Vladan Papic

University of Split, Croatia, Electronics and computer science, Faculty Member

Followers

Following

Co-authors

Public Views

Interests

Uploads

Papers by Vladan Papic

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

Simulation Modelling Practice and Theory, May 1, 2004

In this article, a procedure for acquisition and processing of the human locomotion system kinema... more 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

Mimetic Visual Servoing with Adaptive Neural Network (ANN)

In this paper we present a model free hybrid visual servoing system. The "model free" term refers... more In this paper we present a model free hybrid visual servoing system. The "model free" term refers to the system with the unknown kinematics model that has to be estimated on-line, while "hybrid" specifies the visual controller architecture. The proposed system has a conventional Jacobian estimation part necessary for control output generation and it is supplemented with an additional adaptive neural network (ANN). It is shown that ANN could be used to improve the visual servoing performances of the conventional visual servoing controller, as well as to enable the mimetic control of the robot which dynamics differs from the robot which it mimics.

Download

Adaptive Neural Network (ANN) for Visual Servoing: the Mimetic Approach

International journal of circuits, systems and signal processing, 2007

Download

Laboratory for Identification of Human Movement with LABACs Software Support

The paper gives short impression about Laboratory for Identification of Human Movement, including... more The paper gives short impression about Laboratory for Identification of Human Movement, including the software support. Six modules introduce students to the fundamentals in identification of human locomotion. The modules are: measurements of anthropometric parameters, center of mass of a complex structure, measurement and analysis of ground reaction forces during walking, kinematics of human gait, electromyography, calculation of moments using inverse dynamics. Each module generaly have introductory theoretical path, case example and exercise for student. Also with each module comes autonomus software support. The student is supposed to do measurement and analysis of data for each module.

Spatia Visualisation of Statistically Processed Gait Data

A novel procedure for presentation and visualization of gait data has been presented. Values of t... more A novel procedure for presentation and visualization of gait data has been presented. Values of three main angles (hip, knee and ankle) have been measured in five gait phases on the population of 20 students. After statistical processing for each angle in particular gait phase, ellipsoids containing 80% probability in the distribution has been determined using multivariate normal distribution. Gait normality has been discussed according to the relation of the measured angles and ellipsoid volumes for each phase.

Adaptive Neural Network (ANN) for Visual Servoing: the Mimetic Approach

In this paper we present a model free hybrid visual servoing system. The ? model free? term refer... more In this paper we present a model free hybrid visual servoing system. The ? model free? term refers to the system with the unknown kinematics model that has to be estimated on-line, while ? hybrid? specifies the visual controller architecture. The proposed system has a conventional Jacobian estimation part necessary for control output generation and it is supplemented with an additional adaptive neural network (ANN). It is shown that ANN could be used to improve the visual servoing performances of the conventional visual servoing controller, as well as to enable the mimetic control of the robot which dynamics differs from the robot which it mimics.

Download

Robotics and information technologies in education: four countries from Alpe-Adria-Danube Region survey

International Journal of Technology and Design Education, 2020

This paper presents the results of the survey that was conducted during 2018 in four countries: B... more This paper presents the results of the survey that was conducted during 2018 in four countries: Bulgaria, Greece, Bosnia and Herzegovina and Croatia. The survey is a part of activities within the project "Increasing the well being of the population by RObotic and ICT based iNNovative education" (RONNI), funded by the Danube Strategic Project Fund (DSPF). The survey included two target groups: the teachers/experts and the parents; and the corresponding questionnaires (QR) were delivered to schools in each of the participating countries. A total of 428 subjects participated in the survey (231 parents and 197 teachers/experts). Seven hypotheses related to stakeholders attitudes and opinions were formed and tested in the work, showing highly favorable sentiment toward inclusion of robotics and information technology (IT) in the classroom but with some exceptions. The conclusions drawn, based on the analysis of the results, can be used for proposing strategies and methodologies aimed at boosting inclusion of IT in the teaching process, transferable across the regions to support effective learning as well as to identify possible problems with their implementation in relation to attitudes of stakeholders: teachers and parents.

Download

Identifying Needs of Robotic and Technological Solutions for the Classroom

2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2018

This paper presents preliminary results of the questionnaire (QR) that was conducted during April... more This paper presents preliminary results of the questionnaire (QR) that was conducted during April and May 2018 in three countries: Bulgaria, Greece and Croatia. The QR is part of the activities within project funded by Danube Strategic Project Fund (DSPF): Increasing the well being of the population by RObotic and ICT based iNNovative education (RONNI). The QR has been delivered to schools in each of the participating countries. Two sets of questions were delivered to target groups: teachers/experts and parents. The analysis of the results will be used in proposing innovative teaching strategies and methodologies, transferable across the regions to support effective learning.

Download

A Comparative Study of FFT, DCT, and DWT for Efficient Arrhytmia Classification in RP-RF Framework

International Journal of E-Health and Medical Communications, 2018

Computer-aided ECG classification is an important tool for timely diagnosis of abnormal heart con... more Computer-aided ECG classification is an important tool for timely diagnosis of abnormal heart conditions. This paper proposes a novel framework that combines the theory of compressive sensing with random forests to achieve reliable automatic cardiac arrhythmia detection. Furthermore, the paper evaluates the characterization power of FFT, DCT and DWT data transformations in order to extract significant features that will bring the additional boost to the classification performance. The experiments – carried out over MIT-BIH benchmark arrhythmia database, following the standards and recommended practices provided by AAMI – demonstrate that DWT based features exhibit better performances compared to other two feature extraction techniques for a relatively small number of random projected coefficients, i.e. after considerable (approx. 85%) dimensionality reduction of the input signal. The results are very promising, suggesting that the proposed model could be implemented for practical ap...

Download

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

Mathematical Problems in Engineering, 2016

Search and rescue operations usually require significant resources, personnel, equipment, and tim... more 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 ap...

Download

Performance of Compressive Sensing Image Reconstruction for Search and Rescue

IEEE Geoscience and Remote Sensing Letters, 2016

In this letter, a system combining compressive sensing (CS)-based image reconstruction and object... more 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.

Cardiac arrhythmia detection using DCT based compressive sensing and random forest algorithm

2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech), 2016

The discrimination of ECG signals is of crucial importance in clinical diagnoses of cardiac disea... more The discrimination of ECG signals is of crucial importance in clinical diagnoses of cardiac diseases. Manual analysis of ECG signals is very complex and time consuming task due to their composite nature. This paper proposes a novel scheme for reliable automatic classification of ECG signals into normal and three different abnormal (arrhythmia affected) categories. The feature extraction is based on an amalgamation of discrete cosine transform and random projection for dimensionality reduction. Furthermore, the classification is performed using random forest algorithm. The performance of the proposed scheme is evaluated on the restricted subset of ECG recordings from MIT-BIH arrhythmia database. In the experiments, a near perfect recognition accuracies of 99.33% and 99%, depending on the definition of projection matrix, are achieved with only 50 random projected coefficients; i.e. after considerable dimensionality reduction of the input ECG signal.

Feature weighted nearest neighbour classification for accelerometer-based gesture recognition

Softcom 2012 20th International Conference on Software Telecommunications and Computer Networks, 2012

Understanding human gestures can be posed as a typical classification problem. Within the compute... more Understanding human gestures can be posed as a typical classification problem. Within the computer, gestures are represented as time-varying patterns in feature space. These patterns, though variable, are distinct and have associated meanings. In the absence of a priori knowledge of the underlying class probabilities, classification is performed based on some notion of similarity, e.g. distance, among samples. The k-nearest neighbour (kNN) decision rule has often been used in these pattern recognition problems. The use of this particular technique gives rise to multiple issues, one of them being that it operates under the implicit assumption that all features are of equal importance in deciding the class membership of the pattern to be classified, regardless of their "relevancy". This paper presents an accelerometer-based gesture recognition system that utilizes Mahalanobis distance metric learning to derive optimal weighting scheme for nearest neighbour classification. The metric is trained with the goal of separating different classes by large local margins and pulling closer together samples from the same class, based on using as few features as possible. Our experiments on an arbitrary gesture set show that the proposed method leads to significant improvements in recognition accuracies, yielding simultaneously a maximum of feature discrimination.

LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition

Journal on Multimodal User Interfaces, 2015

This paper presents a novel gesture recognition system using a single three-axis accelerometer, t... more This paper presents a novel gesture recognition system using a single three-axis accelerometer, that is to serve as an alternative or supplementary interaction modality for controlling mobile devices. Capturing, training and classification of the detected hand gestures are expected to be executed in their entirety on the mobile device running the proposed system, instead of being passed to a nearby computer. As gesture recognition belongs to the group of pattern recognition problems where the underlying class probabilities are not a priori known, the classification is based on the distance between neighbouring examples. The distance metric is optimized by using large margin nearest neighbour (LMNN) method. To measure the amount of classification confidence, a fuzzy version of nearest neighbour algorithm is employed. Obtained results for recognition of nine hand gestures using proposed LMNN-fuzzy combination are presented and compared to that of other similar approaches. The system achieves near perfect recognition accuracy that is highly competitive with systems based on statistical methods and other accelerometer-based gesture recognition systems in the literature.

Download

Mimetic visual servoing with adaptive neural network (ANN)

In this paper we present a model free hybrid visual servoing system. The "model free" t... more In this paper we present a model free hybrid visual servoing system. The "model free" term refers to the system with the unknown kinematics model that has to be estimated on-line, while "hybrid" specifies the visual controller architecture. The proposed system has a conventional Jacobian estimation part necessary for control output generation and it is supplemented with an additional adaptive neural network (ANN). It is shown that ANN could be used to improve the visual servoing performances of the conventional visual servoing controller, as well as to enable the mimetic control of the robot which dynamics differs from the robot which it mimics.

Download

Science and Technology in Biomedical Engineering: Labacs Case Example

This paper presents the scientific efforts of the group of engineers and scientists from the Labo... more This paper presents the scientific efforts of the group of engineers and scientists from the Laboratory of Biomechanics and Automatic Control Systems – LaBACS, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Croatia. The field of our expertise is biomedical engineering, and we are dedicated to finding the solutions for various current biomedical engineering problems. The primary goal of our research interests is to develop new, state-of-the-art, cost-effective biomedical and biomechanical measurement systems and to apply them on the human subjects to obtain scientifically significant results. This paper presents a short review of some of our newly developed measurement systems and digital signal processing techniques and algorithms for processing and analysis of the measured biomechanical and biomedical signals.

Motion-Based Gesture Recognition Algorithms for Robot Manipulation

International Journal of Advanced Robotic Systems, 2015

The prevailing trend of integrating inertial sensors in consumer electronics devices has inspired... more The prevailing trend of integrating inertial sensors in consumer electronics devices has inspired research on new forms of human-computer interaction utilizing hand gestures, which may be set-up on mobile devices themselves. At present, motion gesture recognition is intensely studied, with various recognition techniques being employed and tested. This paper provides an in-depth, unbiased comparison of different algorithms used to recognize gestures based primarily on the single 3D accelerometer recordings. The study takes two of the most popular and arguably the best recognition methods currently in use - dynamic time warping and hidden Markov model - and sets them against a relatively novel approach founded on distance metric learning. The three selected algorithms are evaluated in terms of their overall performance, accuracy, training time, execution time and storage efficacy. The optimal algorithm is further implemented in a prototype user application, aimed to serve as an interf...

Download

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

2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012

The need to improve communication between humans and computers has been motivation for defining n... more 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.

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

Simulation Modelling Practice and Theory, 2004

Quantitative human gait analysis

Simulation Practice and Theory, 2000

In this paper, the methodology for normal gait recognition and estimation is described. Normal ga... more 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 speci®c discrete moments in time were statistically processed using the Gaussian and Bootstrap methods. Discrete moments are chosen according to speci®c gait phases of a gait cycle. Finally, as a result of statistical analysis, the gait quality index (GQI) is obtained for each gait phase.

Download

Vladan Papic

Uploads

Papers by Vladan Papic

Log In