Pressure sensors are widely used across engineering sectors, from monitoring load transfer in structural assemblies to ensuring uniform consolidation pressures during composite manufacturing. As composite systems increasingly move toward intelligent and multifunctional capabilities, there is a growing need for sensing approaches that are lightweight, flexible, and compatible with fibrous material architectures. Conventional electronic pressure sensors are often rigid or difficult to integrate within nonwoven and textile-based composite systems. This work presents advances in transforming commercially available nonwoven fabrics into multifunctional, sensing-enabled materials by coating them with carbon-based nanocomposites using a scalable, water-based electrophoretic deposition (EPD) process. Aqueous EPD of functionalized carbon nanotubes onto aramid, polyester, and glass nonwovens produces conformal, micrometer-scale porous nanocomposite coatings that impart electrical conductivity while preserving the inherent compliance and porosity of the fabric. Deposition parameters such as applied electric field, deposition time, and nanotube concentration are used to tune coating morphology and electromechanical response, with processing approaches demonstrated at batch and pilot scales and compatible with continuous textile manufacturing workflows. The resulting nonwoven sensors exhibit a broad and continuous pressure sensing response spanning tactile loads (<1 kPa), vacuum and contact pressures (~100 kPa), and high pressures on the order of tens of megapascals. This ultrawide sensing range arises from multiscale sensing.

Upper limb impairment significantly impacts daily activities and quality of life. Traditional robotic systems have been widely used in neurological rehabilitation applications. However, its adoption has been limited to laboratory and clinical settings due to cost constraints. Our study aimed to assess the feasibility and usability of a cost-effective virtual reality (VR) system for home-based upper limb training. We used a customized wearable sleeve sensor to assess the hand and elbow joint movements objectively. A pilot user study (n = 16) with healthy participants involved evaluating system usability, task load, and presence within two conditions of VR alone and VR combined with a customized inverse kinematics robot arm (KinArm). Results of statistical analysis using a two-way repeated measure (ANOVA) revealed no significant difference between conditions in task completion time. However, significant differences were observed in the normalized number of mistakes and recorded elbow joint angles between tasks. Our findings highlight the potential advantages of an immersive and multi-sensory approach towards performance assessment. This study explores avenues for the development of potentially cost-effective, tailored, and engaging environments for home-based therapy applications.

Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements.

In this work, we propose a creative VR therapy exergame with multi-dimensional reaching tasks for upper extremity rehabilitation. Our system tracks data from the upper extremities using VR hand controllers and a flexible carbon nanotube sensor positioned on the elbow. We conducted a preliminary study (n = 12, 7 F) to evaluate the exergame's therapeutic factors, including orientation (horizontal, vertical), configuration (flat, curved), and user experience. The results show a statistically significant difference in task completion time for the variable orientation, but no significance for the number of mistakes. For the resistance change generated from the carbon nanotube sleeve, the flat configuration in the vertical orientation significantly induced more elbow stretches than the other conditions. These results suggest that VR therapy can be customized to the patient without a change in the intensity and induced body stretch. Our proposed VR exergame has the potential to personalize upper extremity home-based therapy using multi-modal sensory data collection.

Abstract This study presents a new wearable insole pressure sensor (IPS), composed of fabric coated in a carbon nanotube-based composite thin film, and validates its use for quantifying ground reaction forces (GRFs) during human walking. Healthy young adults (n = 7) walked on a treadmill at three different speeds while data were recorded simultaneously from the IPS and a force plate (FP). The IPS was compared against the FP by evaluating differences between the two instruments under two different assessments: (1) comparing the two peak forces at weight acceptance and push-off (2PK) and (2) comparing the absolute maximum (MAX) of each gait cycle. Agreement between the two systems was evaluated using the Bland–Altman method. For the 2PK assessment, the group mean of differences (MoD) was −1.3 ± 4.3% body weight (BW) and the distance between the MoD and the limits of agreement (2S) was 25.4 ± 11.1% BW. For the MAX assessment, the average MoD across subjects was 1.9 ± 3.0% BW, and 2S was 15.8 ± 9.3% BW. The results of this study show that this sensor technology can be used to obtain accurate measurements of peak walking forces with a basic calibration and consequently open new opportunities to monitor GRF outside of the laboratory.

Stroke patients often experience upper limb impairments that restrict their mobility and daily activities. Physical therapy (PT) is the most effective method to improve impairments, but low patient adherence and participation in PT exercises pose significant challenges. To overcome these barriers, a combination of virtual reality (VR) and robotics in PT is promising. However, few systems effectively integrate VR with robotics, especially for upper limb rehabilitation. This work introduces a new virtual rehabilitation solution that combines VR with robotics and a wearable sensor to analyze elbow joint movements. The framework also enhances the capabilities of a traditional robotic device (KinArm) used for motor dysfunction assessment and rehabilitation. A pilot user study (n = 16) was conducted to evaluate the effectiveness and usability of the proposed VR framework. We used a two-way repeated measures experimental design where participants performed two tasks (Circle and Diamond) with two conditions (VR and VR KinArm). We observed no significant differences in the main effect of conditions for task completion time. However, there were significant differences in both the normalized number of mistakes and recorded elbow joint angles (captured as resistance change values from the wearable sleeve sensor) between the Circle and Diamond tasks. Additionally, we report the system usability, task load, and presence in the proposed VR framework. This system demonstrates the potential advantages of an immersive, multi-sensory approach and provides future avenues for research in developing more cost-effective, tailored, and personalized upper limb solutions for home therapy applications.

Functionalized carbon nanotubes are deposited using an aqueous electrophoretic deposition process on everyday fabrics to create flexible wearable sensors, with ultrahigh sensitivity to detect human movements, from arm flexing to finger bending.

Carbon nanotube (CNT) composite films are deposited onto stretchable knit fabrics using electrophoretic deposition (EPD) and dip-coating techniques, which are industrially scalable processes for producing future wearable sensors. The deposited CNTs create an electrically conductive nanocomposite film on the surface of the fibers. These nanocomposite coated fabrics exhibit piezoresistive properties; under mechanical deformation/stretching, a large change in the electrical resistance is observed. Polyethyleneimine (PEI) functionalized carbon nanotubes deposited using EPD create a uniform, extremely thin porous coating on the fiber. Initial results show ultrahigh sensitivity of the carbon nanotube coated fabric when tested on elbow/knee to detect range of motion. The sensitivity of these sensors is exceptionally high when compared to a typical carbon nanotube-based polymer nanocomposite. The nanocomposite coating does not affect fabric's breathability or flexibility, making the sensor comfortable to wear. Because of these unique properties, tremendous potential exists for their use in functional/smart garments. Changes in electrical resistance for these fabrics are influenced by a combination of electron tunneling between the carbon nanotubes and the microstructure of the fabric. To investigate and characterize the unique sensing mechanism, the nanotube coated knit fabric's electromechanical response is studied at different length scales, from individual yarns to fabric levels. For applications in wearable sensors, the durability of the nanotube coating on the fabric is critical for repeatable and reliable sensing response. Durability testing of the sensing fabric for washing loads was conducted to study the nanotube coating's robustness. CNT coating's adhesion quality is evaluated based on the weight loss in the specimen and loss in electrical conductivity in each wash cycle. This research addresses the potential of these sensors for functional/smart garments by examining the underlying mechanism of the sensor response and the durability of the carbon nanotube coating.

Highly sensitive stretch sensors are developed by coating knitted fabrics with carbon nanotubes. An innovative electrophoretic deposition approach is used to deposit a thin and conformal carbon nanotube coating on a nylon-polyesterspandex knitted fabric. The carbon nanotube coating is chemically bonded on the surface of the fibers and creates an electrically conductive network. As a result, these sensors display piezoresistivity; that is, the resistance of the sensor changes due to mechanical deformation. First, the sensing response under tension is characterized using mechanical testing equipment. The sensors are then integrated into compression knee sleeves to investigate sensing response due to knee flexion. When the sensing fabric is stretched, an increase in electrical resistance is observed due to change in the microstructure of the knitted fabric and because of the piezoresistivity of the coating. Under knee flexion, a resistance change of over three thousand percent is detected. The carbon nanotube coated knitted fabrics as flexible stretch sensors have wide-ranging applications in human motion analysis.

Traditionally, carbon nanotubes (CNTs) are integrated with textiles using chemical vapor deposition. Due to the high processing temperature, the use of conventional fibers such as polyester and nylon is challenging. In this research, we use electrophoretic deposition (EPD) to create a thin electrically conductive film of a nanostructured composite consisting of carbon nanotubes functionalized with a dendritic polymer polyethyleneimine (PEI). Different types of fabrics such as cotton, wool, nylon, polyester and aramid can be coated with carbon nanotubes. EPD is inherently scalable because it is performed at room temperature without using any harsh chemicals or volatile solvents. This research is focused on the development of flexible and low-cost wearable technology that can be used to create functional fabrics and smart footwear. Typically, human motion is analyzed using instrumented treadmills and motion capture cameras. Their extremely high cost and complexity make them prohibitive for large scale commercial use. Additionally, the patient/subject can be monitored only for a limited amount of time and not in their natural work/home environment. As a result, a critical need exists for low-cost, comfortable and flexible wearable sensors for human motion analysis. For developing a flexible pressure sensor, carbon nanotubes are deposited on a non-woven aramid veil with randomly oriented fibers. The pressure sensor displays a large in-plane change in electrical conductivity with applied out-of-plane pressure. Upon compression, the number of fiber-fiber contact points between the conductive carbon nanotube coated aramid fibers increases which leads to a decrease in the electrical resistance. The pressure sensors can detect a wide range of pressures from tactile (<10 kPa) to thousands of pounds (~40 MPa). Preliminary experiments of integrating a pressure sensor in the heel of footwear have shown promising results.