From Sensors to Care: How Robotic Skin Is Transforming Modern Healthcare-A Mini Review

In recent years, robotics has made notable progress, becoming an essential component of daily life by facilitating complex tasks and enhancing human experiences. While most robots have traditionally featured hard surfaces, the growing demand for more comfortable and safer human-robot interactions has driven the development of soft robots. One type of soft robot, which incorporates innovative skin materials, transforms rigid structures into more pliable and adaptive forms, making them better suited for interacting with humans. Especially in healthcare and rehabilitation, robotic skin technology has gained substantial attention, offering transformative solutions for improving the functionality of prosthetics, exoskeletons, and companion robots. Although replicating the complex sensory functions of human skin remains a challenge, ongoing research in soft robotics focuses on developing sensors that mimic the softness and tactile sensitivity necessary for effective interaction. This review provides a narrative analysis of current trends in robotic skin development, specifically tailored for healthcare and rehabilitation applications, including skin types of sensor technologies, materials, challenges, and future research directions in this rapidly developing field.

Carbon-Based Piezoresistive Polymer Nanocomposites by Extrusion Additive Manufacturing: Process, Material Design, and Current Progress

Advancement in additive manufacturing (AM) allows the production of nanocomposites with complex and custom geometries not typically allowable with conventional manufacturing techniques. The benefits of AM have led to recent interest in producing multifunctional materials capable of being printed with current AM technologies. In this article, piezoresistive composites realized by AM and the matrices and fillers utilized to make such devices are introduced and discussed. Carbon-based nanoparticles (Carbon Nanotubes, Graphene/Graphite, and Carbon Black) are often the filler choice of most researchers and are heavily discussed throughout this review in combination with extrusion AM methods. Piezoresistive applications such as physiological and wearable sensors, structural health monitoring, and soft robotics are presented with an emphasis on material and AM selection to meet the demands of such applications.

Investigating the Electromechanical Properties of Carbon Black-Based Conductive Polymer Composites via Stochastic Modeling

Conductive polymer composites (CPCs) have shown potential for structural health monitoring applications based on repeated findings of irreversible transducer electromechanical property change due to fatigue. In this research, a high-fidelity stochastic modeling framework is explored for predicting the electromechanical properties of spherical element-based CPC materials at bulk scales. CPC dogbone specimens are manufactured via casting and their electromechanical properties are characterized via uniaxial tensile testing. Model parameter tuning, demonstrated in previous works, is deployed for improved simulation fidelity. Modeled predictions are found in agreement with experimental results and compared to predictions from a popular analytical model in the literature.

Data Glove with Self-Compensation Mechanism Based on High-Sensitive Elastic Fiber-Optic Sensor

With the development of virtual reality (VR) interaction technology, data glove has become one of the most popular devices for human-computer interaction. It's valuable to design high-sensitive and flexible sensor for data glove. Therefore, a low-cost data glove based on self-compensating elastic optical fiber sensor with self-calibration function is proposed. The tunable and stretchable elastic fiber was fabricated by a simple, economical and controllable method. The fiber has good flexibility and high stability under stretching, bending and indentation deformation. The optical fibers are installed in the sensor in a U shape with a bending radius of 5 mm. Compared with the straight fiber, the response sensitivity of the U-shaped fiber to deformation is increased by about 7 times at most. The reference optical fiber is connected to the sensor, which effectively improves the stability and accuracy of the sensor system. In addition, the sensors are easy to install so that the data gloves can be customized for different hand shapes. In the gesture capture test, it can respond quickly and guide the manipulator to track the gesture. This responsive and stable data glove has broad development potential in motion monitoring, telemedicine and human-computer interaction.

Multi-Layered Carbon-Black/Elastomer-Composite-Based Shielded Stretchable Capacitive Sensors for the Underactuated Robotic Hand

Soft and flexible strain sensors are becoming popular for many robotic applications. This article presents a stretchable capacitive sensor by combining a conductive filler of carbon black with elastomers and implementing shielding to reduce parasitic interference, applied to an underactuated robotic hand. Sensors with different configurations were explored. The results show that a shield introduced to the sensor does have some mitigation effect on external interference. Two sensor configurations were explored: longitudinal interdigitated capacitive (LIDC) sensor, where the interdigitated fingers lie along the same axis as the strain, and transverse interdigitated capacitive (TIDC) sensor, where the interdigitated fingers are orthogonal to the strain direction. The LIDC configuration had better performance than TIDC. The fabricated two-layered LIDC sensor had a gage factor of 0.15 pF/mm and the rates of capacitive creep of 0.000667 pF/s and 0.001 pF/s at loads of 120 g and 180 g, respectively. The LIDC sensors attached to an underactuated robotic hand demonstrate the sensors’ ability to determine the bending angles of the proximal interphalangeal (PIP) and metacarpophalangeal (MCP) joints.

Data Glove Using Soft and Stretchable Piezoresistive Sensors

This research investigates the design and implementation of elastomer-based piezoresistive strain sensors and applies them to a data glove to demonstrate their application. The piezoresistive strain sensors are made by mixing Ecoflex 00-30 and carbon-black nanoparticles and then using stencil and doctor blading to deposit the piezoresistive traces as a mass fabrication technique. The primary objective is to integrate two sensing piezoresistive elements as one single-piece sensor that detects the bending angles of the metacarpophalangeal and proximal interphalangeal joints of each finger. Using a unique zig-zag pattern allows to selectively mask any unwanted piezoresistive sensing. The sensor has a gage factor of 0.68. Experiments conducted have demonstrated that the use of these soft, flexible, and stretchable piezoresistive sensors is repeatable and viable sensors for data-glove and has the potential for other wearable applications.

Self-Healing, Self-Adhesive Strain Sensors Made with Carbon Nanotubes/Polysiloxanes Based on Unsaturated Carboxyl-Amine Ionic Interactions

Strain sensors with high sensitivity, long-term durability, and stretchability are required for flexible and wearable electronic devices. This paper reports a bilayer strain sensor consisting of carboxyl-functionalized carbon nanotubes (CNTs) and ionically crosslinked polysiloxane substrates based on unsaturated acid-amine interactions. Vacuum filtration was adopted to prepare the CNT films (2.74-4.70 μm in thickness) onto the polysiloxane substrates to prepare stretchable conductive strain sensors. The strain sensor exhibited self-healing ability, self-adhesiveness, high sensitivity, linearity, low hysteresis, and long-term durability with a gauge factor of 33.99 at 55% strain. The sensitivity and linearity could be adjusted by the thickness of the CNT layer. A crack-related mechanism was proposed in which increasing the thickness of the CNT layer led to simultaneously enhanced sensitivity and linearity. Finally, we investigated the detection of human activities (bending/unbending of fingers or knees) and subtle motions (coughing and swallowing). The fabricated strain sensor succeeded in meeting various needs with satisfactory sensing performance.

Stretch Sensor-Based Facial Expression Recognition and Classification Using Machine Learning

Sensor-based Facial expression recognition (FER) is an attractive research topic. Nowadays, FER is used for different application such as smart environments and healthcare solutions. The machine can learn human emotion by using FER technology. It is the primary and essential for quantitative analysis of human sentiments. FER is an image recognition problem within the broader field of computer vision. Face detection and tracking, reliable face recognition still present a considerable challenge for researchers in computer vision and pattern recognition. First, data processing and analytics are intensive and require a large number of computation resources and memory. Second, the fundamental technical limitation is its robustness in changes in the environment. Finally, illumination variation further complicates the design of robust algorithms because of changes in shadow casts. However, sensor-based FER overcomes all these limitations. Sensor technologies, especially low-power, wireless communication, high-capacity, and data processing have made substantial progress, making it possible for sensors to evolve from low-level data collection and transmission to high-level inference. This study aims to develop a stretchable sensor-based FER system. We use random forest machine learning algorithms used for training the FER model. Commercial stretchable facial expression dataset is simulated into the anaconda software. In this research, our stretch sensor FER dataset obtained around 95% accuracy for four different emotions (Neutral, Happy, Sad, and Disgust).

Fully Implantable Plantar Cutaneous Augmentation System for Rats Using Closed-loop Electrical Nerve Stimulation

Plantar cutaneous feedback plays an important role in stable and efficient gait, by modulating the activity of ankle dorsi- and plantar-flexor muscles. However, central and peripheral nervous system trauma often decrease plantar cutaneous feedback and/or interneuronal excitability in processing the plantar cutaneous feedback. In this study, we tested a fully implantable neural recording and stimulation system augmenting plantar cutaneous feedback. Electromyograms were recorded from the medial gastrocnemius muscle for stance phase detection, while biphasic stimulation pulses were applied to the distal-tibial nerve during the stance phase to augment plantar cutaneous feedback. A Bluetooth low energy and a Qi-standard inductive link were adopted for wireless communication and wireless charging, respectively. To test the operation of the system, one intact rat walked on a treadmill with the electrical system implanted into its back. Leg kinematics were recorded to identify the stance phase. Stimulation was applied, with a 250-ms onset delay from stance onset and 200-ms duration, resulting in the onset at 47.58 ± 2.82% of stance phase and the offset at 83.49 ± 4.26% of stance phase (Mean ± SEM). The conduction velocity of the compound action potential (31.2 m/s and 41.6 m/s at 1·T and 2·T, respectively) suggests that the evoked action potential was characteristic of an afferent volley for cutaneous feedback. We also demonstrated successful wireless charging and system reset functions. The experimental results suggest that the presented implantable system can be a valuable neural interface tool to investigate the effect of plantar cutaneous augmentation on gait in a rat model.

Soft Inductive Coil Spring Strain Sensor Integrated with SMA Spring Bundle Actuator

This study proposes a soft inductive coil spring (SICS) strain sensor that can measure the strain of soft actuators. The SICS sensor, produced by transforming a shape memory alloy (SMA) wire with the same materials as that of an SMA spring bundle actuator (SSBA) into a coil spring shape, measures inductance changes according to length changes. This study also proposes a manufacturing method, output characteristics of the SICS sensor applicable to the SSBA among soft actuators, and the structure of the SICS sensor-integrated SSBA (SI-SSBA). In the SI-SSBA, the SMA spring bundle and SICS sensor have structures corresponding to the muscle fiber and spindle of the skeletal muscle, respectively. It is demonstrated that when a robotic arm with one degree of freedom is operated by attaching two SI-SSBAs in an antagonistic structure, the displacement of the SSBA can be measured using the proposed strain sensor. The output characteristics of the SICS sensor for the driving speed of the robotic arm were evaluated, and it was experimentally proven that the strain of the SSBA can be stably measured in water under a temperature change of 54 °C from 36 to 90 °C.

Multifunctional Ultrahigh Sensitive Microwave Planar Sensor to Monitor Mechanical Motion: Rotation, Displacement and Stretch

This paper presents a novel planar multifunctional sensor that is used to monitor physical variations in the environment regarding distance, angle, and stretch. A double split-ring resonator is designed at 5.2 GHz as the core operating sensor. Another identical resonator is placed on top of the first one. The stacked configuration is theoretically analyzed using an electric circuit model with a detailed parameter extraction discussion. This design is first employed as a displacement sensor, and a compelling high sensitivity of 500 MHz/mm is observed for a wide dynamic range of 0-5 mm. Then, in another configuration, the stacked design is used as a rotation sensor that results in a high sensitivity of 4.5 MHz/° for the full range of 0-180°. In addition, the stacked resonator is utilized as a strain detector, and a 0–30% stretch is emulated with a linear sensitivity of 12 MHz/%. Measurements are well in congruence with simulated results, which proves the accurate functionality of the sensor in tracking mechanical deformations, all in a single compact contraption.

Review of functional materials for potential use as wearable infection sensors in limb prostheses

The fundamental goal of prosthesis is to achieve optimal levels of performance and enhance the quality of life of amputees. Socket type prostheses have been widely employed despite their known drawbacks. More recently, the advent of osseointegrated prostheses have demonstrated potential to be a better alternative to socket prosthesis eliminating most of the drawbacks of the latter. However, both socket and osseointegrated limb prostheses are prone to superficial infections during use. Infection prone skin lesions from frictional rubbing of the socket against the soft tissue are a known problem of socket type prosthesis. Osseointegration, on the other hand, results in an open wound at the implant-stump interface. The integration of infection sensors in prostheses to detect and prevent infections is proposed to enhance quality of life of amputees. Pathogenic volatiles having been identified to be a potent stimulus, this paper reviews the current techniques in the field of infection sensing, specifically focusing on identifying portable and flexible sensors with potential to be integrated into prosthesis designs. Various sensor architectures including but not limited to sensors fabricated from conducting polymers, carbon polymer composites, metal oxide semiconductors, metal organic frameworks, hydrogels and synthetic oligomers are reviewed. The challenges and their potential integration pathways that can enhance the possibilities of integrating these sensors into prosthesis designs are analysed.

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.

Highly Stretchable Capacitive Sensor with Printed Carbon Black Electrodes on Barium Titanate Elastomer Composite

Wearable electronics and soft robotics are emerging fields utilizing soft and stretchable sensors for a variety of wearable applications. In this paper, the fabrication of a highly stretchable capacitive sensor with a printed carbon black/Ecoflex interdigital capacitor is presented. The highly stretchable capacitive sensor was fabricated on a substrate made from barium titanate⁻EcoflexTM 00-30 composite, and could withstand stretching up to 100%. The designed highly stretchable capacitive sensor was robust, and showed good repeatability and consistency when stretched and relaxed for over 1000 cycles.