CC BY
14SPORTS TRAINING
Athletic biomechanical system trajectory modeling experiment using body mass
and length
UDC 796.012
Dr. Hab., Professor V.I. Zagrevskiy1, 3
Dr. Hab., Professor O.I. Zagrevskiy2, 3
1Mogilev State A. Kuleshov University, Mogilev, Belarus
2Tyumen State University, Tyumen
3National Research Tomsk State University, Tomsk
Corresponding author: fizkult@teoriya.ru
Abstract
Objective of the study was to offer and substantiate by computation experiments basics of the athletic biomechanical system trajectory modeling using the mass-inertial characteristics and elementary kinematics of the body parts.
Methods of the study. We used for the purposes of the study system-structuring analysis and movement design mathematical/ simulation/ modeling tools to model the biomechanical system kinematics in the computation experiment.
Computation experiment was designed to model the athlete's musculoskeletal system movements using a biomechanical system movement synthesizing mathematical toolkit. The athlete's musculoskeletal system movement model may be described as the limited kinematic diagram of the connected bodily elements with cylindrical joints that models a biomechanical system plane rotation process around a contact/ support point
Results and conclusion. The biomechanical system trajectory modeling experiment showed that when the biomechanical system rotates around a contact point, provided the programmed control and startup conditions are the same,
• Growths/ falls in masses of the model elements cause no effect on the biomechanical system trajectory;
• Elementary angular velocity angular velocity is directly correlated with the length of element i.e. the higher is the element's length the higher is the angular velocity and vice versa.
Keywords: biomechanical system trajectory, sport exercise, mass-inertial characteristics, athlete's body elements.
Background. Modern physical education theory and practice gives room for the belief that individual anthropometric characteristics are correlated with the sport techniques albeit it is seldom if ever substantiated by sound theoretical provisions [1, 2]. We know only in a few study reports making attempts to find and explain correlations between an athletic biomechanical system trajectory and body mass/ length [4, 5]. Presently these and other associating issues are still relevant and of special interest for the sport practice and, hence, there is a need for a theoretical understanding of the phenomena with due biomechanical arguments for one or another athletic technical performance concept [6].
Objective of the study was to offer and substantiate by computation experiments basics of the athletic biomechanical system trajectory modeling using the mass-inertial characteristics and elementary kinematics of the body parts.
Methods of the study. We used for the purposes of the study system-structuring analysis and movement design mathematical/ simulation/ modeling tools to model the biomechanical system kinematics in the computation experiment.
Results and discussion. Computation experiment was designed to model the athlete's musculoskeletal system movements using a biomechanical system movement synthesizing mathematical toolkit.
The athlete's musculoskeletal system movement model may be described as the limited kinematic diagram of the connected bodily elements with cylindrical joints that models a biomechanical system plane rotation process around a contact/ support point: see Figure 1.
t Programmed control
Function Speed Acceleration
u, u2 u, d2 U
0,00 0,00 0,00 0,00 0,00 0,00 0,00
0,06 6,41 6,51 1,87 2,41 0,29 17,11
0,12 12,88 16,55 1,74 2,51 -6,85 -21,99
0,18 17,97 22,80 1,13 1,48 -12,53 -9,56
0,24 20,63 26,88 0,49 0,76 7,99 -14,83
0,30 21,67 28,04 0,18 0,05 -2,25 -8,94
0,36 22,23 27,20 0,29 -0.63 3,96 -14,36
0,42 23,26 23,42 0,01 -1,51 13,99 -16,06
0,48 21,55 16,03 -0,94 -3,73 -18,65 -48,72
0,54 15,65 0,80 -3,13 -2,33 -47,27 69,96
0,60 0,00 0,00 0,00 0,00 0,00 0,00
M ! -Z
P ="
Y cosP + ZAZu cosP - P)-ZA,ppsinP-P)
ZZAcosp -P)
(1)
Figure 1. Three-element musculoskeletal system movement kinematics
Let us use the following notations for the model: N
- number of elements in the model; i - alphabetic index of every element; (i = 1, 2, ..., N); L - length of the i-th element; Si - distance from the contact point (rotation axis) of the i-th element versus the mass center; (ft
- i-th element inclination angle to the Ox axis (generalized coordinates); ^ - generalized speed of the i-th element (i = 1,..., N); and ^ - generalized acceleration of the i-th element (i = 1,., N).
The biomechanical system movement approximating mathematical model with programmed control at the kinematic level that we developed [3] using the Lagrange formal toolkit, is the following:
Table 1. Programmed/controlled kinematics (U1, U2) of the model joints
Whereas: M1 - frictional moment; Y - generalzed force of the i-th element (i = 1,..., N); and Aij - dynamic coefficients of the model elements (i = 1,., N; j = 1,., N).
Dynamic coefficients Ay, Y of the model elements were computed using the algorithms described in the prior study [3]. Model (1) has no analytical solution, and that is why we used the Runge-Kutta numerical method with the fourth-order accuracy in our computation experiments. We computed the generalized coordinates of every model element and derivatives in time at every integration step using the following algorithm:
(2)
», =<P<* ■? <Pi
The computation experiment modeled the second half of a full backward swing on a gymnastics bar. The computation experiment conditions were formulated as follows:
Timing (temporal movement characteristics):
Startup: t0 = 0 s; final: t10 = 0,6 s; integration step h = 0,06 s. (3)
Startup conditions of the movement: if, =270°. <f. =270°, p, =270°;
=6podlc. q>.=bpodlc, =6pod/c, (radiant/s) (4)
Programmed control of the biomechanical system is given in Table 1.
Computation experiment designs with variations in the model kinematics and elementary mass-inertial characteristics:
Computation experiment-1 implied unvaried model elements:
(L1=L2=L3=0,6 m; S1=S2=S3=0,3 m) and varied elementary mass-inertial characteristics as follow: Option I: m1= m2= m3= 25,0 Kr; Jc1= Jc2 =Jc3=0,750 Kr-M2; Option II:- m1= m2= m3= 12,5Kr; Jc1= Jc2 =Jc3=0,375Kr-M2; (5) Option IN:m= m2= m3= 50,0 Kr; Jc== Jc2 =Jc3=1,500 Kr-M2.
Computation experiment-2 Computation experiment -1 implied unvaried elementary mass-inertial characteristics:
(m1= m2= m3= 25,0 Kr; Jc1= Jc2 =Jc3=0,750 Kr-M2 and varied kinematics:
Option IY: m1= m2= m3= 25,0 Kr; Jc== Jc2 =Jc3=0,750 Kr-M2; Option Y m1= m2= m3= 12,5 Kr; Jc1= Jc2 =Jc3=0,375 Kr-M2; (6) Option YI m1= m2= m3= 50,0 Kr; Jc1= Jc2 =Jc3=1,500 Kr-M2.
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Given hereunder are the modeled synthesized biomechanical system trajectories. Table 2 gives the biomechanical system movement trajectory model produced by Computation experiment-1.
The above computation experiment-2 product shows that when the programmed control is the same, masses of the elements cause no effect on the biomechanical system trajectory (Table 2). Figure 2 hereunder gives the biomechanical system movement trajectory model produced by computation experiment-2.
Table 2. Biomechanical system trajectory model produced by computation experiment-1
Three-element biomechanical system trajectory
0,00
0,06
0,12
0,1 г
0,24
0,30
0,36
0,42
0,48
0,54
0,60
Option 1
Option 2
Option 3
Generalized coordinates
Ф1 Ф2 Ф3
270,00 270,00 270,00
290,44 296,85 303,36
310,38 323,26 339,81
330,81 348,78 371,58
351,69 372,32 399,20
372,35 394,02 422,06
392,04 414,27 441,47
410,83 434,09 457,51
430,48 452,03 468,06
452,53 468,18 468,98
477,01 477,01 477,01
Option 4
Option 5
Option 6
Figure 2. Biomechanical system movement trajectory model produced by computation experiment-2 with varied elementary mass-inertial characteristics
The computation experiment-2 product shows (Figure 2) that, when the programmed control and the startup movement conditions are the same whilst length of the elements grows, the elementary angular velocity and rotation angle grow as well; and vice versa, when the element gets shorter, the elementary AC and rotation angle fall respectively.
Conclusion. The biomechanical system trajectory modeling experiment showed that when the biomechanical system rotates around a contact point, provided the programmed control and startup conditions are the same, then:
• Growths/ falls in masses of the model elements cause no effect on the biomechanical system trajectory;
• Elementary angular velocity angular velocity is directly correlated with the length of element i.e. the higher is the element's length the higher is the angular velocity and vice versa.
References
1. Gymnastics. Textbook for technical schools of physical culture. A.T. Brykin, V.M. Smolevskiy [ed.]. Moscow: Fizkultura i sport publ., 1985. 309 p.
2. Evseev S.V. ForExercise machines to build motor actions in gymnastics. Study guide. Leningrad: Lesgaft SCOLIPE publ., 1987. 91 p.
3. Zagrevskiy V.I., Zagrevskiy O.I., Lavshuk D.A. Lagrange and Hamilton's formalism in modeling movements of biomechanical systems. Mogilev: Kuleshov MSU publ., 2018. 296 p.
4. Korenberg V.B. Performance reliability in gymnastics. Moscow: Fizkultura i sport publ., 1970. 192 p.
5. Tadzhiev M.U., Isyanov R.Z. Difficult acrobatic jumping combinations. Tashkent, 1969. 160 p.
6. Sosunovskiy V.S., Zagrevskaya A.I. Kinesiologi-cal educational technology in physical education of preschoolers. Teoriya i praktika fiz. kultury. 2020. no.11. pp. 68-70.
Benefits of smart yachting technologies for russian yachting reserve training service
Dr. Hab., Professor Dr. Hab., Professor V.V. Ryabchikov1 Dr. Hab., Professor S.M. Ashkinazi1 PhD, Associate Professor V.S. Kulikov1 PhD, Associate Professor N.S. Skok1
1Lesgaft National State University of Physical Education, Sport and Health, St. Petersburg
Corresponding author: v.riabchikov@lesgaft.spb.ru
Abstract
Objective of the study was to analyze benefits of the modern smart yachting technologies for the national yachting sport reserve technical and tactical skills training service.
Methods and structure of the study. In 2019-2021, our research team from the Sports, Health Technologies and Socio-Economic Issues Research Institute in Lesgaft National State University of Physical Education, Sport and Health completed the 'Research innovations to improve the sailing sport reserve technical and tactical skills training service Improvement' Project on the relevant state order. The Project included an experiment to pilot and test benefits of the 'SailData' smart yachting technologies (Italy) and Fast Skipper smart yachting technologies (Russia, St. Petersburg). We sampled for the study the 14-18 year-old sport reserve yachters (n=48) sailing two-person 420/ 470 dinghy and Swan 50 yachts. The smart yachting technologies were operated in the 'crew and coach' setting, with the sensors on the sailing craft designed to read, on a real-time basis, the key data for communication and control. The coaches used tablets with Android-based special software to track and analyze the sailing process.
Conclusion. The studies completed under the Research Innovations to Improve the Sailing sport reserve technical and tactical skills Training Service Improvement' Project gave us the grounds to recommend the following sport reserve training service improvement actions:
- Apply the modern smart yachting technologies for the sailing craft location, control and training service excellence purposes;
- Train the yachters on a systematic and purposeful basis to improve their competitive yachting tactics in regattas; and
- Improve the special physical training service to make the yachters perfectly fit for the sport-specific technical and tactical actions.
Furthermore, we recommended the following yachting sport reserve training service improvement options:
- Special mental training to improve the volitional control, emotional balancing and attention control skills;
- Special conflict-management training to improve the teamwork;
- Competitive mindset formation skills training with a special attention to the potential psychological barriers;
- Efficient interpersonal communication/ cooperation skills training; and
- Special mental training to counter the opponents' psychological tricks and manipulations.
Keywords: sailing, sport reserve, technical and tactical skills, smart yachting technologies, psychological support service.
UDC 796.011,004.9
Background. Analysis of the modern studies of the yachting elite training systems [4] highlighted a few problems and drawbacks in the technical and tactical skills training and excellence service. The national coaches and yachters still use a very limited digital toolkit for the technical and tactical skills analysis dominated by the traditional video records using smart phones without specialized software. Modern smart yachting technologies are seldom if ever used for the location and tracking purposes by the Russian yacht-
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ing sport elite. Furthermore, most of the sport reserve yachters are tested with very superficial knowledge, skills and experience in the modern smart yachting tactics. The situation is further complicated by the fact that the sport reserve physical fitness tests show poor fitness vertically in every key physical quality including endurance, movement coordination, speed, etc. that need to be trained by special individualized exercises.
Objective of the study was to analyze benefits of the modern smart yachting technologies for the na-
