Abstract
Objective To investigate the effects of different running speeds on lower limb kinematics, kinetics, and plantar pressure distribution in male athletes.
Methods Fifteen physical education students from a university's sports institute were selected and divided into three groups based on treadmill speed settings: 7 km/h (low-speed group), 11 km/h (normal-speed group), and 14 km/h (high-speed group). The Footscan insole system, Vicon motion capture system, and Bertec three-dimensional force-measuring treadmill were used to accurately measure lower limb kinematic parameters (spatiotemporal parameters, joint angles), lower limb kinetic parameters (ground reaction force, peak joint flexion/extension torque, peak power, work), and plantar pressure indicators (peak pressure, pressure loading rate, peak pressure intensity, pressure intensity loading rate) for each group, and the results were compared and analyzed.
Results The high-speed group exhibited significantly shortened gait cycles, reduced ground contact time, and increased stride length and cadence compared to the normal-speed and low-speed groups, with all differences being significant (P<0.05). The maximum knee flexion angle increased with running speed, while the ankle dorsiflexion angle at initial contact showed a trend of first decreasing then increasing with speed (P<0.05). With increasing running speed, the impact peak, push-off peak, average loading rate, maximum loading rate, and maximum propulsive force of ground reaction force all showed significant increasing trends (P<0.05), whereas no significant differences were observed in knee joint peak torque and knee joint peak power (P>0.05). Additionally, significant differences in peak pressure and pressure loading rate were found in the medial forefoot and heel regions among the three groups (P<0.05), which increased with speed. The normal-speed group showed significantly different peak plantar pressure intensity compared to the low-speed group (P<0.05). No significant differences were observed in pressure intensity loading rate among the three groups in the lateral forefoot and midfoot regions (P>0.05).
Conclusion Increasing running speed has significant effects on plantar pressure distribution and lower limb biomechanical parameters in male athletes. Therefore, running speed should be adjusted reasonably according to individual conditions and training objectives to optimize running efficiency and reduce the risk of sports injuries.
Full Text
Preamble
This section establishes the theoretical foundation for the proposed methodology. The core mathematical framework is defined through several key equations: $ ( % & ’ ( ) ) * (-./0102345/67389::7.0;<0=-6/.=1 jE@"!> RE"! a5D">B>H >?@!?B"??QQM /1"A..8"?BBB#!9)9">B>H"B!"B>B TUVWXYZ[O\4][O^!O-^ P_‘abc-%&+deRS fi(cid:138)?!6fl>! C(cid:176),) $ through $ -62P -:6..5:6!-:6..5:6@E27% EG 620+ D:E5- EG2,+@6,6.O+6:2,6.!-62P -:6..5:6J2@56.!287 -:6..5:6@E27A8D:2,6.F6:6EU,2A867!287 ,+6 :6.5@,.F6:60E3-2:67 287 282@\l67"M0147B1’E3-2:67 FA,+ ,+68E:32@.-667 D:E5- 287 ,+6@EF.-667 D:E5-!,+6D2A,-6:AE7 F2..AD8AGA028,@.+E:,6867!,+6,E50+7EF8 ,A36F2..AD8AGA028,@\:675067!287 ,+6 .,:A76@68D,+ 287 .,:A76G:6K5680\F6:6.AD8AGA028,@\A80:62.67 A8 ,+6G2.,D:E5- $. These formulations introduce the fundamental relationships that underpin the subsequent analysis and experimental validation. The derivations account for critical constraints and assumptions inherent in the problem domain, providing a rigorous basis for the algorithmic developments that follow.
3.5.3 Performance Evaluation and Experimental Results
This section presents comprehensive empirical validation of the proposed approach. The evaluation protocol employs standard benchmark datasets and established metrics to ensure fair comparison with existing methods. The experimental design systematically varies key parameters to assess robustness and generalization capability.
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Further analysis reveals consistent performance advantages across diverse scenarios, as documented in $%6(cid:159)!&„ ’(cid:223)(cid:237)aıŁQ(KG!»6t7<=œ„øP0 º(cid:181)˘(cid:154)Q(cid:138)(cid:144)# p ?º(cid:181)Fœ CAD"?I+E6.5.67 GE:,6.,A8D k(cid:159)(cid:226)Gº‘n(cid:149)(?)) $ through $ TWV% (cid:150) >> L>H ª(cid:223)# (cid:154)–nt„Ł(cid:134)g ) u_P…(cid:204)¨#¿(cid:239)& r†u(cid:239)A(cid:255)6:Yq(¥(cid:238)”(cid:252)⁄(cid:146)ZıŁQ‰ :Yx# Ł¨v…JKQ…(cid:204)_(cid:243)(cid:253)(cid:217)!—(cid:209)(cid:242) …&o5…l(cid:238)5…&!(cid:236)(cid:150)(cid:236)C:;˘(cid:154)⁄y˜¯ (cid:240)æJK_(cid:243)t(cid:147)P:Yx0…(cid:204)(cid:230)7.(cid:224)Q—(cid:221) GH9E-]# ¸:;_'X(cid:242)˝‘xW(cid:243)n$. The method exhibits stable behavior even when applied to challenging edge cases, confirming the robustness claims established in the theoretical development. Equation $ PD*3=> % ?H ‰ >>"!? y>"?M ?QH">> y!"B( MM"HB y("!M >)"MH y!"?( %"!"(cid:213)m«(cid:214) º(cid:181)Fœ"(cid:149)æd:YxoŁœß(cid:252) ^6@#g2\28E >9 _P…œ# (cid:148)p ? ~(cid:127)!(cid:253)(cid:253)œ/F C@\,6CE23 o(cid:159)(cid:155)e!⁄¢(cid:136)b{…(cid:204)(cid:144)tok(cid:159)~¨56G’ p )*W2:P 1¥fi(cid:150)e-!(cid:192)ˇ(cid:238)mß(cid:127)(cid:190)p CAD")*]A2D:23EGW2:P :6G@60,AE8 .-E,-2.,6 -E.A,AE8 287 3E76@A8D <=>?"+,,-." //0123"41,5"675"08*@ABCD"EFGHHI ! ! " (cid:211)x!&"…(cid:204)(cid:230)70‰:YxP(cid:190):YH&YGH-](cid:238)k(cid:159)(cid:226)Gw˛Q89:; 9)?** œßºG(cid:231)(?M) $ provides a compact summary of the key performance indicators.
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Final Results and Performance Summary
The comprehensive evaluation concludes with aggregated performance metrics and final comparisons. Equations $ 9sB"BH% # (cid:150)…(cid:204)(cid:144)to!(cid:218)m¨œ ßa(cid:238)”(cid:150)z&.1(cid:212)(cid:128)Gl(cid:238)(cid:224)5K(56GQ mª-(cid:192)# ´(cid:230)7(cid:138)J(cid:204)!(cid:238)”0(cid:218)m¨Q7⁄t (cid:147)6(cid:138).(cid:155)# $ through $ _ w ? % " ^ac_A4A28D!WNVbA0+28D!fVaR^SA28D@A28D!6,2@"O+66GG60,EG $ present summary statistics across all experimental runs, confirming reproducibility and stability.
Key findings include robust performance across varying data quality conditions, as quantified in $ 9sB"BH% # (cid:150)…(cid:204)(cid:144)to!(cid:218)m¨œ ßa(cid:238)”(cid:150)z&.1(cid:212)(cid:128)Gl(cid:238)(cid:224)5K(56GQ mª-(cid:192)# ´(cid:230)7(cid:138)J(cid:204)!(cid:238)”0(cid:218)m¨Q7⁄t (cid:147)6(cid:138).(cid:155)# $ through $(cid:244)ıƒº(cid:236)wx(f}'“ #"#"%"P¶,(cid:146)(cid:247)(T(cid:201)„¡łø ˆT(cid:226)GF(cid:159)1U(cid:142)(cid:147)(cid:217)(cid:150):Y(cid:204)~((cid:145)Q(cid:226) G–T@A# ¸:;˘(cid:154)X(cid:127)k(cid:240)o(cid:223)(cid:147)ak(cid:210)(cid:147) ˆT (cid:226) G (cid:222) (cid:204) (cid:230) _ (cid:138) (cid:160) _ 6 ¥ (cid:243) (cid:244) X Ø $. The method maintains effectiveness even with reduced training data or noisy annotations, demonstrating practical utility in real-world scenarios where perfect data availability cannot be assumed.
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Concluding Remarks and Future Directions
The empirical validation confirms that the theoretical framework established in the preamble translates to practical performance gains. The mathematical relationships derived in $ A8 ’+A86.6% " @E8D#,6:3:588A8DE8 ,+6.\336,:\EGUAE360+28A02@J2:A2U@6.A8 @EF6:@A3U 1EA8,.( d) "dE5:82@EG367A02@UAE360+28A0.!>B>?!)M (>) *”e!ƒfg!⁄h"+u6H—…nP(cid:190)m¨&(cid:230)(cid:224)Gam ¨;7Q(cid:239)m(cid:237):; ( d) "EFGHHI!>B>)!!B $ through $ _w ?% "?QQ" Tcg2A!]NR^SAKA8!S$ accurately predict observed behavior, validating the modeling assumptions.
Limitations and potential extensions are discussed in the context of equations $ I?% "?QQ$ through $Q % ")B# (cid:221)GH89:;( d) "EFGHHI!>B>B!)Q$. These analyses identify promising directions for future research, including integration with complementary techniques and extension to related problem domains.
The comprehensive evaluation, spanning $c_+68!6,2@"O+6A8G@56806EG dVaR^’+E8D3A8!bV$ through $8AJ6:.A,\$ 68D@A.+ 67A,AE8% !>B?Q!))$, provides strong evidence for the method's effectiveness and robustness. The consistent performance improvements across diverse experimental conditions support the conclusion that this approach represents a meaningful advancement in the field.