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Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

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As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity from patent data. In line with it, this paper aims to propose a hybrid approach based on morphological analysis (MA) and unified structured inventive thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to explore the intrinsic links of innovation elements. A basic morphology matrix is constructed according to patent information and then is extended using the innovation algorithms that are reorganized from USIT. Technology opportunities are analyzed at two layers to generate new technical ideas. To illustrate the research process and validate its utility, this paper selects the technology of coalbed methane (CBM) extraction as a use case. This hybrid approach contributes by suggesting a semi-autonomous and systematic procedure to perform MA for TOD. By integrating the innovation algorithms, this approach improves the procedure of value extension in MA.

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The reduction of the lead time in measurement and reverse engineering, and the increased requirements in terms of accuracy and flexibility, have resulted in a great deal of research effort aimed at developing and implementing multisensor systems. This paper describes an effective competitive approach for using a tactile probe to compensate the data from a laser line scanner to perform accurate reverse engineering of geometric features. With the data acquired using laser scanning, intelligent feature recognition and segmentation algorithms can be exploited to extract the global surface information of the object. The tactile probe is used to re-measure the geometric features with a small number of sampling points and the obtained information can be subsequently used to compensate the point data patches which are measured by laser scanning system. Then, the compensated point data can be exploited for accurate reverse engineering of a CAD model. The limitations of each measurement system are compensated by the other. Experimental results on three parts validate the rapidity and accuracy of this multi-sensor data fusion approach.