A self-adhesive, elastic fabric, nanocomposite skin-strain sensor (called Motion Tape) has been developed, tested in controlled laboratory environments, and validated through human subject studies. This study aimed to interpret Motion Tape data using deep learning methods to directly predict functional movement parameters (e.g., joint angles and limb positions) and verifying the results using optical motion capture. The approach was to obtain human participant Motion Tape testing data and training the datasets using ground truth values acquired from the optical motion capture system. Predictions of muscle engagement, strain, and range-of-movement of major joints were investigated to validate the proposed methods.
Ambient environmental effects can often interfere with transducers and affect the accuracy of sensor readings. A typical method of compensating for temperature in strain measurements is to construct a full-bridge Wheatstone circuit. The objective of this study was to design patterned nanocomposites deposited onto fabric substrates to form a materials-based Wheatstone bridge circuit that could automatically compensate for unwanted ambient effects. Motion Tape (i.e., a self-adhesive, elastic-fabric-based nanocomposite sensor) specimens with patterned nanocomposite elements and conductive traces were designed to form a full-bridge circuit. The results showed that their strain sensing response was not adversely affected by concurrent temperature effects.
Contact pressure sensing and pressure mapping are commonly used in many automotive, healthcare, industrial, and robotics applications. Many commercially available pressure mapping solutions use a dense array of transducers embedded in a pad. However, the pad is relatively thick with noticeable rigid components, and high-resolution pressure mapping systems can be complex, cumbersome, bulky, expensive, and not portable. Thus, the objective of this study was to validate pressure mapping using a commercial Smartfoam interrogated using an electrical impedance tomography (EIT) measurement strategy and algorithm. In addition, the sensitivity of the Smartfoam was enhanced by depositing on its surface a piezoresistive carbon nanotube-based thin film. EIT electrodes were installed along the foam boundaries, thereby eliminating the need for any electrodes or rigid objects on the foam and pressure mapping surface. A custom data acquisition system was employed to apply electrical current excitations while measuring the boundary voltage response across pairs of boundary electrodes. The boundary voltage datasets were used for solving the EIT inverse problem to reconstruct the conductivity distribution of the specimen. Controlled pressure mapping tests were performed by placing different weights of varying contact areas on different positions of the nanocomposite Smartfoam. The EIT results confirmed that the nanocomposite Smartfoam could resolve pressure hotspots at different locations, as well as different magnitudes of contact pressure applied. Real-time pressure mapping was successfully demonstrated, while pressure mapping resolution and accuracy were also characterized. Overall, the system is lightweight, low-profile, and does not use rigid components on the foam surface. Future work will align this method for targeted consumer and healthcare applications.
Quantitative measurements of human movements can drastically change how coaches, trainers, and clinicians tailor physical training, teach new athletic skills, and prescribe treatment for musculoskeletal injuries, such as ankle sprains. The gold standard for movement characterization today is optical motion capture, which uses an array of fixed high-resolution cameras to track markers mounted on a moving body. However, optical motion capture is inconvenient outside a laboratory and susceptible to movement artifacts such as skin and clothing/shoe deformation. Thus, this study aimed to develop a field-deployable, skin-mounted, skin-strain sensor that can accurately quantify skin strains while measuring muscular engagement during functional movements. The approach was to directly integrate piezoresistive graphene nanocomposites with commercial kinesiology tape to form a self-adhesive skin-strain sensor that could be mounted virtually anywhere on the body, such as the ankle-foot complex. Unlike optical motion capture and electronic textiles, “Motion Tape” can be worn underneath garments and within the shoe, directly measure skin-strains, and are insensitive to movement artifacts. This work began with fabricating Motion Tape using a scalable spray-coating method. The cyclic strain sensing properties were characterized through extensive load frame tests. Then, controlled experiments using test coupons and human studies were performed to compare the Motion Tape sensing response versus optical motion capture during a series of representative movements. Besides showing comparable sensing results, densely distributed skin-strain monitoring using Motion Tape was demonstrated using an electrical impedance tomography measurement strategy and algorithm. The distributed strains induced during dorsiflexion and plantarflexion of the ankle-foot complex were successfully characterized.
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