The Effects of Running Kinematics on Peak Upper Trunk GPS-Measured Accelerations during Foot Contact at Different Running Speeds
Source abstract
Abstract
The overall aim of this study was to determine the effects of running kinematics on the peak upper trunk segmental accelerations captured with an accelerometer embedded in a commonly used GPS device. Thirteen male participants (age: 27 ± 3.7 years, height: 1.81 ± 0.06 m, mass: 82.7 ± 6.2 kg) with extensive running experience completed a single trial of treadmill running (1 degree inclination) for 40 s at nine different speeds ranging from 10 to 18 km/h at 1 km/h increments. Three-dimensional peak upper trunk acceleration values were captured via a GPS device containing a tri-axial accelerometer. Participants’ running kinematics were calculated from the coordinate data captured by an 18-camera motion capture system. A series of generalized linear mixed models were employed to determine the effects of the kinematic variables on the accelerometer acceleration peaks across the key gait phases of foot contact. Results showed that running kinematics had significant effects on peak accelerometer-measured accelerations in all axes (p < 0.05). Overall, peak segment velocities had a larger effect than joint/segment kinematics on resultant (F values = 720.9/54.2), vertical (F values = 149.8/48.1) and medial–lateral (F values = 55.4/33.4) peak accelerometer accelerations. The largest effect on peak accelerometer accelerations were observed during the impact subphase of foot contact at the adduction/abduction velocity of the shank (F value = 129.2, coefficient = −0.03) and anterior/posterior velocity of the pelvis (F value = 58.9, coefficient = 0.01). Axis-dependent effects of running kinematics were also observed, specifically at the trunk segment in the vertical and anterior–posterior peak accelerometer accelerations. This study showed the intersegmental relationship between joint/segment kinematics, segment velocities and the resulting peak accelerations of the upper trunk during running over several speeds. These findings provide insights into the lower body’s GRF attenuation capacity and its contribution to trunk stability whilst running.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lower Extremity Biomechanics and Pathologies · Diabetic Foot Ulcer Assessment and Management · Balance, Gait, and Falls Prevention
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Health Science and Wellbeing, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
Middlesbrough Football Club, Middlesbrough TS3 6RS, UK
References (39)
Show source-provided references
1. Cronin, N.J. (2021). Using Deep Neural Networks for Kinematic Analysis: Challenges and Opportunities. J. Biomech., 123.10.1016/j.jbiomech.2021.110460 ↗
2. Simultaneous Validation of Wearable Motion Capture System for Lower Body Applications: Over Single Plane Range of Motion (ROM) and Gait Activities10.1515/bmt-2021-0429 ↗
3. Stetter, B.J., Krafft, F.C., Ringhof, S., Stein, T., and Sell, S. (2020). A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments during Various Locomotion Tasks. Front. Bioeng. Biotechnol., 8.10.3389/fbioe.2020.00009 ↗
4. Dorschky, E., Nitschke, M., Martindale, C.F., van den Bogert, A.J., Koelewijn, A.D., and Eskofier, B.M. (2020). CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics from Measured and Simulated Inertial Sensor Data. Front. Bioeng. Biotechnol., 8.10.3389/fbioe.2020.00604 ↗
5. Gholami, M., Rezaei, A., Cuthbert, T.J., Napier, C., and Menon, C. (2019). Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks. Sensors, 19.10.3390/s19235325 ↗
6. Zimmermann, T., Taetz, B., and Bleser, G. (2018). IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning. Sensors, 18.10.3390/s18010302 ↗
7. Effect of Fatigue on Leg Kinematics and Impact Acceleration in Long Distance Running10.1016/S0167-9457(00)00013-0 ↗
8. Relationship of Foot Strike Pattern and Landing Impacts during a Marathon10.1249/MSS.0000000000002032 ↗
9. Comparison of Tibial Shock during Treadmill and Real-World Running10.1249/MSS.0000000000002288 ↗
10. Running Kinematics and Shock Absorption Do Not Change after Brief Exhaustive Running10.1519/JSC.0b013e3181ddfcf8 ↗
11. The Effect of Exercise-Induced Muscle Damage on Shock Dissipation during Treadmill Running
12. The Effects of Knee Contact Angle on Impact Forces and Accelerations10.1249/01.MSS.0000126779.65353.CB ↗
13. Tibial Acceleration and Shock Attenuation While Running over Different Surfaces in a Trail Environment10.1016/j.jsams.2021.03.006 ↗
14. Shock Attenuation and Stride Frequency during Running10.1016/0167-9457(95)00004-C ↗
15. A Prospective Study on Gait-Related Intrinsic Risk Factors for Lower Leg Overuse Injuries10.1136/bjsm.2008.055723 ↗
16. Running Technique Is an Important Component of Running Economy and Performance10.1249/MSS.0000000000001245 ↗
17. Assessing Stride Variables and Vertical Stiffness with GPS-Embedded Accelerometers: Preliminary Insights for the Monitoring of Neuromuscular Fatigue on the Field
18. Validation of a Trunk-Mounted Accelerometer to Measure Peak Impacts during Team Sport Movements10.1055/s-0035-1547265 ↗
19. Contributions of Lower Extremity Kinematics to Trunk Accelerations during Moderate Treadmill Running10.1186/1743-0003-11-162 ↗
20. Body Segment Parameters; a Survey of Measurement Techniques
21. Acceleration Patterns in the Lower and Upper Trunk during Running10.1080/02640414.2013.805884 ↗
22. PlayerLoadTM: Reliability, Convergent Validity, and Influence of Unit Position during Treadmill Running10.1123/ijspp.2013-0418 ↗
23. A Public Dataset of Running Biomechanics and the Effects of Running Speed on Lower Extremity Kinematics and Kinetics10.7717/peerj.3298 ↗
24. Effects of Running Velocity on Running Kinetics and Kinematics10.1519/JSC.0b013e3181c64308 ↗
25. Changes in Three Dimensional Lumbo-Pelvic Kinematics and Trunk Muscle Activity with Speed and Mode of Locomotion10.1016/j.clinbiomech.2005.04.004 ↗
26. A New Anatomically Based Protocol for Gait Analysis in Children10.1016/j.gaitpost.2006.12.018 ↗
27. Multi-Segment Trunk Kinematics during Locomotion and Elementary Exercises10.1016/j.clinbiomech.2011.01.015 ↗
28. Multi-Segment Kinematic Model to Assess Three-Dimensional Movement of the Spine and Back during Gait10.1177/0309364615579319 ↗
29. The Use of a Single Inertial Sensor to Identify Stride, Step, and Stance Durations of Running Gait10.1016/j.jsams.2009.01.005 ↗
30. Do Subjective Assessments of Running Patterns Reflect Objective Parameters?10.1080/17461391.2017.1325072 ↗
31. Report Quality of Generalized Linear Mixed Models in Psychology: A Systematic Review10.3389/fpsyg.2021.666182 ↗
32. Potential of GLMM in Modelling Invasive Spread
33. Quan, W., Zhou, H., Xu, D., Li, S., Baker, J.S., and Gu, Y. (2021). Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis. Healthcare, 9.10.3390/healthcare9101321 ↗
34. Relationships between Ground Reaction Force and Tibial Bone Acceleration Parameters10.1123/ijsb.7.3.303 ↗
35. Lower Extremity Stiffness: Implications for Performance and Injury10.1016/S0268-0033(03)00071-8 ↗
36. The Relationship between Knee Muscle Strength and Knee Biomechanics during Running at 6 and 12 Months after Anterior Cruciate Ligament Reconstruction
37. Monitoring Kinematic Changes with Fatigue in Running Using Body-Worn Sensors10.1109/TITB.2012.2201950 ↗
38. Influence of Neuromuscular Fatigue on Accelerometer Load in Elite Australian Football Players10.1123/ijspp.8.4.373 ↗
39. The Practical Application of a Method of Analysing the Variability of Within-Step Accelerations Collected via Athlete Tracking Devices10.1080/02640414.2019.1699987 ↗