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Potential of IMU-Based Systems in Measuring Single Rapid Movement Variables in Females with Different Training Backgrounds and Specialization

Potential of IMU-Based Systems in Measuring Single Rapid Movement Variables in Females with... Hindawi Applied Bionics and Biomechanics Volume 2020, Article ID 7919514, 7 pages https://doi.org/10.1155/2020/7919514 Research Article Potential of IMU-Based Systems in Measuring Single Rapid Movement Variables in Females with Different Training Backgrounds and Specialization 1,2 1,3 2 2 Stefan Marković , Milivoj Dopsaj , Sašo Tomažič , and Anton Umek Faculty of Sport and Physical Education, University of Belgrade, Belgrade 11000, Serbia Faculty of Electrical Engineering, University of Ljubljana, Ljubljana 1000, Slovenia Institute of Sport, Tourism and Service, South Ural State University, Chelyabinsk 454080, Russia Correspondence should be addressed to Stefan Marković; stephan.markovic@hotmail.com Received 26 December 2019; Revised 20 March 2020; Accepted 17 June 2020; Published 1 July 2020 Academic Editor: Juri Taborri Copyright © 2020 Stefan Marković et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The aim of this paper is to determine the discriminative potential of the IMU-based system for the measurement of rapid hand movement properties, i.e., relevant kinematic variables in relation to different groups of participants. The measurement of the kinematics of the rapid hand movement was performed using a standard hand tapping test. The sample in this research included a total of 70 female participants and was divided into 3 subsamples. The discriminant analysis has identified two functions, DF and DF , that explain 91.1 and 8.1% of the variance, respectively. The differences between the examined 1 2 subsamples originate from the variables grouped in DF , which were statistically significant (p ≤ 0:000). In relation to this function, the national volleyball team centroid position was shifted with -1.108 and -1.968 standard deviation values from the control group and youth volleyball team, respectively. The difference between control and Voll_Youth groups was -0.860 standard deviation value. The factors with the greatest discriminative potential among the groups represent the temporal characteristics of the rapid hand movement, i.e., the time elapsed between the onset of the movement and the first and second tap, as defined by the variables t and t , respectively. The established findings clearly indicate that IMU sensors are practically 1 2 applicable in relation to the sensitive measurement of rapid arm movement capability of female athletes. 1. Introduction Miniature inertial measurement unit (IMU) is a typical example of the MEMS technology which has been increas- ingly used as a means for motion analysis [3] for the purposes In recent years, there has been a rapid development of micro- electromechanical sensor systems (MEMS). Along with it of sports science and praxis. Typically, an IMU that incorpo- came the implementation and application of such systems rates a triaxial accelerometer, gyroscope, and magnetometer in different professional environments as well as in everyday is built into a miniature wearable device [4]. This allows use [1]. In this context, the system of sport is not an excep- measurement of acceleration, angular velocity, and orienta- tion and also permits sensor fusion for tracking of three- tion, and various wearable sensors have been developed and used in testing, training, and competition in order to provide dimensional movements to a variable extent of precision. In new, or more in-depth, information regarding different addition, it is possible to use an IMU in order to obtain aspects of sports performance. In essence, this reflects more relevant information about the temporal characteristics of broad tendencies regarding the implementation of new the analyzed movements [5]. In this case, the sampling frequency of the system determines the level of measurement technologies for the purposes of obtaining more sensitive and sport-specific information in relation to the level of precision. Primary applications of IMU-based systems in achieved preparedness in elite athletes [2]. sports training, testing, and competition are related to either 2 Applied Bionics and Biomechanics some of the attributes that are unique to the players [23] or concurrent or terminal biomechanical biofeedback [1] or to the assessment of the physical characteristics relevant for can serve as a basis for identification of the individuals that performance and injury prevention [6–8]. are potentially more capable in this regard. In relation to the aforementioned, the hand tapping test was chosen for The development of sports science increasingly requires a multistructured, integrative approach to information gather- the purposes of this research as it is not sport-specific and ing in both laboratory and field testing conditions. This it is widely used as a part of basic test batteries in different requires the application of multiple measurement methods sports as well as in testing of basic motor abilities in a non- and technologies [9] in order to obtain relevant information athlete population. The aim of this paper is to determine the discriminative regarding the level of achieved physical fitness during differ- ent phases of athletes’ preparation. In addition to being a potential of the IMU-based system for measurement of rapid basis for assessment, these results can be used for the pur- hand movement properties, i.e., to define relevant kinematic poses of calculating the potential of physical abilities and variables in relation to different groups of participants. the efficiency of athletes’ performance [10, 11]. In this sense, sports science and praxis employ both basic, i.e., universal, 2. Materials and Methods and specific testing batteries [12] for permanent and peri- odical monitoring of physical properties, expressed in non- 2.1. The Research Sample. The sample in this research specific conditions as well as in specific conditions of included a total of 70 female participants. The overall sample competitive stress [13]. Although from the aspect of move- was divided into 3 groups, of which one included physically ment, the system of sport is very complex and diversified, active controls (age = 22:3±1:9 years, BH = 168:8±5:3cm, and it can be argued that rapid simple movements are the BW = 64:5±2:8kg), while the other two consisted of the main form of movements in basically all sports [14]. members of the Republic of Serbia national volleyball team Accordingly, regardless of the specificity of the testing (age = 24:5±3:5 years, BH = 186:7±4:2cm, BW = 75:6± conditions, it is necessary to provide relevant information 2:6kg) and national-level young volleyball players about the athletes’ potential in this aspect. In this context, (age = 16:8±1:8 years, BH = 180:4±6:5cm, BW = 71:1± volleyball is a typical example of a sport that sets high and 3:2kg), respectively. complex technical, tactical, and physical requirements for the players. This, in turn, requires overall development of 2.2. Measurement Methods. The measurement of the kine- motor abilities and specific motor skills [15] which can be matics of the rapid hand movement was performed using considered a multidimensional, multistage task that requires a test that represents the gold standard in the measurement constant monitoring. of rapid movements of the extremities—standard hand tap- As previously mentioned, IMU-based measurement sys- ping test [9, 24, 25]. This standard test included lateral tems have been increasingly used in different sport settings alternating hand movement between two markers posi- for various purposes including performance and technique tioned at the 50 cm distance on the table in front of the par- evaluation [16], although their application in measurement ticipant. The test was performed in an upright sitting of fast hand and arm movements has been fairly limited. In position with the dominant hand, which was initially placed this context, baseball pitching has been the most frequently on the mark at the opposite side, while the nondominant researched topic due to the high incidence of injuries related hand was placed at the mark positioned at the midlength to this particular type of throwing motion and the need to of the movement distance, as shown in Figure 1(a). When accurately measure the dynamics of the involved segments ready, the subject performed a maximally fast movement. during peak activity in order to quantify relevant aspects of After performing 2 pretest familiarization trials, each partic- performance [17]. As throwing a baseball and hitting a vol- ipant performed three trials separated with at least 3 leyball are similar in overhead functional demand, although minutes of rest [11]. The best result was taken for further they generate different kinematic patterns [18], IMU-based statistical processing [26]. systems are also applicable in this context and were used in For the purposes of this research, we developed a portable recent studies for classification of volleyball players based measurement system, which allows for quick setup. The on spiking performance and evaluation of wrist speed and wireless sensor device is connected to a laptop running the as a part of measurement systems used for movement classi- LabView application. A custom-made wireless sensor device fication [19–21]. includes an IMU MEMS sensor, a microcontroller with a In volleyball, high arm speed is a general prerequisite of built-in Wi-Fi communication module, and a LiPo battery successful performance, as it is generally required for efficient for multihour operation. Figure 1(b) shows a custom-made spiking [22]. Therefore, relevant information regarding the sensor device without a protective housing. The sensor device differences between groups in relation to the kinematic char- is attached to the glove as shown in Figure 1(a). The acceler- acteristics of rapid arm and hand movement can contribute ation in the Y-axis corresponds to the line of hand move- to the better understanding of the stages of athletes’ develop- ment, i.e., the line connecting the markers. ment and potential effects of training and selection process The sensor device is equipped with a combined 3D on their capabilities in this regard. Comparison of volleyball accelerometer and 3D gyroscope (LSM6DS33, STMicroelec- players of different age categories but similar competitive tronics, Genève, Switzerland) [27]; however, for the purpose ranking within each category and physically active controls of our research, we used only accelerometer data. The sam- (with no volleyball background) can provide insight into pling frequency is 200 Hz, and the dynamic range of the Applied Bionics and Biomechanics 3 (a) (b) Figure 1: (a) The initial position of the subject’s hand with the IMU sensor attached to the glove. (b) A custom-made wireless sensor device (uncovered). accelerometer is ±16 g . The wireless sensor device continu- (v) GA is the maximal acceleration gradient (expressed 0 1 -1 ously sends data via a Wi-Fi interface while a LabVIEW in g ·s ) application is used for acceleration signal processing and (vi) GA is the maximal deceleration gradient (expressed kinematic variable data acquisition. -1 in g ·s ) A custom LabView (LabView 2019, National Instruments, Austin, Texas) application was used in order to process the It should be noted that all acceleration-related variables acceleration signal. The LabView application contains a mod- were measured in the first part of tapping, prior to the first ule for receiving accelerometer samples in UDP packets, and hand tap. The examined variables and the time frame of the obtained accelerometer signal was filtered with a low- events are shown on a typical example of the acceleration sig- pass Butterworth filter (order = 5, fcof = 40 Hz). The relevant nal (Figure 2). variables in the movement kinematics were automatically identified after the onset of the motion, which was detected 2.4. Statistical Analysis. For the purposes of this paper, all when the absolute acceleration exceeded 1.15 g . The applica- variables were processed using descriptive statistical analysis tion implements automatic threshold and peak detection in order to determine relevant measures of central tendency, using predefined SubVIs provided by National Instruments data dispersion, and range (mean, StDev, SEM, cV%, Min for both A and abs (A), thus providing the location and/or and Max) for the respective subsamples. The normality of magnitude of relevant kinematic and temporal variables. the distribution of the results was determined by the applica- Detection of the acceleration gradient variables was per- tion of the nonparametric Kolmogorov-Smirnov goodness- formed using the peak detector SubVI on the signal obtained of-fit test (K-S Z). The position of centroid groups’ location, by derivation of the acceleration over time. as a group standardized multivariate score, and the structure of the extracted functions and group differences were defined 2.3. Variables. The following variables acquired from the by discriminant analysis. The level of statistical significance processed hand acceleration signal were used in order to was defined based on the criterion p ≤ 0:05 [28]. All data define the relevant temporal and kinematic characteristics analyses were conducted using Excel 2013 and IBM SPSS of the movement: v23 statistical software. (i) t is the time from the start of the movement to the 3. Results and Discussion first tap of the hand (expressed in s) (ii) t is the time from the first tap to the second tap of Table 1 shows the results of the descriptive statistical analysis the hand (expressed in s) of the relevant kinematic variables in relation to the exam- ined groups, as well as the results of the one-sample nonpara- (iii) A is the maximal acceleration (expressed as a mul- metric Kolmogorov-Smirnov goodness-of-fit test. tiplier of g ) Table 2 shows the summary of the canonical discrimi- (iv) A is the maximal deceleration (expressed as a mul- nant functions and the results of the general statistical differ- tiplier of g ) ences between groups in relation to the examined variables. 0 4 Applied Bionics and Biomechanics are -0.112, -1.220, and 0.748, respectively (Figure 3). These results show that, in relation to DF , the Voll_Nat_Team group centroid position is shifted with -1.968 and -1.108 standard deviation values from the Voll_Youth and the con- trol group, respectively. The difference between control and Voll_Youth is -0.860. The second discriminant function (DF ) did not show a significant difference between the observed groups; thus, the centroid positions of the groups in relation to this function are similar (Figure 3). The factors with the greatest discriminative value among the groups 0.02 0.12 0.22 0.32 0.42 (s) represent the temporal characteristics of the rapid hand movement, i.e., the time elapsed between the onset of the –5 movement and the first (t ) and second (t ) tap, as shown 1 2 in Table 3. –10 Regarding the efficiency of the IMU-based measurement t GA A GA t A t system in relation to the discrimination of the examined sub- 0 1 1 2 1 2 2 –15 samples from the aspect of kinematic characteristics relevant for the rapid hand movement, it was determined that it was abs (A) 65.7% overall (Table 4). It should be pointed out that the Figure 2: Absolute acceleration (abs) and acceleration in the Y- highest accuracy of classification (80.6%) was determined in (dominant) axis with the time frame of relevant events. the subsample of young volleyball players (Voll_Youth), while players in the control group were classified as having Table 3 shows the structure matrix of the extracted func- the lowest accuracy (40.9%). Based on the kinematic charac- tions explaining the determined general differences between teristics of rapid hand movement, 36.4 and 22.7% of the con- groups. trol group was classified in the subsamples Voll_Youth and Voll_Nat_Team, respectively (Table 4). For the subsample Table 4 shows the classification of the group membership in relation to the results of the discriminant analysis based on Voll_Nat_Team, the discriminative efficiency was 70.6%, or the relevant kinematic variables of rapid hand movement. 88.2% when taking into account the participants classified Figure 3 shows the graphical representation of the cen- in the Voll_Youth group. troid position of the examined subsamples in relation to the The presented results show the potential of IMU sensors relevant functions based on the kinematic variables of rapid in relation to the measurement of rapid movement kinemat- hand movement. ics. The discriminative nature of the obtained results indi- Based on the results of the descriptive statistical analysis, cates the applicability of such systems for the purposes of it was determined that the obtained results of the examined assessment, monitoring, and even selection of athletes. kinematic variables of rapid hand movement have acceptable variation, given the fact that the coefficient of variation is 4. Conclusions in the range from 7.87 to 45.00 for t in Voll_Youth and GA in control samples, respectively. The results of the The aim of this paper was to determine the discriminative Kolmogorov-Smirnov goodness-of-fit test indicate that the potential of IMU sensor technology in detecting single rapid examined variables are normally distributed on a general movement variables/characteristics in females with different level (Table 1). The results of Box’s test of equality of covari- training backgrounds and specialization. Rapid hand move- ance matrices have shown that the multiple distribution of ment properties, i.e., relevant kinematic variables in relation the examined groups is similar on a statistically significant to different groups of participants, were examined. The mea- level (M =78:488, F =1:605, p =0:008). On the basis of the surement of the kinematic variables was performed using a aforementioned, it can be argued that the obtained results standard hand tapping test. The sample in this research have average homogeneity [29] and normal distribution included a total of 70 female participants and was divided and belong to the same measurement area which makes them into 3 subsamples, of which one included physically active representative in terms of further scientific interpretation. controls, while the other two consisted of the members The discriminant analysis has identified two functions, of the Republic of Serbia national volleyball team and DF and DF , that explain 91.9 and 8.1% of the variance, national-level young volleyball players, respectively. The dis- 1 2 criminant analysis was used in order to define the centroid respectively. It was determined that DF is statistically signif- icant (p ≤ 0:000). This function is composed of the variables location, as a group standardized multivariate score, as well t and t . The second function DF is composed of the vari- as the structure of the extracted functions and group differ- 1 2 2 ables A , A ,GA , and GA .DF reached a p value of 0.616, ences between the respective subsamples. The discriminant 1 2 1 2 2 thus yielding nonsignificant results (Table 2). This indicates analysis has identified two functions, DF and DF ,that 1 2 that the differences between the examined subsamples origi- explain 91.9 and 8.1% of the variance, respectively. The dif- nate from the variables grouped in DF , i.e., the first function. ferences between the examined subsamples originate from The centroid positions of the examined groups control, Voll_ the variables grouped in extracted function DF , which was Nat_Team, and Voll_Youth in relation to the function DF statistically significant at the level p ≤ 0:000. In relation to (g ) 0 Applied Bionics and Biomechanics 5 Table 1: Basic descriptive statistics of the examined variables in relation to the research subsamples with the results of the one-sample Kolmogorov-Smirnov test. Control Mean SEM StDev cV% Min Max K-S Z Sig. t (s) 22 0.23 0.01 0.03 14.20 0.19 0.29 0.611 0.849 t (s) 22 0.43 0.01 0.05 12.50 0.34 0.54 0.741 0.642 A (g ) 22 3.87 0.25 1.17 30.23 2.02 6.23 0.351 1.000 1 0 A (g ) 22 8.33 0.44 2.06 24.75 5.34 12.24 0.713 0.689 2 0 -1 GA (g ·s ) 22 70.94 5.21 24.42 34.42 36.00 122.13 0.834 0.491 1 0 -1 GA (g ·s ) 22 211.73 20.31 95.27 45.00 84.34 485.88 0.961 0.314 2 0 Voll_Nat_Team Mean SEM StDev cV% Min Max K-S Z Sig. t (s) 17 0.21 0.01 0.03 13.92 0.17 0.26 0.590 0.877 t (s) 17 0.40 0.01 0.04 9.63 0.37 0.50 1.190 0.117 A (g ) 17 3.88 0.21 0.88 22.63 2.17 5.32 0.563 0.909 1 0 A (g ) 17 8.35 0.46 1.91 22.88 4.39 12.07 0.440 0.990 2 0 -1 GA (g ·s ) 17 57.30 5.81 23.97 41.84 23.59 109.81 0.433 0.992 1 0 -1 GA (g ·s ) 17 229.26 17.62 72.63 31.68 142.95 394.64 0.775 0.586 2 0 Voll_Youth Mean SEM StDev cV% Min Max K-S Z Sig. t (s) 31 0.24 0.00 0.03 11.52 0.18 0.30 0.679 0.746 t (s) 31 0.45 0.01 0.04 7.87 0.40 0.52 0.815 0.520 A (g ) 31 3.78 0.18 0.99 26.25 2.48 5.89 0.684 0.737 1 0 A (g ) 31 8.94 0.43 2.42 27.04 4.90 14.16 0.725 0.669 2 0 -1 GA (g ·s ) 31 72.34 4.63 25.79 35.65 37.88 154.98 0.908 0.382 1 0 -1 GA (g ·s ) 31 252.19 17.87 99.51 39.46 96.47 520.85 0.754 0.620 2 0 Table 2: The summary of canonical discriminant functions and Table 4: Classification results. general intergroup differences. Predicted group membership Eigenvalues Voll_ Groups Voll_ Total Control Nat_ %of Cumulative Canonical Youth Function Eigenvalue Team variance % correlation Control 9 5 8 22 1 0.641 91.9 91.9 0.625 Voll_ 2 0.057 8.1 100 0.231 Nat_ 212 3 17 Wilks’ lambda Count Team Test of Wilks’ Chi-square df Sig. Voll_ function(s) lambda 5 1 25 31 Youth 1 0.577 35.492 12 0.000 Original Control 40.9 22.7 36.4 100 2 0.946 3.550 5 0.616 Voll_ Nat_ 11.8 70.6 17.6 100 Team Table 3: The structure matrix. Voll_ 16.1 3.2 80.6 100 Function Youth DF DF 1 2 65.7% of the original grouped cases were correctly classified. 0.516 -0.007 this function, the Voll_Nat_Team group centroid position 0.408 -0.209 was shifted with -1.108 standard deviation values from the 0.145 0.654 control and -1.968 standard deviation values from the Voll_ 0.295 -0.412 Youth group. The difference between the control and Voll_ GA 0.144 0.318 Youth groups was -0.860 standard deviation value. The GA -0.056 -0.093 factors with the greatest discriminative potential among the groups are the variables of the temporal characteristics of 6 Applied Bionics and Biomechanics Voll_Nat_Team Voll_Youth –1 –2 –3 Control –4 –4 –3 –2 –1 01 2 3 4 DF1 Group Control Control Voll_Nat_Team Voll_Nat_Team Voll_Youth Voll_Youth Group Centroid Figure 3: The graphical representation of the centroid position of the examined subsamples. the rapid hand movement, i.e., the time elapsed between the Acknowledgments onset of the movement and the first and second tap, as This work is sponsored in part by the Slovenian Research defined by the variables t and t . The established findings 1 2 Agency within the research program ICT4QoL—Informa- clearly indicate that IMU sensors are practically applicable tion and Communications Technologies for Quality of Life in this context and can be included as a new technology used (research core funding no. P2-0246) and within the bilateral for the purposes of assessment, monitoring, and selection of project between Slovenia and Serbia titled “Sensor technolo- athletes. gies as support systems for the detection and selection of tal- ents in sport and monitoring the performance of athletes” (research core funding no. BI-RS/20-21-023). This paper Data Availability was partially financed by the Slovenian Research Agency bilateral project SRB-SLO for the period 2018-2019 (no. The data used to support the findings of this study are avail- R2-2046). able from the corresponding author upon request. References Disclosure [1] A. Kos and A. Umek, Biomechanical Biofeedback Systems and This paper is a part of the project “Effects of the Applied Applications, Springer International Publishing, 2018. Physical Activity on Locomotor, Metabolic, Psychosocial [2] V. Bachev, M. Gadev, O. Groshev, P. Yordanov, and B. 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Potential of IMU-Based Systems in Measuring Single Rapid Movement Variables in Females with Different Training Backgrounds and Specialization

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Hindawi Applied Bionics and Biomechanics Volume 2020, Article ID 7919514, 7 pages https://doi.org/10.1155/2020/7919514 Research Article Potential of IMU-Based Systems in Measuring Single Rapid Movement Variables in Females with Different Training Backgrounds and Specialization 1,2 1,3 2 2 Stefan Marković , Milivoj Dopsaj , Sašo Tomažič , and Anton Umek Faculty of Sport and Physical Education, University of Belgrade, Belgrade 11000, Serbia Faculty of Electrical Engineering, University of Ljubljana, Ljubljana 1000, Slovenia Institute of Sport, Tourism and Service, South Ural State University, Chelyabinsk 454080, Russia Correspondence should be addressed to Stefan Marković; stephan.markovic@hotmail.com Received 26 December 2019; Revised 20 March 2020; Accepted 17 June 2020; Published 1 July 2020 Academic Editor: Juri Taborri Copyright © 2020 Stefan Marković et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The aim of this paper is to determine the discriminative potential of the IMU-based system for the measurement of rapid hand movement properties, i.e., relevant kinematic variables in relation to different groups of participants. The measurement of the kinematics of the rapid hand movement was performed using a standard hand tapping test. The sample in this research included a total of 70 female participants and was divided into 3 subsamples. The discriminant analysis has identified two functions, DF and DF , that explain 91.1 and 8.1% of the variance, respectively. The differences between the examined 1 2 subsamples originate from the variables grouped in DF , which were statistically significant (p ≤ 0:000). In relation to this function, the national volleyball team centroid position was shifted with -1.108 and -1.968 standard deviation values from the control group and youth volleyball team, respectively. The difference between control and Voll_Youth groups was -0.860 standard deviation value. The factors with the greatest discriminative potential among the groups represent the temporal characteristics of the rapid hand movement, i.e., the time elapsed between the onset of the movement and the first and second tap, as defined by the variables t and t , respectively. The established findings clearly indicate that IMU sensors are practically 1 2 applicable in relation to the sensitive measurement of rapid arm movement capability of female athletes. 1. Introduction Miniature inertial measurement unit (IMU) is a typical example of the MEMS technology which has been increas- ingly used as a means for motion analysis [3] for the purposes In recent years, there has been a rapid development of micro- electromechanical sensor systems (MEMS). Along with it of sports science and praxis. Typically, an IMU that incorpo- came the implementation and application of such systems rates a triaxial accelerometer, gyroscope, and magnetometer in different professional environments as well as in everyday is built into a miniature wearable device [4]. This allows use [1]. In this context, the system of sport is not an excep- measurement of acceleration, angular velocity, and orienta- tion and also permits sensor fusion for tracking of three- tion, and various wearable sensors have been developed and used in testing, training, and competition in order to provide dimensional movements to a variable extent of precision. In new, or more in-depth, information regarding different addition, it is possible to use an IMU in order to obtain aspects of sports performance. In essence, this reflects more relevant information about the temporal characteristics of broad tendencies regarding the implementation of new the analyzed movements [5]. In this case, the sampling frequency of the system determines the level of measurement technologies for the purposes of obtaining more sensitive and sport-specific information in relation to the level of precision. Primary applications of IMU-based systems in achieved preparedness in elite athletes [2]. sports training, testing, and competition are related to either 2 Applied Bionics and Biomechanics some of the attributes that are unique to the players [23] or concurrent or terminal biomechanical biofeedback [1] or to the assessment of the physical characteristics relevant for can serve as a basis for identification of the individuals that performance and injury prevention [6–8]. are potentially more capable in this regard. In relation to the aforementioned, the hand tapping test was chosen for The development of sports science increasingly requires a multistructured, integrative approach to information gather- the purposes of this research as it is not sport-specific and ing in both laboratory and field testing conditions. This it is widely used as a part of basic test batteries in different requires the application of multiple measurement methods sports as well as in testing of basic motor abilities in a non- and technologies [9] in order to obtain relevant information athlete population. The aim of this paper is to determine the discriminative regarding the level of achieved physical fitness during differ- ent phases of athletes’ preparation. In addition to being a potential of the IMU-based system for measurement of rapid basis for assessment, these results can be used for the pur- hand movement properties, i.e., to define relevant kinematic poses of calculating the potential of physical abilities and variables in relation to different groups of participants. the efficiency of athletes’ performance [10, 11]. In this sense, sports science and praxis employ both basic, i.e., universal, 2. Materials and Methods and specific testing batteries [12] for permanent and peri- odical monitoring of physical properties, expressed in non- 2.1. The Research Sample. The sample in this research specific conditions as well as in specific conditions of included a total of 70 female participants. The overall sample competitive stress [13]. Although from the aspect of move- was divided into 3 groups, of which one included physically ment, the system of sport is very complex and diversified, active controls (age = 22:3±1:9 years, BH = 168:8±5:3cm, and it can be argued that rapid simple movements are the BW = 64:5±2:8kg), while the other two consisted of the main form of movements in basically all sports [14]. members of the Republic of Serbia national volleyball team Accordingly, regardless of the specificity of the testing (age = 24:5±3:5 years, BH = 186:7±4:2cm, BW = 75:6± conditions, it is necessary to provide relevant information 2:6kg) and national-level young volleyball players about the athletes’ potential in this aspect. In this context, (age = 16:8±1:8 years, BH = 180:4±6:5cm, BW = 71:1± volleyball is a typical example of a sport that sets high and 3:2kg), respectively. complex technical, tactical, and physical requirements for the players. This, in turn, requires overall development of 2.2. Measurement Methods. The measurement of the kine- motor abilities and specific motor skills [15] which can be matics of the rapid hand movement was performed using considered a multidimensional, multistage task that requires a test that represents the gold standard in the measurement constant monitoring. of rapid movements of the extremities—standard hand tap- As previously mentioned, IMU-based measurement sys- ping test [9, 24, 25]. This standard test included lateral tems have been increasingly used in different sport settings alternating hand movement between two markers posi- for various purposes including performance and technique tioned at the 50 cm distance on the table in front of the par- evaluation [16], although their application in measurement ticipant. The test was performed in an upright sitting of fast hand and arm movements has been fairly limited. In position with the dominant hand, which was initially placed this context, baseball pitching has been the most frequently on the mark at the opposite side, while the nondominant researched topic due to the high incidence of injuries related hand was placed at the mark positioned at the midlength to this particular type of throwing motion and the need to of the movement distance, as shown in Figure 1(a). When accurately measure the dynamics of the involved segments ready, the subject performed a maximally fast movement. during peak activity in order to quantify relevant aspects of After performing 2 pretest familiarization trials, each partic- performance [17]. As throwing a baseball and hitting a vol- ipant performed three trials separated with at least 3 leyball are similar in overhead functional demand, although minutes of rest [11]. The best result was taken for further they generate different kinematic patterns [18], IMU-based statistical processing [26]. systems are also applicable in this context and were used in For the purposes of this research, we developed a portable recent studies for classification of volleyball players based measurement system, which allows for quick setup. The on spiking performance and evaluation of wrist speed and wireless sensor device is connected to a laptop running the as a part of measurement systems used for movement classi- LabView application. A custom-made wireless sensor device fication [19–21]. includes an IMU MEMS sensor, a microcontroller with a In volleyball, high arm speed is a general prerequisite of built-in Wi-Fi communication module, and a LiPo battery successful performance, as it is generally required for efficient for multihour operation. Figure 1(b) shows a custom-made spiking [22]. Therefore, relevant information regarding the sensor device without a protective housing. The sensor device differences between groups in relation to the kinematic char- is attached to the glove as shown in Figure 1(a). The acceler- acteristics of rapid arm and hand movement can contribute ation in the Y-axis corresponds to the line of hand move- to the better understanding of the stages of athletes’ develop- ment, i.e., the line connecting the markers. ment and potential effects of training and selection process The sensor device is equipped with a combined 3D on their capabilities in this regard. Comparison of volleyball accelerometer and 3D gyroscope (LSM6DS33, STMicroelec- players of different age categories but similar competitive tronics, Genève, Switzerland) [27]; however, for the purpose ranking within each category and physically active controls of our research, we used only accelerometer data. The sam- (with no volleyball background) can provide insight into pling frequency is 200 Hz, and the dynamic range of the Applied Bionics and Biomechanics 3 (a) (b) Figure 1: (a) The initial position of the subject’s hand with the IMU sensor attached to the glove. (b) A custom-made wireless sensor device (uncovered). accelerometer is ±16 g . The wireless sensor device continu- (v) GA is the maximal acceleration gradient (expressed 0 1 -1 ously sends data via a Wi-Fi interface while a LabVIEW in g ·s ) application is used for acceleration signal processing and (vi) GA is the maximal deceleration gradient (expressed kinematic variable data acquisition. -1 in g ·s ) A custom LabView (LabView 2019, National Instruments, Austin, Texas) application was used in order to process the It should be noted that all acceleration-related variables acceleration signal. The LabView application contains a mod- were measured in the first part of tapping, prior to the first ule for receiving accelerometer samples in UDP packets, and hand tap. The examined variables and the time frame of the obtained accelerometer signal was filtered with a low- events are shown on a typical example of the acceleration sig- pass Butterworth filter (order = 5, fcof = 40 Hz). The relevant nal (Figure 2). variables in the movement kinematics were automatically identified after the onset of the motion, which was detected 2.4. Statistical Analysis. For the purposes of this paper, all when the absolute acceleration exceeded 1.15 g . The applica- variables were processed using descriptive statistical analysis tion implements automatic threshold and peak detection in order to determine relevant measures of central tendency, using predefined SubVIs provided by National Instruments data dispersion, and range (mean, StDev, SEM, cV%, Min for both A and abs (A), thus providing the location and/or and Max) for the respective subsamples. The normality of magnitude of relevant kinematic and temporal variables. the distribution of the results was determined by the applica- Detection of the acceleration gradient variables was per- tion of the nonparametric Kolmogorov-Smirnov goodness- formed using the peak detector SubVI on the signal obtained of-fit test (K-S Z). The position of centroid groups’ location, by derivation of the acceleration over time. as a group standardized multivariate score, and the structure of the extracted functions and group differences were defined 2.3. Variables. The following variables acquired from the by discriminant analysis. The level of statistical significance processed hand acceleration signal were used in order to was defined based on the criterion p ≤ 0:05 [28]. All data define the relevant temporal and kinematic characteristics analyses were conducted using Excel 2013 and IBM SPSS of the movement: v23 statistical software. (i) t is the time from the start of the movement to the 3. Results and Discussion first tap of the hand (expressed in s) (ii) t is the time from the first tap to the second tap of Table 1 shows the results of the descriptive statistical analysis the hand (expressed in s) of the relevant kinematic variables in relation to the exam- ined groups, as well as the results of the one-sample nonpara- (iii) A is the maximal acceleration (expressed as a mul- metric Kolmogorov-Smirnov goodness-of-fit test. tiplier of g ) Table 2 shows the summary of the canonical discrimi- (iv) A is the maximal deceleration (expressed as a mul- nant functions and the results of the general statistical differ- tiplier of g ) ences between groups in relation to the examined variables. 0 4 Applied Bionics and Biomechanics are -0.112, -1.220, and 0.748, respectively (Figure 3). These results show that, in relation to DF , the Voll_Nat_Team group centroid position is shifted with -1.968 and -1.108 standard deviation values from the Voll_Youth and the con- trol group, respectively. The difference between control and Voll_Youth is -0.860. The second discriminant function (DF ) did not show a significant difference between the observed groups; thus, the centroid positions of the groups in relation to this function are similar (Figure 3). The factors with the greatest discriminative value among the groups 0.02 0.12 0.22 0.32 0.42 (s) represent the temporal characteristics of the rapid hand movement, i.e., the time elapsed between the onset of the –5 movement and the first (t ) and second (t ) tap, as shown 1 2 in Table 3. –10 Regarding the efficiency of the IMU-based measurement t GA A GA t A t system in relation to the discrimination of the examined sub- 0 1 1 2 1 2 2 –15 samples from the aspect of kinematic characteristics relevant for the rapid hand movement, it was determined that it was abs (A) 65.7% overall (Table 4). It should be pointed out that the Figure 2: Absolute acceleration (abs) and acceleration in the Y- highest accuracy of classification (80.6%) was determined in (dominant) axis with the time frame of relevant events. the subsample of young volleyball players (Voll_Youth), while players in the control group were classified as having Table 3 shows the structure matrix of the extracted func- the lowest accuracy (40.9%). Based on the kinematic charac- tions explaining the determined general differences between teristics of rapid hand movement, 36.4 and 22.7% of the con- groups. trol group was classified in the subsamples Voll_Youth and Voll_Nat_Team, respectively (Table 4). For the subsample Table 4 shows the classification of the group membership in relation to the results of the discriminant analysis based on Voll_Nat_Team, the discriminative efficiency was 70.6%, or the relevant kinematic variables of rapid hand movement. 88.2% when taking into account the participants classified Figure 3 shows the graphical representation of the cen- in the Voll_Youth group. troid position of the examined subsamples in relation to the The presented results show the potential of IMU sensors relevant functions based on the kinematic variables of rapid in relation to the measurement of rapid movement kinemat- hand movement. ics. The discriminative nature of the obtained results indi- Based on the results of the descriptive statistical analysis, cates the applicability of such systems for the purposes of it was determined that the obtained results of the examined assessment, monitoring, and even selection of athletes. kinematic variables of rapid hand movement have acceptable variation, given the fact that the coefficient of variation is 4. Conclusions in the range from 7.87 to 45.00 for t in Voll_Youth and GA in control samples, respectively. The results of the The aim of this paper was to determine the discriminative Kolmogorov-Smirnov goodness-of-fit test indicate that the potential of IMU sensor technology in detecting single rapid examined variables are normally distributed on a general movement variables/characteristics in females with different level (Table 1). The results of Box’s test of equality of covari- training backgrounds and specialization. Rapid hand move- ance matrices have shown that the multiple distribution of ment properties, i.e., relevant kinematic variables in relation the examined groups is similar on a statistically significant to different groups of participants, were examined. The mea- level (M =78:488, F =1:605, p =0:008). On the basis of the surement of the kinematic variables was performed using a aforementioned, it can be argued that the obtained results standard hand tapping test. The sample in this research have average homogeneity [29] and normal distribution included a total of 70 female participants and was divided and belong to the same measurement area which makes them into 3 subsamples, of which one included physically active representative in terms of further scientific interpretation. controls, while the other two consisted of the members The discriminant analysis has identified two functions, of the Republic of Serbia national volleyball team and DF and DF , that explain 91.9 and 8.1% of the variance, national-level young volleyball players, respectively. The dis- 1 2 criminant analysis was used in order to define the centroid respectively. It was determined that DF is statistically signif- icant (p ≤ 0:000). This function is composed of the variables location, as a group standardized multivariate score, as well t and t . The second function DF is composed of the vari- as the structure of the extracted functions and group differ- 1 2 2 ables A , A ,GA , and GA .DF reached a p value of 0.616, ences between the respective subsamples. The discriminant 1 2 1 2 2 thus yielding nonsignificant results (Table 2). This indicates analysis has identified two functions, DF and DF ,that 1 2 that the differences between the examined subsamples origi- explain 91.9 and 8.1% of the variance, respectively. The dif- nate from the variables grouped in DF , i.e., the first function. ferences between the examined subsamples originate from The centroid positions of the examined groups control, Voll_ the variables grouped in extracted function DF , which was Nat_Team, and Voll_Youth in relation to the function DF statistically significant at the level p ≤ 0:000. In relation to (g ) 0 Applied Bionics and Biomechanics 5 Table 1: Basic descriptive statistics of the examined variables in relation to the research subsamples with the results of the one-sample Kolmogorov-Smirnov test. Control Mean SEM StDev cV% Min Max K-S Z Sig. t (s) 22 0.23 0.01 0.03 14.20 0.19 0.29 0.611 0.849 t (s) 22 0.43 0.01 0.05 12.50 0.34 0.54 0.741 0.642 A (g ) 22 3.87 0.25 1.17 30.23 2.02 6.23 0.351 1.000 1 0 A (g ) 22 8.33 0.44 2.06 24.75 5.34 12.24 0.713 0.689 2 0 -1 GA (g ·s ) 22 70.94 5.21 24.42 34.42 36.00 122.13 0.834 0.491 1 0 -1 GA (g ·s ) 22 211.73 20.31 95.27 45.00 84.34 485.88 0.961 0.314 2 0 Voll_Nat_Team Mean SEM StDev cV% Min Max K-S Z Sig. t (s) 17 0.21 0.01 0.03 13.92 0.17 0.26 0.590 0.877 t (s) 17 0.40 0.01 0.04 9.63 0.37 0.50 1.190 0.117 A (g ) 17 3.88 0.21 0.88 22.63 2.17 5.32 0.563 0.909 1 0 A (g ) 17 8.35 0.46 1.91 22.88 4.39 12.07 0.440 0.990 2 0 -1 GA (g ·s ) 17 57.30 5.81 23.97 41.84 23.59 109.81 0.433 0.992 1 0 -1 GA (g ·s ) 17 229.26 17.62 72.63 31.68 142.95 394.64 0.775 0.586 2 0 Voll_Youth Mean SEM StDev cV% Min Max K-S Z Sig. t (s) 31 0.24 0.00 0.03 11.52 0.18 0.30 0.679 0.746 t (s) 31 0.45 0.01 0.04 7.87 0.40 0.52 0.815 0.520 A (g ) 31 3.78 0.18 0.99 26.25 2.48 5.89 0.684 0.737 1 0 A (g ) 31 8.94 0.43 2.42 27.04 4.90 14.16 0.725 0.669 2 0 -1 GA (g ·s ) 31 72.34 4.63 25.79 35.65 37.88 154.98 0.908 0.382 1 0 -1 GA (g ·s ) 31 252.19 17.87 99.51 39.46 96.47 520.85 0.754 0.620 2 0 Table 2: The summary of canonical discriminant functions and Table 4: Classification results. general intergroup differences. Predicted group membership Eigenvalues Voll_ Groups Voll_ Total Control Nat_ %of Cumulative Canonical Youth Function Eigenvalue Team variance % correlation Control 9 5 8 22 1 0.641 91.9 91.9 0.625 Voll_ 2 0.057 8.1 100 0.231 Nat_ 212 3 17 Wilks’ lambda Count Team Test of Wilks’ Chi-square df Sig. Voll_ function(s) lambda 5 1 25 31 Youth 1 0.577 35.492 12 0.000 Original Control 40.9 22.7 36.4 100 2 0.946 3.550 5 0.616 Voll_ Nat_ 11.8 70.6 17.6 100 Team Table 3: The structure matrix. Voll_ 16.1 3.2 80.6 100 Function Youth DF DF 1 2 65.7% of the original grouped cases were correctly classified. 0.516 -0.007 this function, the Voll_Nat_Team group centroid position 0.408 -0.209 was shifted with -1.108 standard deviation values from the 0.145 0.654 control and -1.968 standard deviation values from the Voll_ 0.295 -0.412 Youth group. The difference between the control and Voll_ GA 0.144 0.318 Youth groups was -0.860 standard deviation value. The GA -0.056 -0.093 factors with the greatest discriminative potential among the groups are the variables of the temporal characteristics of 6 Applied Bionics and Biomechanics Voll_Nat_Team Voll_Youth –1 –2 –3 Control –4 –4 –3 –2 –1 01 2 3 4 DF1 Group Control Control Voll_Nat_Team Voll_Nat_Team Voll_Youth Voll_Youth Group Centroid Figure 3: The graphical representation of the centroid position of the examined subsamples. the rapid hand movement, i.e., the time elapsed between the Acknowledgments onset of the movement and the first and second tap, as This work is sponsored in part by the Slovenian Research defined by the variables t and t . The established findings 1 2 Agency within the research program ICT4QoL—Informa- clearly indicate that IMU sensors are practically applicable tion and Communications Technologies for Quality of Life in this context and can be included as a new technology used (research core funding no. P2-0246) and within the bilateral for the purposes of assessment, monitoring, and selection of project between Slovenia and Serbia titled “Sensor technolo- athletes. gies as support systems for the detection and selection of tal- ents in sport and monitoring the performance of athletes” (research core funding no. BI-RS/20-21-023). This paper Data Availability was partially financed by the Slovenian Research Agency bilateral project SRB-SLO for the period 2018-2019 (no. The data used to support the findings of this study are avail- R2-2046). able from the corresponding author upon request. References Disclosure [1] A. Kos and A. Umek, Biomechanical Biofeedback Systems and This paper is a part of the project “Effects of the Applied Applications, Springer International Publishing, 2018. Physical Activity on Locomotor, Metabolic, Psychosocial [2] V. Bachev, M. Gadev, O. Groshev, P. Yordanov, and B. 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Applied Bionics and BiomechanicsHindawi Publishing Corporation

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