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The paper presents a set of remote, unobtrusive sensing technologies that can be used in upper and lower limbs rehabilitation monitoring. The advantages of using sensors based on microwave Doppler radar or infrared technologies for physiotherapy assessment are discussed. These technologies allow motion sensing at distance from monitored subject, reducing thus the discomfort produced by some wearable...
Parkinson disease (PD) cure remains one of the greatest challenges in chronic neurological disorder therapy, motivating efforts to provide actionable information to guide self-managed therapy adjustments. In this paper, we develop a data fusion approach to combine multi-dimensional data from body-worn inertial sensors to automatically identify different medication states of patients with PD. The proposed...
Energy saving in buildings has attracted many researchers attention. One of the research topics is occupancy detection for each room to automatically control airconditioner, light, heating, etc. by monitoring the temperature, humidity, light, and co2 using the corresponding sensors. For existing data analysis, traditional regression methods such as CART, RF, LDA are often used to predict the occupancy...
Human motion recognition is a challenging task, especially when motion capture data is huge. Existing approaches for this task focused mainly on how to extract features from motion capture data to achieve high recognition performance. However, due to the presence of redundant features and the high dimensionality of data, these approaches may not achieve the optimal performance. In order to rapidly...
Human activity recognition (HAR) is of crucial importance in ambient assisted living (AAL) environments. In such environments, residents' data are collected via unobtrusive sensors to be further interpreted as human activities. Essential services are provided for the users according to the HAR results. In this paper, a novel scheme is represented for HAR, which is based on minimizing a convex objective...
In response to the technological development in recent years, many technology giants are making efforts toward Industry 4.0. However, many small-and medium-sized factories cannot even computerize and automate their factories, which is the foundation of Industry 4.0, due to inadequate capital and scale. This is because the majority of these factories are still using conventional machines in which the...
With the enhanced computing capabilities and a wide variety of functions available on smartphones, critical and sensitive information, such as contact lists, messages, schedules, credit card numbers, is stored on smartphones, which makes preventing smartphone from being stolen of an unprecedented importance. Loss of smartphones not only causes economic loss but also jeopardizes the privacy of the...
In this paper, we address the problem of predicting wind turbine electrical subsystem fault using time series data obtained from multiple sensors on wind turbine. While considering this as a time series classification problem, we are facing with the challenge that there is no explicit label information regarding the temporal location and duration of symptoms of the fault. Besides, significant data...
Wearable devices such as smartwatches have become popular. The accelerometer embedded in smartwatch can record the movement of hand, so that it may lead to privacy compromise when performing sensitive inputting on keyboard with hand wearing smartwatch. The existing inference attack collects smartwatch's accelerometer readings which correspond to the movement of hand when inputting a numeric password,...
In real applications of one class classification, new features may be added due to some practical or technical reason. While lacking of representative samples for the new features, multi-task learning idea could be used to bring some information from the former learning model. Based on the above assumption, a new multi-task learning approach is proposed to deal with the training of the updated system...
On-line supervised spotting and classification of subsequences can be performed by comparing some distance between the stream and previously learnt time series. However, learning a few incorrect time series can trigger disproportionately many false alarms. In this paper, we propose a fast technique to prune bad instances away and automatically select appropriate distance thresholds. Our main contribution...
Multi-modal scene analysis is a growing field of importance as additional sensors, such as 3D LIDAR, is becoming a common complement to image capturing systems. However, while additional sensory data potentially can make the analysis more accurate, it also comes with a host of associated issues. For example, inconsistencies in the data between sensors resulting from, e.g., misalignment, moving objects,...
In this paper, we propose a multiclass classifier training method which reduces “fatal” misclassifications by cost-relaxation of “tolerable” misclassifications in one-against-all classifiers training, named misclassification tolerable learning. In a binary classifier in the one-against-all classifiers, we introduce a new class group “conceptually similar classes,” whose class labels are similar to...
We present a multi-user multiple-input multiple-output (MIMO) cognitive radio system consisting of a secondary receiver that deploys spatial multiplexing to decode signals from multiple secondary transmitters, under the presence of primary transmissions. The secondary receiver carries out minimum mean-squared error detection to decode the secondary data streams, while it performs spectrum sensing...
Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, Lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings....
For the problems that we can't monitor abnormal conditions of heart rate as well as carrying out scientific and efficient training plans based on knowledge from variation of them. A ZigBee and big data analysis based pulse monitoring system has been proposed. The system is composed of multiple ZigBee based pulse monitoring sensors, customized gateways and back-end system. Individuals' pulse information...
In this paper we introduce an automated mechanism for knowledge discovery from data streams. As a part of this work, we also present a new approach to the creation of classifiers ensemble based on a wide variety of models. Furthermore, we describe an innovative, highly scalable feature extraction and selection framework designed to work with the MapReduce programming model and the application of designed...
Prediction for deck-motion is a practical measure to improve the landing/taking off safety of carrier-based aircraft when those deck-motions in six-degree freedoms cannot be effectively controlled/restrained. Deck-motions excited by waves and winds own characteristics of randomness and nonlinearity. It is generally believed those classical feed-forward neural networks, such as back propagation networks...
Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions...
In this paper we provide an intelligent and convenient shopping cart, Intelligent Shopping Assistant System (ISAS). With ISAS, customers can concentrate on what he/she has to buy, and do not be bothered where he/she has to buy or it is want to buy. In contrast to conventional shopping cart, two modes of autonomous functions are added to ISAS in order to reduce the labor of customers for pushing goods...
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