Conductive polymers (CPs) are generally insoluble, and developing hydrophilic CPs is significant to broaden the applications of CPs. In this work, a mussel-inspired strategy was proposed to construct hydrophilic CP nanoparticles (CP NPs), while endowing the CP NPs with redox activity and biocompatibility. This is a universal strategy applicable for a series of CPs, including polyaniline, polypyrrole, and poly(3,4-ethylenedioxythiophene). The catechol/quinone contained sulfonated lignin (LS) was doped into various CPs to form CP/LS NPs with hydrophilicity, conductivity, and redox activity. These CP/LS NPs were used as versatile nanofillers to prepare the conductive hydrogels with long-term adhesiveness. The CP/LS NPs-incorporated hydrogels have a good conductivity because of the uniform distribution of the hydrophilic NPs in the hydrogel network, forming a well-connected electric path. The hydrogel exhibits long-term adhesiveness, which is attributed to the mussel-inspired dynamic redox balance of catechol/quinone groups on the CP/LS NPs. This conductive and adhesive hydrogel shows good electroactivity and biocompatibility and therefore has broad applications in electrostimulation of tissue regeneration and implantable bioelectronics.
An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the “pixel values” as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc.) performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value), the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.
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