Two-Dimensional Mechanical Model of Human Stability in External Force-Caused Fall
Source abstract
Abstract
The paper proposes a two-dimensional model of human stability. The model allows for gaining data on forces and moments of forces being the effect of musculoskeletal interactions of body parts at human joints. The necessary input data are registered with the use of an optoelectronic motion capture system, two force plates, and a dynamometer. The latter measures the magnitude of the external force that is randomly applied to cause a backward fall. Therefore, the position of the participant’s body during the experiment, the external force used to cause the fall, and the ground reaction forces are known. The model proposed has the structure of a closed kinematic chain with one loop and two open subchains and uses the Euler–Newton approach to analyse the motion. In addition to external loads, linear and angular accelerations are also needed. To calculate those, we used a variant of approximation. This consists in carrying out a simultaneous approximation of a given function and its first and second derivatives in the subintervals that shift along the whole data range. The experiments conducted include two activities while maintaining a state of equilibrium and the fall itself. This gives the basis for examining whether the internal forces and moments at human joints differ significantly in both these states. The results and conclusions are discussed in the final part of the paper.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Balance, Gait, and Falls Prevention · Muscle activation and electromyography studies · Sports Performance and Training
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Applied Mechanics, Faculty of Mechanical Engineering, Poznan University of Technology, Jana Pawla II 24, 60-965 Poznan, Poland
Funding
Ministry of Science and Higher Education
References (28)
Show source-provided references
1. World Health Organization (2007). WHO Global Report on Falls Prevention in Older Age, World Health Organization.
2. Epidemiology, morbidity and mortality from fall-related injuries in the United Arab Emirates10.1186/s13049-014-0051-5 ↗
3. Mroczkowski, A. (2020). Susceptibility to Head Injury during Backward Fall with Side Aligning of the Body. Appl. Sci., 10.10.3390/app10228239 ↗
4. Risk of non-collision injuries to public transport passengers: Synthesis of evidence from eleven studies10.1016/j.jth.2019.03.017 ↗
5. Trajectories in physical performance and fall prediction in older adults: A longitudinal population-based study10.1111/jgs.17995 ↗
6. Howcroft, J., Lemaire, E.D., Kofman, J., and McIlroy, W.E. (2017). Elderly fall risk prediction using static posturography. PLOS ONE, 12.10.1371/journal.pone.0172398 ↗
7. Deep neural networks for human’s fall-risk prediction using force-plate time series signal10.1016/j.eswa.2021.115220 ↗
8. Application of Artificial Neural Networks in Fall Prediction
9. Fall prediction based on key points of human bones10.1016/j.physa.2019.123205 ↗
10. Prospective elderly fall prediction by older-adult fall-risk modeling with feature selection10.1016/j.bspc.2018.03.005 ↗
11. Utility of center of pressure measures during obstacle crossing in prediction of fall risk in people with Parkinson’s disease10.1016/j.humov.2019.03.010 ↗
12. FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications10.1109/JSEN.2020.3018335 ↗
13. A Human Fall Detection Method based on Machine Learning and TRIZ Theory
14. Winter, D.A. (2005). Biomechanics and Motor Control of Human Movement, John Wiley & Sons, Inc.. [3rd ed.].
15. Dumas, R., and Wojtush, J. (2018). Handbook of Human Motion, Springer.
16. Modeling the neuro-mechanics of human balance when recovering from a fall: A continuous-time approach
17. Hettich, G., Fennell, L., and Mergner, T. (2011, January 4–7). Double Inverted Pendulum Model of Reactive Human Stance Control, Multibody Dynamics. Proceedings of the ECCOMAS Thematic Conference, Brussels, Belgium.
18. Design of a Human Lower Limbs Exoskeleton for Biomechanical Energy Harvesting and Assist Walking10.1002/ente.202000726 ↗
19. Berret, B., Darlot, C., Jean, F., Pozzo, T., Papaxanthis, C., and Gauthier, J.P. (2008). The Inactivation Principle: Mathematical Solutions Minimizing the Absolute Work and Biological Implications for the Planning of Arm Movements. PLoS Comput. Biol., 4.10.1371/journal.pcbi.1000194 ↗
20. A 4-Link of a Human for Simulating a Forward Fall
21. Motion Planning and Adaptive Neural Tracking Control of an Uncertain Two-Link Rigid–Flexible Manipulator With Vibration Amplitude Constraint10.1109/TNNLS.2021.3054611 ↗
22. Lu, S., Ban, Y., Zhang, X., Yang, B., Liu, S., Yin, L., and Zheng, W. (2022). Adaptive control of time delay teleoperation system with uncertain dynamics. Front. Neurorobotics, 16.10.3389/fnbot.2022.928863 ↗
23. Simulation Biomechanical Modeling of Human Walking
24. Modelling of Forward Fall on Outstretched Hands as a System with Ground Contact
25. A Least-Squares Estimation Approach to Improving the Precision of Inverse Dynamics Computations10.1115/1.2834295 ↗
26. A general strategy based on the Newton–Euler approach for the dynamic formulation of parallel manipulators10.1016/S0094-114X(98)00081-0 ↗
27. A model-based motion capture marker location refinement approach using inverse kinematics from dynamic trials10.1002/cnm.3283 ↗
28. Optimal estimation of complex aerial movements using dynamic optimisation10.1080/14763141.2022.2066015 ↗