Force Sensor Array Research Articles

A Simple Method to Manufacture a Force Sensor Array Based on a Single-Material 3D-Printed Piezoresistive Foam and Metal Coating.

Nowadays, 3D printing is becoming an increasingly common option for the manufacturing of sensors, primarily due to its capacity to produce intricate geometric shapes. However, a significant challenge persists in integrating multiple materials during printing, for various reasons. In this study, we propose a straightforward approach that combines 3D printing with metal coating to create an array of resistive force sensors from a single material. The core concept involves printing a sensing element using a conductive material and subsequently separating it into distinct parts using metal-coated lines connected to the electrical ground. This post-printing separation process involves manual intervention utilizing a stencil and metallic spray. The primary obstacle lies in establishing a sufficient contact surface between the sprayed metal and the structure, to ensure effective isolation among different zones. To address this challenge, we suggest employing a lattice structure to augment the contact surface area. Through experimental validation, we demonstrate the feasibility of fabricating two sensing elements from a single-material 3D-printed structure, with a maximum electrical isolation ratio between the sensors of above 30. These findings hold promise for the development of a new generation of low-tech 3D-printed force/displacement sensor arrays.

3-D Tactile-Based Object Recognition for Robot Hands Using Force-Sensitive and Bend Sensor Arrays

Tactile sensing is a particularly important and challenging task for a modern robot to safely manipulate objects, interact with humans in a shared space, and provide various services. This paper presents a 3D tactile glove for robots with the combination of a piezoresistive-based force sensor array (412 sensors) covering the full hand and a resistive bend sensor array (5 sensors) on the back of five fingers. Deep learning-based CNN (Convolutional Neural Network) methods using the designed tactile glove are proposed for object recognition. In the experiment for recognizing 15 objects with a dexterous robot hand, an average classification accuracy of 93.67% has been achieved. Comparison experiments with three other typical classifiers (the Quadratic SVM, Weighted KNN, and Bagged Trees) and our CNN methods show an average recognition accuracy of 91.67% with the 3D tactile glove, revealing an accuracy improvement of 4.17% over only using the force sensor array. We further apply our 3D tactile glove and the multi-modal CNN to identify three other objects and demonstrate their generalization ability of tactile object recognition with an average success accuracy of 78.33%. The proposed 3D tactile glove can be further used in human-robot interactions, the design of prosthetics and humanoid robots, and for improving the intelligence level in brain-computer collaborative systems.

Tactile Sensing for Soft Robotic Manipulators in 50 MPa Hydrostatic Pressure Environments

Deep‐sea exploration remains a challenging task as the extreme hydrostatic pressure environment, darkness, and suspended sediment launch severely hinder the capability of deep‐sea vehicles. As a complement to underwater camera, tactile perception becomes especially important in situations where machine vision is limited. However, tactile sensors utilized in deep sea, which should be able to detect pressure changes of only hundreds of pascals under high hydrostatic pressure, are still lacking. To tackle the challenge imposed by hydrostatic pressure, a simulated deep‐sea environment flexible sensor (SDEFS) is proposed, consisting of a force sensor array and a bending sensor based on hydrogels for tactile sensing in 50 MPa hydrostatic pressure environments. The force sensor is unaffected by the hydrostatic pressure and achieves high sensitivity of 82.62 N−1 under 100 MPa hydrostatic pressure. The SDEFS is utilized to classify objects based on the difference in hardness. It can accurately classify seven objects on the ground, and three objects in an underwater environment with hydrostatic pressure of 50 MPa, with total recognition accuracies of 98.3% and 96%, respectively. With high force measurement sensitivity and accurate recognition ability under water, the SDEFS is expected to provide very valuable haptic sensing and feedback in deep‐sea exploration.

A Dual-Layer Orthogonal-Layout Thin-Film Force Sensor for Digital Orthodontic Functional Appliances

Measuring the force exerted on oral tissue is crucial for orthodontic treatment. Without proper tools and methods, orthodontists must rely on their clinical experience to estimate the force, which limits the efficiency and accuracy of diagnosis and treatment. Previous works using flexible force sensor arrays and wireless technology are power-hungry and require either complex wiring or batteries for power supply. However, human oral nerves are highly sensitive to foreign objects, so these solutions are not suitable for clinical practice. This work presents a thin-film force sensor based on carbon paste on a flexible printed circuit (FPC) substrate with an area of 24×4mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The sensor is embedded in functional appliances and can be read out through a miniature connector by a handheld device that uses frequency-domain signal processing. The device is powered by a 360-mAh battery and can last for 15 hours with a form factor of 86×40×21 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The device achieves a force resolution of 10 mN with an update rate of 10 Hz. Six samples are characterized and after two-point calibration, this system achieves a force accuracy of 1.3/-0.6 N. Our measurement results also show that the sensor is robust against liquid environment. A dual-layer orthogonal-layout method is proposed to check the alignment of the contact surfaces.

A Highly Sensitive Multimodal Tactile Sensing Module with Planar Structure for Dexterous Manipulation of Robots

Herein, a multimodal tactile sensing module that can improve the dexterous manipulation capabilities of robots with parallel grippers is reported. The tactile sensor consists of a 4 × 4 matrix 3‐axis force sensor array (with spacings of 2 mm) and a single‐temperature sensor. The tactile sensor uses inorganic silicon and gold as materials for the detection of strain and temperature and a polymer‐based elastomer to encapsulate the sensing layer. The sensing module is equipped with a readout circuitry for signal processing. Within the measurable force range (≈1.5 N) of the sensor cell, a prototype module exhibits a repeatability error within 2%, a hysteresis error within 3%, and a resolution as small as 10 mN. Furthermore, each sensor cell independently measures 3‐axis forces with a cross‐talk error of approximately 3%. The temperature sensor exhibits linear output properties in the approximate range of 5–75 °C. Experiments are performed by mounting the module on a parallel gripper to grasp a paper cup and a reflex hammer toy. According to the experimental results, the sensor can accurately detect the state of contact with an object by analyzing three tactile modalities (i.e., 3‐axis force distribution, vibration, and temperature).

Printed, wearable e-skin force sensor array

The need to develop electronic skin (e-skin) for robotics is essential to provide a sense of touch on a large area, the same as human skin. This paper reports the design and development of printed e-skin force sensor arrays. The performance analysis of these capacitive force sensor arrays showed a resolution of 2.5 sq. mm, localization accuracy of 99% and a sensitivity of 0.53 pF/N. The sensor patches provided significant force sensitivity and stable loading and unloading for static force. A graphical user interface is developed in open-source python software. An unsupervised machine learning algorithm trains the tactile e-skin system for different user styles to distinguish the cluster of lower and higher touch angles for improving accuracy. The proposed e-skin sensor finds application in large-area sensors and gripper pads for prosthetics in biomedical devices where the sense of touch and high resolution is needed.

Applications of wireless sensor systems to sleep stage estimation for home sleep monitoring

In recent years, research on sleep monitoring and analysis has attracted many scholars. Among them, the polysomnography (PSG) is performed more accurately. However, PSG is not suitable to be used at home due to its complicated operation and expensive cost. On the other hand, although the Pittsburgh sleep quality index (PSQI) is a standardized form for sleep quality assessment, the subjective and backward evaluation may lead to intuitive results. Therefore, this paper is intended to develop a sleep stage estimation system for home health care services. In the proposed platform, the sleep conditions, including the heart rate (HR) and body movement, are collected by an HR monitor and a force sensor array, respectively. Also, the fuzzy inference system is applied to the sleep depth evaluation, and then, the finite state machine is utilised to estimate the sleep stage. Experimental results show that the developed platform not only reduces the burden of PSG measurements, but also provides more convincible and reasonable results, presenting as an assistive tool of the conventional PSQI tests.

Understanding the Effect of Speed on Human Emotion Perception in Mediated Social Touch Using Voice Coil Actuators

Touch as a modality in social communication has been getting more attention with recent developments in wearable technology and an increase in awareness of how limited physical contact can lead to touch starvation and feelings of depression. Although several mediated touch methods have been developed for conveying emotional support, the transfer of emotion through mediated touch has not been widely studied. This work addresses this need by exploring emotional communication through a novel wearable haptic system. The system records physical touch patterns through an array of force sensors, processes the recordings using novel gesture-based algorithms to create actuator control signals, and generates mediated social touch through an array of voice coil actuators. We conducted a human subject study (N= 20) to understand the perception and emotional components of this mediated social touch for common social touch gestures, including poking, patting, massaging, squeezing, and stroking. Our results show that the speed of the virtual gesture significantly alters the participants' ratings of valence, arousal, realism, and comfort of these gestures with increased speed producing negative emotions and decreased realism. The findings from the study will allow us to better recognize generic patterns from human mediated touch perception and determine how mediated social touch can be used to convey emotion. Our system design, signal processing methods, and results can provide guidance in future mediated social touch design.

A Posture Recognition System to Track Drivers’ Activities While Driving

To improve traffic safety, different systems were invented to warn drivers when detecting a conflict. However, drivers may be already aware of such conflicts. In these cases, warning drivers may not effective and may lead drivers to not pay attention to warnings in the case of serious conflicts. The purpose of this study is to develop a novel posture recognition system to detect different driver activities while driving. If drivers are not braking or decelerating in the case of a conflict, they are alerted and notified about the conflict. The proposed system is based on a passive method for measuring bodypostures by using two force sensor arrays to inspect the pressure patterns exhibited in the driver’s seat and backrest. Different sensors' distributions were tested to find the most suitable distribution to improve the accuracy of the recognition. A virtual reality (VR) driving simulation was developed to test the accuracy of recognition in an immersive environment. Experiments were carried out to test the posture recognition accuracy in both realistic and VR settings. The results showed that the system could recognize different postures with high accuracy. Such a system can be used to alert drivers and disengage autonomous mode in the case of self-driving and autonomous vehicles when the situation unsafe.

Influence of eccentric nanoindentation on top surface of silicon micropillar arrays

In this paper we present an investigation of the influence of nanoindentation location on the top surface of silicon micro-pillar. This silicon micro-pillar array which will be employed as a micro force sensor array based on three-dimension silicon (3D Si) structures, is fabricated by near UV nanoimprint lithography (NIL) technique and etched by Cryogenic Inductively Coupled Plasma (ICP) sequentially. To determine its mechanical properties, those micropillars are measured by instrument indentation testing (IIT) to obtain its hardness and reduced modulus. For the measurement, a Berkovich diamond indenter is utilized to penetrate a single and also multiple point indentations on a micro-pillar surface. Afterwards, these results are compared to the indentation at the central point of the tested pillar and Si bulk as its reference to examine the influence of different probing locations on the measured reduced modulus and hardness.

Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography.

In diffuse optical tomography (DOT) and spectroscopy (DOS) using handheld probes, tissue curvature can cause bad fiber-to-tissue contact. Understanding and minimizing image artifacts caused by these coupling errors would significantly improve DOT and DOS image quality. In this work, we utilized Monte Carlo simulations and experiments with gelatin-Intralipid phantoms to systematically study the influence of source or detector (optode) coupling errors. Optode coupling errors can increase the amplitude and decrease the phase of the measured diffuse reflectance, creating artifacts in the reconstructed absorption maps, such as hot spots on the edges. We propose an outlier removal algorithm that can correct these image artifacts, and we demonstrate its performance using simulations, phantom experiments, and breast patient data acquired with bad probe contact due to a dense or small breast. Further, we designed and implemented a new resistance-type thin-film force sensor array that provides real-time optode coupling feedback and guides the outlier removal to minimize optode coupling errors. Our approaches and study results have significant implications for reducing image artifacts arising from handheld probes, which are commonly used with mobile and wearable DOT and DOS devices.

Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumental in enabling this performance. Using a wind-tunnel wing model instrumented with distributed arrays of strain and pressure sensors, we characterized the relationship between the distributed sensor signals and aerodynamic and load-related variables. Estimation approaches based on nonlinear artificial neural networks (ANNs) and linear partial least squares were tested with different combinations of sensor signals. The ANN estimators were accurate and robust, giving good estimates for all variables, even in the stall region when the distributed array pressure and strain signals became unsteady. The linear estimator performed well for load estimates but was less accurate for aerodynamic variables such as angle of attack and airspeed. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase maneuverability and improved control of unmanned aerial vehicles with high degrees of freedom such as highly flexible or morphing wings.

Development of a Soft Pneumatic Actuator System Based on Flexible Force Sensor for Lower Limb Compression Therapy

The use of a soft pneumatic actuator (SPA) in compression therapy to enhance blood circulation shows potential because of its unique proactive bio-mechanical force delivery mode and wide range of pneumatic pressure control. This work presented new insights into the model-experiment method using mathematical simulation and flexible force sensor array (FFSA) to explore the bio-mechanical force delivery mechanism of a newly developed SPA and offer accurate pressure for lower limb compression therapy. Multichambered bladders were incorporated in the SPA system as key components to exert pressure on the lower limb, and the FFSA visualized the skin pressure on a software interface. Pressure signals were transiently collected by differential sensors and FFSA using the data acquisition unit and then transmitted to a computer software via Bluetooth module to build the interface pressure feedback and control subsystem. According to the interface pressure variation, the SPA system could automatically adjust the air pressure in bladders to generate suitable bio-mechanical force for compression therapy. Results revealed that the sensors-based SPA system could generate rhythmic (intermittent) bio-mechanical force, which could be transformed by FFSA and visualized in a morphological interface pressure mapping from quantitative and qualitative perspectives. This study provided an evidence-based technical guidance for a new SPA system design which could delivered controllable and precise dynamic pressure to affect the lower limb during compression therapy.

Self-Synchronized, Continuous Body Weight Monitoring Using Flexible Force Sensors and Ground Reaction Force Signal Processing

Change patterns in body weight are significant indicators of a variety of medical conditions and in some instances, such as congestive heart failure, strong predictors of critical care interventions. The continuous monitoring of body weight can therefore help care givers and patients reduce the risks that are inherent in ignoring body weight changes or in measuring them infrequently. One possible approach to continuous body weight monitoring is to use shoe-integrated sensors. Such approach is however hampered by the lack of patient’s adherence to medical protocols and by the detrimental impact of motion artifacts on weight measurements during body movement. The objective of this paper is to show that both problems can be readily addressed by pushing body weight measurement into the background of the patient’s daily routine and by adapting piezoresistive flexible force sensors to the specific requirements of accurate body weight measurement during motion. The proposed solution is enabled by two major innovations. The first is a sensor array architecture that helps in collecting the total vertical GRF on each foot during the walk cycle. The second is based on extracting unique features from the waveforms of ground reaction forces (GRFs) as measured by the flexible force sensor array. This paper is dedicated to a full and detailed description of this second innovation that includes the extraction of GRF waveform features using real-time signal processing and the elimination of motion artifacts using accurate estimates of the vertical acceleration of the body’s center of mass. A prototype demo system of the proposed solution is described along with testing results using a human subject in various walk conditions. The prototype is comprised of three subsystems: the shoe-integrated flexible sensor array, a sentinel subsystem implemented as a wearable smart watch, and a communication subsystem between sensors and sentinel. The prototype is distinguished with several unique features that ensures its wearability and low cost, including its self synchronizing data collection and the absence of any inertial motion units.

A Force and Temperature Sensor Array Based on 3-D Field-Coupled Thin-Film Transistors for Tactile Intelligence

Tactile sensors that acquire force and temperature information are key to machine tactile intelligence. In this article, an active tactile sensor incorporating an array of 3-D field-coupled thin-film transistors (FCTFTs) with a $4 \times 4$ pixelated polyvinylidene fluoride (PVDF)-based force and temperature sensors is studied. In order to enhance sensitivity and reduce power consumption, a 3-D FIN-shaped FCTFT is designed and a subthreshold operation is implemented. We have demonstrated its functions in human–machine interfacing, object characterization and recognition, force and temperature mapping, and dynamic force tracing. Such a tactile sensor array is promising for applications in machine tactile intelligence and humanoid robotics.

An Instrumented Urethral Catheter with a Distributed Array of Iontronic Force Sensors.

This paper develops a novel instrumented urethral catheter with an array of force sensors for measuring the distributed pressure in a human urethra. The catheter and integrated portions of the force sensors are fabricated by the use of 3D printing using a combination of both soft and hard polymer substrates. Other portions of the force sensors consisting of electrodes and electrolytes are fabricated separately and assembled on top of the 3D-printed catheter to create a soft flexible device. The force sensors use a novel supercapacitive (iontronic) sensing mechanism in which the contact area between a pair of electrodes and a paper-based electrolyte changes in response to force. This provides a highly sensitive measure of force that is immune to parasitic noise from liquids. The developed catheter is tested using a force calibration test rig, a cuff-based pressure application device, an extracted bladder and urethra from a sheep and by dipping inside a beaker of water. The force sensors are found to have a sensitivity of 30-50 nF/N, which is 1000 times larger than that of traditional capacitive force sensors. They exhibit negligible capacitance change when dipped completely in water. The pressure cuff tests and the extracted sheep tissue tests also verify the ability of the sensor array to work reliably in providing distributed force measurements. The developed catheter could help diagnose ailments related to urinary incontinence and inadequate urethral closure pressure.

Soft and flexible sensor array using carbon black pillars for object recognition via pressure mapping

Soft and flexible arrays of pressure or force sensors are particularly useful for wearable and robotic applications. This article presents a soft and flexible sensor array made from carbon black composite pillars. These pillars allows more sensing elements to be constructed in a given planar area. A prototype sensor consisting of a 3 × 3 array of carbon black composite pillars is used to demonstrate their potential. Each pillar shows a reliable corresponding change in resistance with instantaneous pressure or force applied on it. With a sensor array, the pressure distribution across the contact surface of the sensor can be represented as a pressure map allowing the recognition of the shape of an object applying the pressure on the sensor array. The soft and flexible nature of the sensor array allows it to be easily deployed and enables prediction of the shape of an object through the 3D pressure map.

Design, Analysis and Experiment of a Tactile Force Sensor for Underwater Dexterous Hand Intelligent Grasping.

This paper proposes a novel underwater dexterous hand structure whose fingertip is equipped with underwater tactile force sensor (UTFS) array to realize the grasping sample location determination and force perception. The measurement structure, theoretical analysis, prototype development and experimental verification of the UTFS are purposefully studied in order to achieve accurate measurement under huge water pressure influence. The UTFS is designed as capsule shape type with differential pressure structure, and the external water pressure signal is separately transmitted to the silicon cup bottom which is considered to be an elastomer with four strain elements distribution through the upper and lower flexible contacts and the silicone oil filled in the upper and lower cavities of UTFS. The external tactile force information can be obtained by the vector superposition between the upper and lower of silicon cup bottom to counteract the water pressure influence. The analytical solution of deformation and stress of the bottom of the square silicon cup bottom is analyzed with the use of elasticity and shell theory, and compared with the Finite Element Analysis results, which provides theoretical support for the distribution design of four strain elements at the bottom of the silicon cup. At last, the UTFS zero drift experiment without force applying under different water depths, the output of the standard force applying under different water depth and the test of the standard force applying under conditions of different 0 C–30 C temperature with 0.1 m water depth are carried out to verify the performance of the sensor. The experiments show that the UTFS has a high linearity and sensitivity, and which has a regular zero drift and temperature drift which can be eliminated by calibration algorithm.

Mechanically activated ion channels.

The ability of cells to sense and respond to their physical environment is fundamental to a broad spectrum of biological processes. Cells express an array of force sensors that can transduce mechanical inputs into biochemical signals, including the mechanically activated ion channels PIEZO1, PIEZO2 and TRPV4. The identification and characterisation of mechanically activated ion channels has proven challenging, as has identifying their mode of activation and regulation in vivo. However, the diverse channelopathies associated with the known channels highlights their physiological importance.

Modeling Touch and Palpation Using Autoregressive Models.

The human haptic system uses a set of reproducible and subconscious hand maneuvers to identify objects. Similar subconscious maneuvers are used during medical palpation for screening and diagnosis. The goal of this work was to develop a mathematical model that can be used to describe medical palpation techniques. Palpation data were measured using a two-dimensional array of force sensors. A novel algorithm for estimating the hand position from force data was developed. The hand position data were then modeled using multivariate autoregressive models. Analysis of these models provided palpation direction and frequency as well as palpation type. The models were tested and validated using three different data sets: simulated data, a simplified experiment in which participant followed a known pattern, and breast simulator palpation data. Simulated data showed that the minimal error in estimating palpation direction and frequency is achieved when the sampling frequency is five to ten times the palpation frequency. The classification accuracy was for the simplified experiment and for the breast simulator data. Proper palpation is one of the vital components of many hands-on clinical examinations. In this study, an algorithm for characterizing medical palpation was developed. The algorithm measured palpation frequency and direction for the first time and provided classification of palpation type. These newly developed models can be used for quantifying and assessing clinical technique, and consequently, lead to improved performance in palpation-based exams. Furthermore, they provide a general tool for the study of human haptics.