This paper presents a novel approach to human gait analysis with a sensor-based technique involving a wearable inertial measurement unit (IMU). The proposed system emphasizes the detection of certain abnormal gait patterns, including hemiplegic, tiptoe, and cross-threshold gait. First, we use the dynamic step conjugate gradient algorithm to calculate the attitude of the gait data, and we then use the gait feature information location algorithm to segment the attitude data. The segmented attitude data are used as input in the classification model. In this paper, we propose an algorithm based on a long short-term memory network and convolutional neural network (LCWSnet) for diagnosis and classification of abnormal gait patterns using the leg Euler angle information, and parameters related to features can be adjusted adaptively according to the feedback of objectives and optimization functions. We optimize the convergence layer of the LSTM-CNN model and improve the classification accuracy of abnormal gait. The experimental results demonstrate that the proposed LCWSnet-based technique is able to detect gait abnormality in the data. The proposed personalized gait classification approach is accurate and reliable and can be implemented for the abnormal gait. INDEX TERMS Convolutional neural network, dynamic step conjugate gradient algorithm, gait feature information location, long short-term memory network, wireless body area network.

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There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.

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A data-driven approach combining together the experimental laser soldering, finite element analysis and machine learning, has been utilized to predict the morphology of interfacial intermetallic compound (IMC) in Sn-xAg-yCu/Cu (SAC/Cu) system. Six types of SAC solders with varying weight proportion of Ag and Cu, have been processed with fiber laser at different magnitudes of power (30-50 W) and scan speed (10 -240 mm/min), and the resultant IMC morphologies characterized through scanning electron microscope are categorized as prismatic and scalloped ones. For the different alloy composition and laser parameters, finite element method (FEM) is employed to compute the transient distribution of temperature at the interface of solder and substrates. The FEM-generated datasets are supplied to a neural network that predicts the IMC morphology through the quantified values of temperature dependent Jackson parameter (αJ). The numerical value of αJ predicted from neural network is validated with experimental IMC morphologies. The critical scan speed for the morphology transition between prismatic and scalloped IMC is estimated for each solder composition at a given power. Sn-0.7Cu having the largest critical scan speed at 30 W and Sn-3.5Ag alloy having the largest critical scan speed at input power 2 values of 40 W and 50 W, thus possessing the greatest likelihood of forming prismatic interfacial IMC during laser soldering, can be inferred as most suitable SAC solders in applications exposed to shear loads.

the problem of software aging widely exists in long-running software system, and the solution is software rejuvenation. Traditional software rejuvenation strategy has some deficiencies in solving the problems of discrete web services aging, for example, the higher failure rate and the poorer stability. Therefore, considering the discrete web services has the loose coupling characteristic, we establish and revise the discrete web service aging damage model by using multiple linear regression method to calculate the aging damage of an individual web service. On this basis, based on the web service priority, call frequency and aging condition, we propose an adaptive rejuvenation strategy which ensures the key web services’ quality. The experiment result shows that, compared with traditional rejuvenation strategy, this strategy improves the stability and dependability of the web services.