Vladan Papić

Identification of sport talents using a web-oriented expert system with a fuzzy module

This paper presents a fuzzy expert system for scouting and evaluation of young sport talents. Based on the knowledge of several human sport experts, various motoric skills tests, morphologic characteristics measurements and functional tests are quantized according to their importance for a chosen set of sports. Obtained values are entered into the knowledge database along with the grades of the measured results for each test. Fuzzy logic is implemented in order to make the system more flexible and robust. The whole system is web-oriented, i.e. developed ASP.NET application is available to the internet users with a proper login and password. The developed expert system gives acceptability prediction and proposal of the most suitable sports for the person being tested. The output results of the system were evaluated by 4 experts, using real data collected during several years. Comparison is done between the sport proposed by our expert system and the actual outcome of the persons sports career. Also, the comparison of the expert system output and the human expert suggestions were done. All tests showed high reliability and accuracy of the developed system. Strengths, possibilities and future plans of the Sport Talent expert system are also discussed.

Quantitative human gait analysis

Abstract In this paper, the methodology for normal gait recognition and estimation is described. Normal gait recognition is derived on the basis of kinematics data of the human locomotion system. Measurements were carried out and the data were processed and statistically analyzed. The procedure was done on a group of 20 students. Kinematics data have been presented in phase plane. Sets of data in phase plane for the specific discrete moments in time were statistically processed using the Gaussian and Bootstrap methods. Discrete moments are chosen according to specific gait phases of a gait cycle. Finally, as a result of statistical analysis, the gait quality index (GQI) is obtained for each gait phase.

Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

Motion analysis system for identification of 3D human locomotion kinematics data and accuracy testing

Abstract In this article, a procedure for acquisition and processing of the human locomotion system kinematics data is presented. Software for 3D motion analysis system is developed, discussed and described. Data acquisition was performed using two commercial camcorders, framegrabber and a personal computer. Data processing by direct linear transformation provided 3D coordinates of the measured movement. Inaccuracies due to the relatively low frame rate and non-synchronised cameras were improved using software. The results of laboratory testing are given, and confirm this improvement. The procedure for synchronisation error elimination is described in detail.

A novel feature descriptor for gesture classification using smartphone accelerometers

Since gestures are a natural form of human expression, gesture-based interfaces can serve as an alternative interaction modality with numerous aspects to be utilized in human computer interaction. In this paper, we address the issue of finding a compact but effective set of features for a robust gesture recognition, using a single 3-axis accelerometer. A novel feature extraction scheme, that allows the gesture form to be clearly discriminated, is proposed. Fuzzy k-Nearest Neighbour classifier is used for recognition of gestures in transformed feature space. The experiments, conducted on an custom gesture vocabulary, reveal that Histogram of Direction (HoD) descriptor, in conjunction with statistical features, produces a highly competitive performance, in terms of recognition accuracy.

Accelerometer based gesture recognition system using distance metric learning for nearest neighbour classification

The need to improve communication between humans and computers has been motivation for defining new communication models, and accordingly, new ways of interacting with machines. In many applications today, user interaction is moving away from traditional keyboards and mouses and is becoming much more physical, pervasive and intuitive. This paper examines hand gestures as an alternative or supplementary input modality for mobile devices. A new gesture recognition system based on the use of acceleration sensor, that is nowadays being featured in a growing number of consumer electronic devices, is presented. Accelerometer sensor readings can be used for detection of hand movements and their classification into previously trained gestures. The proposed system utilizes Mahalanobis distance metric learning to improve the accuracy of nearest neighbour classification. In the approach we adopted, the objective function for metric learning is convex and, therefore, the required optimization can be cast as an instance of semidefinite programming. The experiments, carried out to evaluate system performance, demonstrate its efficacy.

Performance of Compressive Sensing Image Reconstruction for Search and Rescue

In this letter, a system combining compressive sensing (CS)-based image reconstruction and object detection algorithm is introduced. The use of CS is a promising approach for search-and-rescue applications, since it highly reduces the amount of data that needs to be transmitted. However, the high-quality reconstruction of such images is a challenging task due to the complexity of structures and the number of tiny details, possibly being the objects of interest. Hence, the performance of image reconstruction is evaluated in terms of the missing data amount and the object detection quality. Object detection is performed by applying two-stage data segmentation algorithm based on mean shift clustering. The results quality is measured using structural similarity index and peak signal-to-noise ratio.

User-dependent gesture recognition on android handheld devices

Nowadays, many mobile devices are equipped with built-in inertial sensors. This spurred the research on new forms of communication between man and machines based on the movements or “gestures” performed by the user when holding the device. Here we discuss a gesture recognition system for controlling mobile devices with a wide range of possible practical applications. The system was designed to run in realtime on a resource-constrained platform and therefore has a low computational complexity. The paper describes a GestWiz user application for Android operating system which uses the data from a single triaxial accelerometer to recognize a collection of 9 different hand gestures. The systems performance was evaluated off-line, using a gesture dataset, and on-line, through the series of user tests with the application being executed on a smartphone.

Analysis of saliency object detection algorithms for search and rescue operations

The aim of saliency object detection algorithms is to find objects in image or video which draw attention of humans at the first sight. This very popular topic in robotics and computer vision research is useful in various areas and applications like object segmentation, adaptive compression, object recognition, visual surveillance and so on. In this paper, we will explore the possibilities of using these algorithms on the problem of detection of objects for Search and Rescue operations (SAR) in UAV images.

Gradient Compressive Sensing for Image Data Reduction in UAV Based Search and Rescue in the Wild

Search and rescue operations usually require significant resources, personnel, equipment, and time. In order to optimize the resources and expenses and to increase the efficiency of operations, the use of unmanned aerial vehicles (UAVs) and aerial photography is considered for fast reconnaissance of large and unreachable terrains. The images are then transmitted to control center for automatic processing and pattern recognition. Furthermore, due to the limited transmission capacities and significant battery consumption for recording high resolution images, in this paper we consider the use of smart acquisition strategy with decreased amount of image pixels following the compressive sensing paradigm. The images are completely reconstructed in the control center prior to the application of image processing for suspicious objects detection. The efficiency of this combined approach depends on the amount of acquired data and also on the complexity of the scenery observed. The proposed approach is tested on various high resolution aerial images, while the achieved results are analyzed using different quality metrics and validation tests. Additionally, a user study is performed on the original images to provide the baseline object detection performance.