensor exhibits high sensitivity (up to 0.85 kPa<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>) in the low-pressure regime (0–2.5 kPa), and reliable response for dynamic foot pressure mapping during walking. The system enables real-time visualization and mapping of pressure distribution across a 6 × 6 array, providing rich data for gait and rehabilitation monitoring. This design improves electrode-material contact uniformity, and reduces noise compared to conventional stacked cross-bar configurations. The combination of a low-cost fabrication approach with the novel plain-weave architecture establishes the key novelty of this work, offering a scalable and customizable platform for wearable biomechanics and health monitoring.

Bicycle riding is an increasingly popular recreational and competitive activity, however, the more popular the more biking-related injuries. Most of cycling injuries are musculoskeletal related and caused by a combination of inadequate preparation, inappropriate bike fitting, poor technique, and overuse of prolonged uphill biking. However, after well bike fitting, the injury is still existed, the leg alignment and foot types might be an important factor that mainly causes the muscular injury after an appropriate bike fitting. [1-3]The purpose of this study was to investigate the efficacy of an arch support insole with/o forefoot wedge in muscle activities and joint loads in order to mimic the musculoskeletal sport injury in cycling and to enhance the performance. Eleven amateur cyclists were recruited for this study. Vicon motion analysis system, Pedar in-sole foot pressure sensor and Delsys EMG system were used to measure the three-dimensional lower extremity kinematics, kinetics, EMG signal. Each subject was randomly shot four different insoles (Bikepro, off-counter insole with/o arch support and forefoot wedge) with his own bike shoes and bike mounted on a cycle ergometer set to a fixed power of 150W in 75rpm. An One-way ANOVA repeated measurement was used to discriminate the effect of insole material, arch, and forefoot wedge. The results showed that the BikePro significantly increased the ankle varus angle(0.4°, p=0.029), the knee internal rotation (1.4°, p=0.030), and it significantly decreased the ankle abduction angle (1.2°, p=0.047)at the bottom dead center (BDC); reduced the knee sway area by (10.4% , p=0.037). Combined with forefoot wedge, it significantly decreased the ankle varus(0.5°, p=0.005), and increased the ankle abduction angle (1.2°, p=0.005) but without changing the knee trajectory patterns.

Saline based microfluidic soft pressure sensor utilizing a three-dimensional focused electric field for motion and healthcare monitoring

Aiming at monitoring of lower limb movement, a MEMS integrated chip packaged in a sensing suit and a thin film pressure sensor packaged in a sensing insole are used to collect lower limb motion information. However, it was found that there was a huge error in the measurement of pressure when using the film-based pressure sensors to monitor the foot pressure in the lower limb movement. Hence, this paper describes a method for calibration of soft pressure sensors and characterizes the pressure through the proposed approach. The results show that there is a nonlinear relationship between the input (pressure) and the output (inverse value of the electrical resistance); the fitting error is about 5.5%; the hysteresis is around 4.2%;the zero drift is about 0.11%; the repeatability error is nearly 10.7% and the uncertainty of the sensor is about 14.64%. Therefore, it suggests that there often are notable repeatability errors and sensor uncertainty in the soft thin film pressure sensors, which would affect the accuracy in the monitoring of lower limb movements.

Textile-based pressure sensor arrays: A novel scalable manufacturing technique

Gait analysis plays an important role in various applications such as health care, clinical rehabilitation, sport training and pedestrian navigation. In order to monitor the human gait, an interesting approach is to analyze the foot plantar pressure distribution between the foot and the ground. In recent years, the emergence of flexible, soft and lightweight sensors facilitates the rapid technological advances in in-shoe foot pressure measurements, thereby especially carbon nanotubes-based sensors provide an outstanding solution for the implementation of flexible, soft pressure sensors in foot pressure distribution analysis. This chapter focuses on the design and implementation of multiwalled carbon nanotubes (CNT)/polydimethylsil-oxane (PDMS) based nanocomposite pressure sensors for the analysis of the foot pressure distribution. The sensor is durable, stable and shows sensitivity of 3.3 k\({\Omega }\)/kPa and hysteresis smaller than 3.64% with maximum detectable pressure up to 217 kPa, which is suitable for the measurement of human foot pressure. The proposed sensor has been implemented in a flexible in-sole, which is designed based on normal arch foot anatomy. A total of 12 sensors are distributed in the heel, lateral back foot, midfoot and front foot. The foot pressure distribution for different persons while walking and standing using nanocomposite sensor based in-sole were investigated by measuring the changing in resistance of the pressure sensors, when pressure applied on it. It shows that foot pressure distribution is higher in the fore foot and the heel while person standing in normal position. While walking, initially the foot pressure is in the heel and then transferred to the entire foot and finally it is concentrated on the fore foot.KeywordsCarbon nanotubesPolydimethylsiloxaneNanocompositesPressure sensorsScreen printingSolution mixingGait analysis