Wearable sensor devices are playing an increasingly important role in providing pervasive and personalized healthcare. Important elements of modern healthcare services are physical rehabilitation and injury prevention. Wearable sensor devices attached to the patient can offer valuable supplemental information to healthcare professionals during treatment or therapy. In physical rehabilitation, wearable inertial sensor devices help therapists to monitor and evaluate parameters and key performance indicators of the rehabilitation activities. Therapy efficiency can be increased by using real-time feedback systems. Three different feedback system architectures are defined and studied: 1) therapist; 2) user; and 3) cloud system. A case study involving rehabilitation therapy based on swimming exercises was performed. Field test results show that the developed sensor device and real-time therapist feedback application provide sufficiently accurate and precise data for the efficient evaluation of swimming parameters, such as stroke time and stroke rotation angle symmetry. Rehabilitation therapists should be able to operate the system without the constant presence of trained and experienced technical personnel (engineers). The wide adoption of wearable sensor device-based applications will lead to cloud systems where big data analytics will offer additional benefits to healthcare, well-being, and quality of life.

In recent years, smart sports equipment and body sensor systems have become popular in professional and amateur sports. One of a few remaining problems in real-time applications is the discovery of knowledge from the embedded sensors data. In sports training, such knowledge helps accelerated motor learning. The authors start with exploring the possibilities of the classification of golf swing performance with the 1-D convolutional neural network (CNN) in real-time. They thoroughly investigate multiple golf swing data classifiers based on CNNs fed with multi-sensor signals. The authors test the possibilities of real-time performance of CNN methods on the multi-length sequences. In addition, they thoroughly evaluate the performance of their well-trained CNN-based classifier on the aforementioned test set in terms of common indicators. Experiments and corresponding results show that the authors' models can satisfy the real-time requirement of the accuracy of the classification and outperform support vector machine (SVM).

Modern information systems make it increasingly easy to gain more insight into the public interest, which is becoming more and more important in diverse public and corporate activities and processes. The disadvantage of existing research that focuses on mining the information from social networks and online communities is that it does not uniformly represent all population groups and that the content can be subjected to self-censoring or curation. In this paper, we propose and describe a framework and a method for estimating public interest from the implicit negative feedback collected from the Internet protocol television (IPTV) audience. Our research focuses primarily on the channel change events and their match with the content information obtained from closed captions. The presented framework is based on concept modeling, viewership profiling, and combines the implicit viewer reactions (channel changes) into an interest score. The proposed framework addresses both above-mentioned disadvantages or concerns. It is able to cover a much broader population, and it can detect even minor variations in user behavior. We demonstrate our approach on a large pseudonymized real-world IPTV dataset provided by an ISP, and show how the results correlate with different trending topics and with parallel classical long-term population surveys.

Smartphones are currently the most pervasive wearable devices. One particular use of smartphone inertial sensors is motion tracking in various mobile systems and applications. The objective of this study is to validate smartphone gyroscopes for angular tracking in mobile biofeedback applications. The validation method includes measurements of angular motion performed concurrently by a smartphone gyroscope and a professional optical tracking system serving as the reference. The comparison of the measurement results shows that the inaccuracies of a calibrated smartphone gyroscope for various movements are between 0.42° and 1.15°. Based on the measurement results and the general requirements of biofeedback applications, smartphone gyroscopes are sufficiently accurate for angular motion tracking in mobile biofeedback applications.

Science and technology are ever more frequently used in sports for achieving the competitive advantage. Motion tracking systems, in connection to the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Special focus is given on feedback loop delays and its real-time operation. Optical tracking and inertial sensor tracking systems are presented and compared. Real-time sensor signal acquisitions and real-time processing challenges, in connection to biomechanical biofeedback, are presented. This paper can serve as a starting point for determining the adequate combination of technical equipment and its specifications that work favorably for the operation of the planned real-time biofeedback application.

This article presents a new Fast Hash-based File Existence Checking (FHFEC) method for archiving systems. During the archiving process, there are many submissions which are actually unchanged files that do not need to be re-archived. In this system, instead of comparing the entire files, only digests of the files are compared. Strong cryptographic hash functions with a low probability of collision can be used as digests. We propose a fast algorithm to check if a certain hash, that is, a corresponding file, is already stored in the system. The algorithm is based on dividing the whole domain of hashes into equally sized regions, and on the existence of a pointer array, which has exactly one pointer for each region. Each pointer points to the location of the first stored hash from the corresponding region and has a null value if no hash from that region exists. The entire structure can be stored in random access memory or, alternatively, on a dedicated hard disk. A statistical performance analysis has been performed that shows that in certain cases FHFEC performs nearly optimally. Extensive simulations have confirmed these analytical results. The performance of FHFEC has been compared to the performance of a binary search (BIS) and B+tree, which are commonly used in file systems and databases for table indices. The results show that FHFEC significantly outperforms both of them.