Gesture Recognition Via Learning Deep Spatio-Temporal Features In Wi-Fi Sensing

Internet of Things (IoT) expects to incorporate massive machine-type (MCT) devices, such as vehicles, sensors, and wearable devices, which brings a large number of application tasks that need to be processed. Additionally, data collected from various devices needs to be executed and processed in a timely, reliable, and efficient manner. Gesture recognition has enabled IoT applications such as human-computer interaction and virtual reality. In this work, we propose a cross-domain device-free gesture recognition (DFGR) model, that exploits 3D-CNN to obtain spatiotemporal features in Wi-Fi sensing. To adapt the sensing data to the 3D model, we carry out 3D data segment and supplement in addition to signal denoising and time-frequency transformation. We demonstrate that our proposed model outperforms the state-of-the-art method in the application of DFGR even cross 3 domain factors simultaneously, and is easy to converge and convenient for training with a less complicated hierarchical structure.

Weighted recurrence network for characterizing continuous dynamical systems

Recurrence network (RN) is a powerful tool for the analysis of complex dynamical systems. It integrates complex network theory with the idea of recurrence of a trajectory, i.e. whether two state vectors are close neighbors in a phase space. However, the differences in proximity between connected state vectors are not considered in the RN construction. Here, we propose a weighted state vector recurrence network method which assigns weights to network links based on the proximity of the two connected state vectors. On the basis, we further propose a weighted data segment recurrence network that takes continuous data segments as nodes for the analysis of noisy time series. The feasibility of the proposed methods is illustrated based on the Lorenz system. Finally, an application to five types of EEG recordings is conducted to demonstrate the potentials of the proposed methods in the study of real-world data.

Head motion classification using thread-based sensor and machine learning algorithm

Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.

Efficient Learning From Two-Class Categorical Imbalanced Healthcare Data

When data classes are differently represented in one v. other data segment to be mined, it generates the imbalanced two-class data challenge. Many health-related datasets comprising categorical data are faced with the class imbalance challenge. This paper aims to address the limitations of imbalanced two-class categorical data and presents a re-sampling solution known as ‘Syn_Gen_Min' (SGM) to improve the class imbalance ratio. SGM involves finding the greedy neighbors for a given minority sample. To the best of one's knowledge, the accepted approach for a classifier is to find the numeric equivalence for categorical attributes, resulting in the loss of information. The novelty of this contribution is that the categorical attributes are kept in their raw form. Five distinct categorical similarity measures are employed and tested against six real-world datasets derived within the healthcare sector. The application of these similarity methods leads to the generation of different synthetic samples, which has significantly improved the performance measures of the classifier. This work further proves that there is no generic similarity measure that fits all datasets.

An efficient clustering mechanism in big data framework for data preprocessing and management

An efficient data handling mechanism has been applied based on epoch-based k-means associated fuzzy clustering (EKFC). In the first phase weights have been assigned to individual data segment presented based on the classification key metrics. It has been assigned automatically. Then weight preprocessing has been done in such manner to prune the unwanted weights. It has been pruned in such way to filter the weights which are not scalable. Then epoch-based k-means associated fuzzy clustering (EKFC) approach has been applied for data arrangement. First different epochs have been considered for the calculation of initial seeds values. These seeds have been considered after considering 100 epochs. After 100 epochs seeds have been determined. These seeds values have been used as the initial centroid for the k-means clustering. After the complete validation similar clusters from the two clustering approaches have been considered. In the next phase operational clustering has been performed. In the final phase threshold ranking has been performed. It has been performed for the final classification based on the above clusters. It will arrange in the order of threshold values. It will be used for the determination of the priority of the task in the big data environment. The results are found to be prominent in terms of classification accuracy.

Evaluation of Harmonic Contributions for Multi Harmonic Sources System Based on Mixed Entropy Screening and an Improved Independent Component Analysis Method

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

Detection Method of Data Integrity in Network Storage Based on Symmetrical Difference

In order to enhance the recall and the precision performance of data integrity detection, a method to detect the network storage data integrity based on symmetric difference was proposed. Through the complete automatic image annotation system, the crawler technology was used to capture the image and related text information. According to the automatic word segmentation, pos tagging and Chinese word segmentation, the feature analysis of text data was achieved. Based on the symmetrical difference algorithm and the background subtraction, the feature extraction of image data was realized. On the basis of data collection and feature extraction, the sentry data segment was introduced, and then the sentry data segment was randomly selected to detect the data integrity. Combined with the accountability scheme of data security of the trusted third party, the trusted third party was taken as the core. The online state judgment was made for each user operation. Meanwhile, credentials that cannot be denied by both parties were generated, and thus to prevent the verifier from providing false validation results. Experimental results prove that the proposed method has high precision rate, high recall rate, and strong reliability.