Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors.

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

Combining SWNT and Graphene in Polymer Nanofibers: A Route to Unique Carbon Precursors for Electrochemical Capacitor Electrodes.

It is important to fabricate nanostructured architectures comprised of functional components for a wide variety of applications because precise structural control in the nanometer regime can yield unprecedented, fascinating properties. Owing to their well-defined microstructural characteristics, it has been popular to use carbon nanospecies, such as nanotubes and graphene, in fabricating nanocomposites and nanohybrids. Nevertheless, it still remains hard to control and manipulate nanospecies for specific applications, thus preventing their commercialization. Herein, first, we report unique one-dimensional nanoarchitectures with meso-/macropores, consisting of single-walled nanotubes (SWNTs), graphene, and polyacrylonitrile, in which poly(vinyl alcohol) was employed as a dispersing agent and sacrificial porogen. One-dimensional SWNTs and two-dimensional graphene pieces were combined in the confined interior space of electrospun nanofibers, which led to unique microstructural characteristics such as enhanced ordering of SWNTs, graphene pieces, and polymer chains in the nanofiber interior. Next, the SWNT/graphene-in-polymer nanofiber (SGPNF) structures were converted into carbonized products (SGCNFs) with effective porosity and tunable electrochemical properties. Similar to SGPNFs, the microstructural and electrical properties of the SGCNFs depended on the incorporated amount of SWNT and graphene. At higher SWNT content, the mesopore volume proportion and specific discharge capacitance of the SGCNFs increased by max. 63 and 598%, respectively. The SGCNFs showed strong potential as a high-performance electrode material for electrochemical capacitors (max. capacitance: nonactivated ∼390 F g-1 and activated ∼750 F g-1). Flexible, all solid-state capacitor cells based on SGCNFs were also successfully demonstrated as a model application. The SGCNFs can be further functionalized by various methods, which will impart attractive properties for extended applications.

A Solution-Processable, Nanostructured, and Conductive Graphene/Polyaniline Hybrid Coating for Metal-Corrosion Protection and Monitoring.

A smart and effective anticorrosive coating consisting of alternating graphene and polyaniline (PANI) layers was developed using top-down solution processing. Graphite was exfoliated using sonication assisted by polyaniline to produce a nanostructured, conductive graphene/polyaniline hybrid (GPn) in large quantities (>0.5 L of 6 wt% solution in a single laboratory-scale process). The GPn was coated on copper and exhibited excellent anticorrosion protection efficiencies of 46.6% and 68.4% under electrochemical polarization in 1 M sulfuric acid and 3.5 wt% sodium chloride solutions, chosen as chemical and seawater models, respectively. Impedance measurements were performed in the two corrosive solutions, with the variation in charge transfer resistance (R ct) over time indicating that the GPn acted as an efficient physical and chemical barrier preventing corrosive species from reaching the copper surface. The GPn-coated copper was composed of many PANI-coated graphene planes stacked parallel to the copper surface. PANI exhibits redox-based conductivity, which was facilitated by the high conductivity of graphene. Additionally, the GPn surface was found to be hydrophobic. These properties combined effectively to protect the copper metal against corrosion. We expect that the GPn can be further applied for developing smart anticorrosive coating layers capable of monitoring the status of metals.

Nanostructured mesophase electrode materials: modulating charge-storage behavior by thermal treatment.

3D nanostructured carbonaceous electrode materials with tunable capacitive phases were successfully developed using graphene/particulate polypyrrole (PPy) nanohybrid (GPNH) precursors without a separate process for incorporating heterogeneous species. The electrode material, namely carbonized GPNHs (CGPNHs) featured a mesophase capacitance consisting of both electric double-layer (EDL) capacitive and pseudocapacitive elements at the molecular level. The ratio of EDL capacitive element to pseudocapacitive element (E-to-P) in the mesophase electrode materials was controlled by varying the PPy-to-graphite weight (Pw/Gw) ratio and by heat treatment (TH), which was demonstrated by characterizing the CGPNHs with elemental analysis, cyclic voltammetry, and a charge/discharge test. The concept of the E-to-P ratio (EPR) index was first proposed to easily identify the capacitive characteristics of the mesophase electrode using a numerical algorithm, which was reasonably consistent with the experimental findings. Finally, the CGPNHs were integrated into symmetric two-electrode capacitor cells, which rendered excellent energy and power densities in both aqueous and ionic liquid electrolytes. It is anticipated that our approach could be widely extended to fabricating versatile hybrid electrode materials with estimation of their capacitive characteristics.