A sensor-based golfer-swing signature recognition method using linear support vector machine

ELEKTROTEHNIŠKI VESTNIK 84(5): 247-252, 2017 ORIGINAL SCIENTIFIC PAPER A sensor-based golfer-swing signature recognition ethod using linear support vector achine Zhichao Zhang 1, Yuan Zhang 1,*, Anton Kos 2, Anton Uek 2 1 Shandong Provincial Key Laboratory of Network Based Intelligent Coputing, University of Jinan, Jinan, China 2 Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia * Corresponding author e-ail: yzhang@ujn.edu.cn Abstract. Golf perforance varies fro person to person because of the differences in physical features of golfer s body and skill level. Recognizing golf swings of an individual golf player is essential to help iproving the golf skill level. This can be done through feedback inforation provided by a specialized equipent or by a personal coach. Based on the classification of the golfer-swing shapes, this work analyses golf-swings. A sensorbased golfer-swing signature-recognition ethod is perfored by using linear support vector achine (LSVM). Golf-swing signals are acquired by a strain-gage sensor fitted to the golf club that easures the club bend. To classify each golfer-swing ulti-class classifier is built by cobining binary LSVM odels with an errorcorrecting-output-codes ulti-class strategy. The experient results of the training, testing and training tie are copared with the results of other odels including decision-tree algoriths, discriinantanalysis algoriths, other support vector achine algoriths, k-nearest neighbor (KNN) classifiers, and enseble classifiers. A coparison shows that by using the strain-gage sensor and ulti-class LSVM odel, the golfer-swing signature is recognized accurately and effectively. Keywords: strain-gage sensor; golf swing; golfer-swing signature recognition; linear support vector achine Prepoznava golfskega zaaha na osnovi senzorskih signalov in etode linearnih podpornih vektorjev Izvedba golfskega zaaha se razlikuje glede na fizične sposobnosti igralca in nivoja njegovega znanja. Prepoznava posaeznikovega golfskega zaaha je poebna za pooč pri njegovi izboljšavi. Le ta je navadno izvedena na osnovi povratne inforacije, ki jo priskrbi posebej za to razvita oprea ali osebni trener. Članek obravnava razvrščanje oblik golskega zaaha, ki je osnova za personalizirano analizo izvedbe zaaha. V članku je opisana izvedba prepoznave posaeznikovega golskega zaaha (osebni podpis) na osnovi senzorskih signalov in etode linearnih podpornih vektorjev (linear support vector achine - LSVM). Signali golfskih zaahov so pridobljeni s poočjo strain gage senzorja pričvrščenega na palico za golf, ki eri upogib palice ed izvedbo zaaha. Za razvrščanje posaeznikovega zaaha je uporabljen odel LSVM razvrščevalnika z več razredi in strategijo izhodnih kod za odpravljanje napak (error-correctingoutput-codes). Rezultati poskusov kažejo, da naša etoda z uporabo strain gage senzorjev in razvrščanja po etodi LSVM z več razredi prepozna posaeznikov golfski zaah natančno in učinkovito. 1 INTRODUCTION Golf is popular all over the world since the 20th century [1]-[3]. Golfers usually pay quite a lot for personal coach fees to learn playing golf well. Thus sart golf systes are expected to help golf players to reduce the coach fee costs [4]-[5]. The coputer science and sensor technology play a vital role in sports activity analysis [6]. Multiple fields of technology has been applied in golf swing detecting and recognition; e.g. sensor, video analysis and iage processing, achine learning, signal processing, etc. In Lv Dongyue's paper, the RGB-D iages of the golfers and golf swings were generated by Kinect [7]. Since the resolution of the depth iages is quite low, the author designed a dynaic Bayesian Network odel to restore the joints positions fro the original iages. Video is also a popular choice for the data source in detecting and analyzing the golf swings or golfer perforance [8]-[9] Golf-swing instructions for users with different physical features are provided in [8]. A professional's golferswings video was recorded to extract the specific postures, and a Gaussian process-regression odel was built to predict the posture of other golfers (especially the non-professional players). In [9], the features of joints position, velocity, and angulation were extracted fro golfer three-diensioned skeleton coordination acquired by Kinect. The author designed a odel cobined with the Hidden Markov Model and Fuzzy Neural Network to classify the feature sequences into the perforance groups of excellent, good, ediu, and bad with the of 80%. Received 31 August 2017 Accepted 20 Septeber 2017

248 ZHANG, ZHANG, KOS, UMEK There is another interesting work using video as the data source in which the authors analyzed the putter video to recognize the putting of each golfer [10]. They detected putter head, signaled by a red arker with a digital caera when the golfers perfored putting. Six golfers perfor 30 putting oveents each. A siple iageprocessing algorith was used to get the ain point clusters and the Darwinian particle swar optiization was applied to fit the function of each trial. The paraeters of the function were the input of five classifiers: linear discriinant analysis, quadratic discriinant analysis, naive Bayes with a noral distribution, naive Bayes with a kernel soothing density estiate, and least-squares support vector achines (LS-SVM). The LS-SVM yielded the best classification of 74.11%. Fro the above works we notice that a high-quality caera is beneficial for the golf-swing analysis but the syste costs quite a lot. Thus Kinect, a gae controller designed by Microsoft, is popular for taking golf-swing videos. However, using Kinect to take golf-swing videos, still costs uch ore than using pressure sensors [7], [11]. In [11], the author designed a syste that cobines a Wii balance board with Kinect sensors to detect the golfer s defined coon istakes in gravity and posture oveent. There are four pressure sensors in the four corners of a Wii balance board, whereas 20 different joints of the huan skeleton are provided by Kinect. Wii balance board pressure-sensor signals are copared to videos taken by Kinect. Their experient shows that the cost of using Wii balance board pressure-sensor is lower at a higher copared to caeras. In our work we use a strain-gage (SG) sensor to acquire golf-swing signals. Our syste is relatively low-cost and has a high classification. Authors [12] developed a portable instruent coposed of a icrocontroller, six-axis inertial sensor (MPU- 6050), and Bluetooth wireless transission odule. MPU-6050 integrates a triaxial acceleroeter and a triaxial gyroscope to detect the accelerations and angular velocities generated fro golf swing oveents. The authors designed an algorith to recognize the golf swing otion with seven stages: address, backswing, top of the swing, downswing, ipact, follow-through, and finish. This work shows that recognizing the signal/golf swing collected by their sensors is eaningful and feasible, and the recognized signal/golf swing in different stages can be interpreted by the echanic. Different fro the works entioned above, our original golf-swing signal is acquired fro a strain-gage (SG) sensor, fitted to the golf club, accurately easuring the golf-club bend [13]-[14]. Our prie goal is to recognize the golf swing of each golfer. This is essential for a later analysis of golf-swing perforance of individual golfers [13]. For golf players, one of the ost iportant steps of the training process is to achieve consistency; that eans repeating the proper gestures and club positions any ties. Consequently, our focus is on recognizing individual golfer-swing (signature) to provide the basis for the analysis of their different type of swings and possible feedback about their perforance and progress. The contributions of our work are: 1) High-precision golf swing signal acquisition using SG sensor fitted to the golf club for a easureent of the golf-club bend. 2) Golfer-swing signature recognition using a ulticlass LSVM odel. The rest of the paper is organized as follows. Section 2 presents the golf swing/signal acquisition. Section 3 introduces the LSVM odel and the ulti-class strategy. Details of our experient and coparison are explained in Section 4. The discussion is deonstrated in Section 5. Section 6 presents the conclusion and future work. 2 GOLF SWING SIGNAL ACQUISITION In this work, the signal (golf swing) is collected by a SG sensor fitted to the golf club [13]-[14]. The golf club bend is easured using the SG sensor SGD-3/350-LY11 fro Oega (Norwalk, CT, USA) shown in Figure 1 [13]. The crio professional easureent syste cobined with odule 9237 (National Instruents Corporation, Austin, TX, USA) is used for connection with the SG sensor and acquiring the golf-swing signal as seen in Figure 2. Golf club shaft Z Y X SG sensor Figure 1. A train gage sensor fitted to the shaft of the golf club easures its bend during execution of the swing. In our study, the golf-swing signals are collected fro four skilled golf players. The sapling frequency of the syste is 500 Hz and we record 2 seconds of each golf swing. The acquired SG golf-swing signal representing the golf-club shaft bend is shown in Figure 3. In Figure 3, there is an obvious variation seen fro around 0.7 s to 1.25 s between the signals of players 2, 3, and 4. Meanwhile, fro 1.8 s to the end, the signals of player 1 are different fro those of the other three players. Thus, separateness in the tie doain of the acquired golf-swing signals is noticed. In addition, there are 1000 points of each of the acquired 134 swings indicating a high diension of data set. Accordingly, we adopt the Principle Coponent Analysis (PCA) to reduce the data diension before classification.

A SENSOR-BASED GOLFER-SWING SIGNATURE RECOGNITION METHOD USING LINEAR SUPPORT VECTOR MACHINE 249 Figure 2. Golf-swing easureent syste. Figure 4. Exaple of a binary SVM with a soft argin for a two-diension of input vector x i {x 1,i, x 2,i } [17] The ain idea of SVM is to find the optial hyperplane shown as a solid line to separate the points of these two classes [18]. To axiize the distance fro the hyperplane to the points of each class, a parallel support vector arked with a dotted line of each class is fixed by the closest points, whereas the distance between the support vector and the hyperplane called argin doubled and axiized. Equation (1) obtained fro the above analysis [17]: 1 w is ax w,b 2 w, s. t. { wt x i + b +1, y i = +1 w T x i + b 1, y i = 1 (1) Figure 3. Golf swing signal representing the bend of the golf club during the swing execution. 3 MULTI-CLASS LSVM MODEL SVM is an algorith used to solve a convex quadratic prograing designed according to the knowledge of statistical learning. The algorith was proposed by C. Cortes in 1995 [15]. LSVM is one of the variants of the SVM algoriths cobined with the linear kernel function that is usually faster and sipler than the nonlinear function [16]. 3.1 LSVM introduction As shown in Figure 4, the points with the output space of two classes are arked with + and - separately [17]. The hyperplane is defined asw T x + b = 0, whereas the support vectors of a positive and negative class are w T x + b = 1 and w T x + b = 1, respectively. Variable x is the input space, whereas w and b are the constant paraeters. Supposing the points with an -diension input and output space of a binary support vector achine (SVM) odel, the data set can be defined as D = {(x 1, y 1 ), (x 2, y 2 ),, (x, y )}, y i {1, 1}. The value of y i indicates which class the input vector of x i belongs to. equaling 2 1 in w,b 2 w, (2) s. t. y i (w T x i + b) 1, i = 1, 2,,. However, soeties it is ipossible to find a hyperplane to separate all the points, e.g. in Figure 4, there are a few points arked with red circles which are classified incorrectly even with the found optial hyperplan [18]. Therefore, the soft argin is designed so that soe points are allowed to violate y i (w T x i + b) 1. Therefore, y i (w T x i + b) 1 ξ i with the slack variable ξ i 0 is used to replace the previous constraint of equation (2). Accordingly, we get: 1 in w,b 2 w 2 + C ξ i, (3) s. t. y i (w T x i + b) 1 ξ i, ξ i 0,, 2,,. We notice that equation (3) is a constrained optiization proble that can be dealt with Lagrangian equation (4):

250 ZHANG, ZHANG, KOS, UMEK L(w, b, α, ξ, μ) = 1 2 w 2 + C ξ i μ i ξ i + α i (1 ξ i y i (w T x i + b)) (4) s. t. α i 0, μ i 0 To find a saddle point, we set the derivative of L(w, b, α, ξ, μ) with respect to variables w, b and ξ i to be zero, and cobine the results with Lagrangian equation (4), and after that we can get a dual proble as seen in equation (5). ax α α i 1 α 2 i α j y i y j x T i x j (5) s. t. α i y i = 0, j=1 0 α i C, i = 1, 2, We can find the Karush-Kuhn-Tucker (KKT) copleentarity conditions as seen in equation (6) [19]. The Sequential Minial Optiization (SMO) is a classical effective algorith usually used to calculate α i [20]. The ain idea of SMO is to introduce a constraint as seen in equation (7), when calculating equation (5). { α i(y i f(x i ) 1 + ξ i ) = 0, i = 1, 2, (6) ξ i (C α i ) = 0 y 1 α 1 + y 2 α 2 = k (7) After getting α i and ξ i, variables w and b are calculated, whereas hyperplane w T x + b = 0 is found, and function f(x) = w T x + b is the one used to predict the response (output) for the predictor space (input). 3.2 Multi-class strategy An error-correcting output codes ulti-class odel is used in our work since it can iprove the classification [21]. Table 1. The one-versus-one coding design Class1 Class 2 Class 3 Class 4 Learner 1 1-1 0 0 Learner 2 1 0-1 0 Learner 3 1 0 0-1 Learner 4 0 1-1 0 Learner 5 0 1 0-1 Learner 6 0 0 1-1 The swings in this work are acquired fro four golfers, which ean that 4 classes of swings are included into the dataset. Therefore, the one-versus-one coding design is chosen in the first step with C 4 2 =6 learners as shown in Table 1. Each learner is a binary LSVM odel of f(x) = w T x + b that arks the positive class and the negative class with 1 and -1, respectively. Coding atrix M coposed of eleents (k,l) (the variables of l and k indicate the nuber of learners and classes, respectively) is shown below, whereas l=1, 2, 3, 4, 5, 6 and k=1, 2, 3, 4. 1 1 1 0 0 0 1 0 0 1 1 0 M = [ ] 0 1 0 1 0 1 0 0 1 0 1 1 The final predicted observation calculated by equation (8) is assigned to class k that iniizes the aggregation of the losses for the six binary learners. 6 l=1 kl l hinge ( kl, s l ) k = arg in k L, l=1 kl l hinge (z) = ax(0,1 z) (8) 4 EXPERIMENT AND COMPARISON In the experient, we acquired 134 swings fro 4 golf players. The swings of each individual are divided into training set and testing set. Figure 5 shows the fraework of the experient. After collecting the golf swings, six confidences are calculated to be the input of the ulti-class LSVM odel by using the Principle Coponent Analysis (PCA). In addition, the classification results can be given in the output of the ulti-class LSVM odel. Golf-swing acquisition PCA LSVM ulti-class odel Classification result Figure 5. Fraework of the experient We copare the ulti-class LSVM odel in ters of the training, testing and training tie with 18 other odels which include decision-tree algoriths, discriinant-analysis algoriths, other support vector achine algoriths, k-nearest neighbor (KNN) classifiers, and enseble classifiers (see Table 2 to Table 6). We can see that the ulti-class LSVM odel gets a 100% training and 100% testing with the iniu training tie of four seconds, which

A SENSOR-BASED GOLFER-SWING SIGNATURE RECOGNITION METHOD USING LINEAR SUPPORT VECTOR MACHINE 251 perfors the best within all 19 odels. The coparison result shows that our ethod using the SG sensor and the ulti-class odel is effective and little tie consuing in the golfer-swing signature classification. Table 2. The perforance of decision tree tie (s) Coplex tree 93.9% 89.79% 7 Mediu tree 93.9% 89.79% 4 Siple tree 93.9% 89.79% 5 Table 3. The perforance of discriinant analysis tie (s) Linear discriinant 100% 100% 7 Quadratic discriinant 98.5% 98.53% 4 Table 4. The perforance of support vector achine tie (s) LSVM 100% 100% 4 Quadratic SVM 100% 98.53% 4 Mediu Gaussian SVM 59.1% 57.35% 4 Coarse Gaussian SVM 59.1% 57.35% 4 Table 5. The perforance of nearest neighbor classifier tie (s) Fine KNN 100% 100% 6 Mediu KNN 75.6% 83.82% 4 Coarse KNN 59.1% 57.35% 4 Cosine KNN 84.8% 94.12% 4 Cubic KNN 75.8% 77.94% 4 Table 6. The perforance of enseble classifier tie (s) Boosted trees 59.1% 57.35% 6 Bagged trees 98.5% 97.06% 7 Subspace discriinant 100% 100% 10 Subspace KNN 100% 100% 6 Rusboost 92.4% 92.66% 6 5 DISCUSSION This work addresses the issues of recognizing golf-swing signature of individual golfers. The result of using linear discriinant, LSVM, and fine KNN classifiers with which the 100% training and 100% testing are achieved deonstrates the usefulness and applicability of achine learning algorith in identifying the golfer-swing signature. The ulti-class LSVM perfors the best with the 100% testing and iniu training tie copared with the other 18 odels listed in Table 2 - Table 6. The conclusion drawn fro our analysis is that the golf swings vary fro one golfer to the other, and that our SG sensor-based LSVM odel works well in golfer-swing signature recognition. Based on the current golfer-swing analysis and personal golfer-swing history, our next step will be providing a (real-tie) feedback to golfers. 6 CONCLUSION AND FUTURE WORK In this work we designed a ethod to recognize golferswing signature of individual golf players using a SG sensor and the ulti-class LSVM odel. A coparison with other odels shows that the presented ulti-class LSVM odel is effective in recognizing golfer-swing signature and that it outperfors other coparable classifiers. Our experient shows that each golfer has his/her own distinguishable swing signature. The ai of our future work is to invite a large nuber of golf players to analyze their golf swings and to build odels to classify different types of swings and identify possible errors in swing execution. ACKNOWLEDGEMENT This work is supported in part by the National Natural Science Foundation of China under Grant 61572231, in part by the Shandong Provincial Key Research & Developent Project under Grant 2017GGX10141, and in part by the Slovenian Research Agency within the research progra Algoriths and Optiization Methods in Telecounications. The corresponding author is Yuan Zhang. REFERENCES [1] Thériault, Gerain, and Pierre Lachance. "Golf injuries." Sports Medicine 26, no. 1 (1998): 43-57. [2] Ong, T.F.,and Mansor, S.H., 2014. Travel Motivation and Points of Attachent Aong Golf Spectators. In Proceedings of the International Colloquiu on Sports Science, Exercise, Engineering and Technology 2014 (ICoSSEET 2014) (pp. 581-590). Springer Singapore. [3] Stenner, Brad J., Aber D. Mosewich, and Jonathan D. Buckley. "An exploratory investigation into the reasons why older people play golf." Qualitative Research in Sport, Exercise and Health 8, no. 3 (2016): 257-272. [4] Wu, Hsien-Huang P., and Hong-Yi Guo. "Autoatic Optical Inspection for steel golf club." In Fuzzy Systes and Knowledge Discovery (FSKD), 2015 12th International Conference on, pp. 2332-2336. IEEE, 2015. [5] Natsagdorj, Sainbuyan, John Y. Chiang, Che-Han Su, Chih-chung Lin, and Cheng-yen Chen. "Vision-based Assebly and Inspection Syste for Golf Club Heads." Robotics and Coputer-Integrated Manufacturing 32 (2015): 83-92. [6] Thoas, Graha, Rikke Gade, Thoas B. Moeslund, Peter Carr, and Adrian Hilton. "Coputer vision for sports: current applications and research topics." Coputer Vision and Iage Understanding (2017). [7] Lv, Dongyue, Zhipei Huang, Lixin Sun, Nenghai Yu, and Jiankang Wu. "Sart otion reconstruction syste for golf swing: a DBN odel based transportable, non-intrusive and inexpensive golf swing capture and reconstruction syste." Multiedia Tools and Applications 76, no. 1 (2017): 1313-1330. [8] Sugiura, Daisuke, Hiroharu Tsutsui, and Takayuki Haaoto. "Detecting flaws in golf swing using coon oveents of professional players." Machine Vision and Applications 27, no. 1 (2016): 13-22.

252 ZHANG, ZHANG, KOS, UMEK [9] Zhang,Lichao, Jui-Chien Hsieh, and Jiangping Wang. "A Kinectbased golf swing classification syste using HMM and neurofuzzy." In Coputer Science and Inforation Processing (CSIP), 2012 International Conference on, pp. 1163-1166. IEEE, 2012. [10] Couceiro, M.S., Dias, G., Mendes, R. and Araújo, D., 2013. Accuracy of pattern detection ethods in the perforance of golf putting. Journal of otor behavior, 45(1), pp.37-53. [11] Huang, Shih-Yu, Kuei-Pin Kuo, and Yi-Hsuan Lin. "A golf swing analysis syste using Wii balance board and Kinect sensors for novice players." Multiedia Tools and Applications 74, no. 23 (2015): 10679-10696. [12] Hsu, Yu-Liang, Yu-Tai Chen, Po-Huan Chou, Yu-Chen Kou, Yi- Chung Chen, and Heng-Yi Su. "Golf swing otion detection using an inertial-sensor-based portable instruent." In Consuer Electronics-Taiwan (ICCE-TW), 2016 IEEE International Conference on, pp. 1-2. IEEE, 2016. [13] Uek, Anton, Yuan Zhang, Sašo Toažič, and Anton Kos. "Suitability of Strain Gage Sensors for Integration into Sart Sport Equipent: A Golf Club Exaple." Sensors 17, no. 4 (2017): 916. [14] Kos, Anton, Sašo Toažič, and Anton Uek. "Suitability of sartphone inertial sensors for real-tie biofeedback applications." Sensors 16, no. 3 (2016): 301. [15] Corinna Cortes, Vladiir Vapnik, Support vector networks, Machine Learning, 20(3), str. 273-297, 1995. [16] András kocsor, János csirik, Fast Independent Coponent Analysis in Kernel Feature Spaces, International Conference on Current Trends in Theory and Practice of Coputer Science, 2001(2234), str: 271-281, 2001. [17] Zhihua Zhou, Machine Learning, Tsinghua University Press, 2016, pp. 121-140. [18] Schölkopf, Bernhard, and Alexander J. Sola. Learning with kernels: support vector achines, regularization, optiization, and beyond. MIT press, 2002, pp.6-19. [19] Boyd, Stephen, and Lieven Vandenberghe. Convex optiization. Cabridge University Press, 2004, pp.244. [20] Platt, John. "Sequential inial optiization: A fast algorith for training support vector achines." (1998). [21] Fürnkranz, Johannes, "Round Robin Classification." J. Mach. Learn. Res., Vol. 2, 2002, pp. 721 747. Zhichao Zhang received her BS in Coputer Science in 2015 fro University of Jinan, China. She is a aster student ajoring in coputer science at Shandong Provincial Key Laboratory of Network Based Intelligent Coputing, University of Jinan, and was a visiting student sponsored by the EU's Erasus+ progra at the Faculty of Electrical Engineering, University of Ljubljana, Slovenia. As a first author, she currently has four papers published in bioedical signal processing and M-Health. She is a eber of both ACM and CCF. Her current research interests include bioedical signal processing, achine learning, and pattern recognition. Yuan Zhang received his Ph.D. degree in Control Theory & Engineering fro Shandong University, China, in 2012. He is currently an Associate Professor at University of Jinan (UJN), China. Dr. Zhang was a visiting professor at Coputer Science Departent, Georgia State University, USA, in 2014. As the first author or corresponding author he has published ore than 40 peer reviewed papers in international journals and conference proceedings, one book chapters, and five patents in the areas of bioedical engineering and -Health. He has served as Leading Guest Editor for four special issues of IEEE, Elsevier, Springer and InderScience publications, and has served on the technical progra coittee for nuerous international conferences. He is an associate editor for IEEE Access. Dr. Zhang's research interests are in bioedical signal processing and -Health. His research has been extensively supported by the Natural Science Foundation of China, China Postdoctoral Science Foundation, and Natural Science Foundation of Shandong Province with total grant funding over one illion RMB. Dr. Zhang is a Senior Meber of both IEEE and ACM. Anton Kos received his Ph.D. degree in electrical engineering fro the University of Ljubljana, Slovenia, in 2006. He is currently an assistant professor at the Faculty of Electrical Engineering, University of Ljubljana. He is a eber of the Laboratory of Inforation Technologies at the Departent of Counication and Inforation Technologies. Between 2000 and 2009 he was the head of IT at GlaxoSithKline d.o.o., Ljubljana, Slovenia. He is the (co)author of fourteen papers appeared in the international engineering journals and of ore than thirty papers presented at international conferences. He is a eber of the research progra Algoriths and Optiization Methods in Telecounications, which is one of the two research progras that are every year listed aong the best research progras in Slovenia. He is the leader of two industrial research and developent projects in designing of sensor-based sart sport equipent and forestry achinery. His professional work encopasses co-organizing international VIPSI conferences, serving as a progra coittee eber at several international conferences (IIKI, FABULUOS, and ICIST), serving as a guest editor and reviewer at several international scientific journals. He is the Maxeler abassador for Slovenia (Dataflow coputing). He is a eber of IEEE, Union of Electrical Engineering of Slovenia, and SICOM. Anton Uek received his Ph.D. degree in electrical engineering fro the University of Ljubljana, Slovenia, in 1999. He is currently a Senior Lecturer at the Faculty of Electrical Engineering, University of Ljubljana. He is a eber of the Laboratory of Inforation Technologies at the Departent of Counication and Inforation Technologies. He is a eber of the research progra Algoriths and Optiization Methods in Telecounications that was two years in a row the best research progra financed by the Slovenian research agency. Since last year he is in charge of industrial research and developent projects in designing of sensor-based sart sport equipent and sensor-based forestry achinery. His teaching and research work includes signal processing, digital counication, secure counications, access network technologies and design of sensor-supported sport training systes. He is the (co)author of eight papers appeared in the international engineering journals and of ore than thirty papers presented at international conferences. He is also a eber of IEEE and since 2015 the Slovenian section CoSOC chapter chair.