Science and Technology in Biomedical Engineering: Labacs Case Example

Research Article

Phys Med Rehabil Int. 2014;1(2): 11.

Science and Technology in Biomedical Engineering: Labacs Case Example

Cecic M1, Papic V1, Bonkovic M1, Grujic T1*, Music J1, Kuzmanic-Skelin A1, Stancic I1, Marasovic T1, Cic M1 and Plestina V2

1Laboratory of Biomechanics, University of Split, Croatia

2Faculty of Science, University of Split, Croatia

*Corresponding author: Grujic T, Laboratory of Biomechanics and Automatic Control Systems - LaBACS, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, R. Boskovica 32, 21000 Split, Croatia

Received: July 31, 2014; Accepted: September 05, 2014; Published: September 09, 2014


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.

Keywords: Biomechanics of human movements; 3D Optical motion tracking system; Inertial sensors; Computer vision; Biosignals; Human anthropometric parameters


Biomedical engineering or bioengineering can be defined as the application of engineering techniques to the understanding of biological systems of the human, and to the development of therapeutic techniques, bioinstrumentation, and biosensors, among other. Bioengineering is relatively new, but multidisciplinary research field which combines knowledge and principles from electrical and mechanical engineering, computer science, biology, medicine, chemistry, etc. Therefore, biomedical engineering as a scientific discipline can be divided into a whole range of research fields including biomechanics, biosignals, bioinstrumentation, biosensors, biomaterials, biomolecular engineering - let us mention only few of them. The main goal of each of these fields is to improve the quality of human life not only in illness, but in wellness, as well.

At the Laboratory for Biomechanics and Automatic Control Systems - LaBACS, at the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Croatia, a group of electrical and computer science engineers and scientists have specialized different subfields of biomedical engineering research and education. Our research interests include biomechanics of human movements, biosignals measurement, analysis, identification, and classification, and development and application of bioinstrumentation and biosensors.

Biomechanics involves the precise description of human movements and the study of the causes of human movement [1]. The study of biomechanics is relevant to professional practice in many professions related to kinesiology, sport and medicine. For example, the physical educator or coach who is teaching movement technique and the athletic trainer or physical therapist treating an injury uses biomechanics to qualitatively analyze movement [1]. Therefore, the precise measurement and analysis of human movements is an essential step in biomechanical research used in sports and medicine. t can include various methods and technologies because of large range of possible applications.

Regarding the term of biosignal; it implies a broad spectrum of different signals, from Electroencephalograms (EEG), Electrocardiograms (ECG), and Electromyograms (EMG) which measure the electrical activity of muscles during contraction, to the biomechanical signals such as kinematics data of human motion (motion trajectories, velocities and accelerations). Biosignals measurement, processing, analysis, identification and classification are a wide research area for itself. As an example, biosignal processing involves the use of signal processing techniques for the interpretation of physiological measurements and the understanding of physiological systems [2]. Although the computerized analytical techniques of signal processing are obtained mostly from engineering fields such as telecommunications and applied mathematics, the nature of physiological data requires substantial biological understanding for its interpretation [2].

Design and development of bioinstrumentation and biosensors, including biosignal processing and classification software, require an extensive knowledge and experience in different engineering fields like electrical and mechanical engineering, computer science, artificial intelligence, and machine learning.

The research done by LaBACS in the area of biomedical engineering is recognized through research grants and numerous publications in international scientific journals [3-8]. The intention of this review paper is to shortly present our new achievements in some of the research areas in which we are currently involved. The paper is organized in four major sections, as follows:

In Section 2; our multimodal approaches to the field of biomechanics of human movements are presented. We have designed, developed, and tested several different systems for human motion tracking and detection which will be described in this paper in the following manner:

The first approach is the design, development, and evaluation of optical motion-tracking system based on active white light markers [3]. The whole system is developed as an affordable (low cost) kinematics measurement system for laboratories where commercial 'gold standard' motion-capturing systems such as Vicon [9] and Optotrak [10] are unavailable. The novelty of our approach really rests with the experimental setup and application of the system to measurement of human kinematics. The use of cost-effective visible light LED markers, and two low-cost fast cameras, instead of expensive Infrared (IR) markers and IR cameras is the novelty itself, as well. The system is intended to be used in measurement of human motions in laboratory conditions (activities like human gait, treadmill walking, and some in-door individual sport activities like cycling and ergo meter rowing).

The second approach aims to overcome the drawbacks of the first one (system implementation only in laboratory conditions and only on individuals). Therefore, we developed the system based on image processing procedures which detects the objects (humans) in demanding backgrounds such as water [11]. We tested our system in case of tracking players in water polo and the obtained results showed the validity and efficiency of our proposed system.

Many biomechanical analyses are interested in the estimation of the pose (position and orientation) of body segments. In particular, head pose estimation is of relevance for both, kinematics data acquisition and head range of motion evaluation. While pose inference can be made with many commercially available optoelectronic systems and inertial measurement units, computer vision techniques draw research attention since they provide a cost-effective solutions to body segment pose estimation. Therefore, as a third approach to human movement, we developed a computer-vision method which estimates head pose from uncalibrated monocular images [12]. Our method is based on a weak perspective projection model of camera and a triangular face model.

Besides three previously mentioned motion-tracking techniques, our fourth approach is oriented toward implementation of inertial Micro-Electro-Mechanical (MEMS) sensors. In the field of human motion tracking, inertial sensors have become attractive alternative to optoelectronic methods since they are usually more cost effective and they have higher mobility and lower subject set-up time [4-13]. As an alternative to computer-vision based human pose detection, previously described, we proposed the system based on inertial sensors. In this case, our system was implemented on a problem of human head pose estimation with the aim of using head motion as a means of controlling computer pointer and different objects (like robot manipulator) by subjects with no control over upper limbs [6].

Apart from the third and fourth approaches, that were oriented toward detection of head pose, our fifth approach is focused on detection and recognition of other body part movements. We developed gesture recognition system which also implements MEMS inertial sensor (three-dimensional accelerometer build into smart phone) [14,15]. Our system uses advanced machine

learning algorithms, specifically, distance metric learning for gesture classification and is capable to recognize nine different gestures performed by a human, with very high accuracy.

Section 3: Deals with biosignal measurement, analysis, identification and classification. In this paper we will present two of our different applications of biosignal processing:

Regarding biosignals, first research interest was focused on gait patterns of human and humanoid robots [7]. Human (or humanoid robot) gait is often described by changes in angular rotation, angular velocity, and angular acceleration of hip, knee and ankle joints during one gait cycle. Using optical measurement system, developed in our laboratory [3], we performed measurement on 30 healthy barefoot humans while walking on a treadmill. We also simulated types of irregular gait, by measurements on subjects wearing knee constraints. By analyzing obtained measurement results, we proposed new kinematics parameters (among which is so-called Gait Factor) which clearly indicate the discrepancy between normal, healthy gait trials and irregular gait trials. We showed that the proposed Gait Factor parameter is a valuable measure for the detection of irregularities in gait patterns of humans and humanoid robots.

The second focus of our biosignal research is dealing with EEG signals. The purpose of our work was to perform the efficient and automatic classification of sleep stage, based on features extracted from measured EEG signals [8]. We analyzed EEG signals of 20 healthy babies, during daytime sleep. We proposed novel feature vectors of a single EEG channel, and performed sleep stage classification by using the Support Vector Machine (SVM) classification algorithm. We obtained high classification accuracy, higher than the human experts' agreement, which confirms our method as an efficient procedure for automatic sleep stage classification.

Section 4: Deals with human anthropometric parameters estimation. Obtaining accurate anthropometric body segment parameters in a fast and reliable manner is an essential step in biomechanical analysis of human motion. With advance of computer vision, and reduction in cost of electronic components, building a customized computer-vision based measurement device becomes possible. Therefore, we developed 3D structured light scanner for anthropometric parameter estimation, consisting of stereovision system with one active sensor (LPD projector) and one passive sensor (camera) [5]. We proposed novel structured light pattern for 3D scanner. The efficacy and accuracy of the proposed system was tested both on artificial objects with known dimensions, and on eight human subjects.

Human Motion Tracking and Detection

Optical motion-tracking system based on active white light markers

Our objective was to design, develop, and validate a simple and cost-effective kinematics measurement system with sub-pixel accuracy [3]. The novelty of the developed system is its design, which is based on LED markers operating with visible light, rather than the IR markers or passive reflective markers that are commonly used. The backbone of the developed system is a pair of high-speed industrial cameras. Calibration procedures and a super-resolution marker model were introduced, ensuring sub-pixel marker centre detection which in final resulted with higher 3D reconstruction accuracy. Evaluation of the system consisted of an accuracy test for stationary and dynamic objects. Major limitations of any markerbased optical tracking systems are marker occlusion by body parts and marker aliasing, and inoperability in dynamic lighting conditions (outdoors). Skin-mounted marker trajectories measured with optical motion capture systems are considered as an adequate representation of the underlying bone trajectories, even though there is an evident motion between the bone and a skin-mounted marker. Despite these drawbacks, optoelectronic devices are today the major tool used for tracking the movements of a human body. Unlike other methods, they offer a complete solution, since they enable simple reconstruction in all three spatial dimensions of the global coordinate system, and are relatively simple to build. Implementation of these systems in laboratories has resulted in a higher quality of research and many clinical applications.

Materials and Methods

Proposed system was developed as an affordable measurement system for laboratories where commercial systems such as Vicon and Optotrak are unaffordable. Improvement of the accuracy (resolution) of the measurement system was achieved by introduction of an image super-resolution technique, which pre-processes the observed image frames, and determines the location of objects in the analyzed frames. Measurement system (depicted in Figure 1) includes both hardware and software components. The main hardware component is a pair of Basler 602fc high speed digital cameras, capable of feeding the computer at a rate of 100 frames per second with resolution of 560 X 490 pixels. Body markers were manufactured using a 3mm flattop LEDs with light intensity of 10 Cd. Special plastic housings were used for holding the LEDs and for their optimal placement on a body surface. Software components include procedures for camera distortion removal, system calibration, marker detection, tracking and 3D marker position reconstruction. A user-friendly interface was designed, offering initial marker identification and final representation of the measured and analyzed kinematics data in 3D space.