Sensors (Basel, Switzerland)
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Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. ⋯ By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.
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Systems which permit the weighing of vehicles in motion are called dynamic Weigh-in-Motion scales. In such systems, axle load sensors are embedded in the pavement. Among the influencing factors that negatively affect weighing accuracy is the pavement temperature. ⋯ Tests carried out in the climatic chamber allowed the influence of temperature on the sensor intrinsic error to be determined. The results presented clearly show that all kinds of sensors are temperature sensitive. This is a new finding, as up to now the quartz and bending plate sensors were considered insensitive to this factor.
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Conventional correction approaches are unsuitable for effectively correcting remote sensing images acquired in the seriously oblique condition which has severe distortions and resolution disparity. Considering that the extraction of control points (CPs) and the parameter estimation of the correction model play important roles in correction accuracy, this paper introduces an effective correction method for large angle (LA) images. ⋯ The whole image is partitioned into contiguous subparts which are corrected by different correction formulae to guarantee the accuracy of each subpart. The extensive experimental results demonstrate that the proposed method significantly outperforms conventional approaches.
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The aim of this paper is to determine the physical proximity of connected things when they are accessed from a smartphone. Links between connected things and mobile communication devices are temporarily created by means of dynamic URLs (uniform resource locators) which may be easily discovered with pervasive short-range radio frequency technologies available on smartphones. In addition, a multi cross domain silent logging mechanism to allow people to interact with their surrounding connected things from their mobile communication devices is presented. The proposed mechanisms are based in web standards technologies, evolving our social network of Internet of Things towards the so-called Web of Things.
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The Internet of Things (IoT) is an infrastructure that interconnects uniquely-identifiable devices using the Internet. By interconnecting everyday appliances, various monitoring, and physical mashup applications can be constructed to improve human's daily life. In general, IoT devices provide two main capabilities: sensing and tasking capabilities. ⋯ This paper demonstrates the contribution of the proposed solution by interconnecting different IoT devices for different applications. In addition, the proposed solution is integrated with the OGC SensorThings API standard, which is a Web service standard defined for the IoT sensing capability. Consequently, the Extended SensorThings API can realize both IoT sensing and tasking capabilities in an integrated and interoperable manner.