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25 Oct 2024
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Feedback-driven adaptation of gravity-related sensorimotor control to an upside-down posture

An inverse gravity experiment supports the theory of an internal gravity model in the central nervous system

Recommended by ORCID_LOGO based on reviews by Jan Hondzinski and 3 anonymous reviewers

The study by Barbusse et al. (2024) investigated how motor control of arm movements is affected by reversed gravity. It is commonly assumed that the central nervous system contains an internal gravity model, and that this model is used to optimize movements to minimize effort under the influence of gravity (e.g., Berret et al., 2008). Previously, the effect of decreased and increased gravity was investigated, and it was shown that people were able to adapt to this novel environment in a matter of minutes or days (e.g., Gaveau et al., 2011). Therefore, the authors investigated the effect of inverse gravity on motor control of arm movements.

In this study, an experiment was performed in which participants were placed in an inversion table and asked to perform as many pointing movements with their shoulder as possible in 12 blocks. In each block, the inversion table was placed either in the head-up or head-down position, and the position was switched every 35 seconds, starting from the head-up position. After 4 blocks, a 90 second break was taken. It was found that movement duration and amplitude did not significantly differ between both orientations. An analysis of the difference in time to peak acceleration, time to peak velocity, and time to peak deceleration between upward and downward movements revealed no significant difference for the peak acceleration, while for the peak velocity, the time difference was significantly smaller in the head-down than the head-up position, and for the peak deceleration, the time difference changed in the head-down position with the number of blocks, reaching a value more similar to the head-up (baseline) position.

The time to peak acceleration did not reverse for the head-down position, which showed that the central nervous system is not able to take advantage of gravity when it is placed in a head-down position, since it does not take advantage of the “free” acceleration provided by gravity. A longer exposure to inverse gravity might allow the body to adapt and re-optimize its internal gravity model to the new situation. The time difference was significantly different for the deceleration, but not for acceleration, which indicates that the movement was adapted mainly by feedback control, but that feedforward control remained largely the same. This further supports the conclusion that the central nervous system had not yet adapted its internal gravity model, and that re-optimization starts with adapting feedback control (Izawa et al., 2008). An important limitation is the discomfort that is experienced in the head-down position, which not only changes gravity, but also created negative physiological responses.

References

Denis Barbusse, Sarah Amoura, Jérémie Gaveau, Olivier White (2024) Feedback-driven adaptation of gravity-related sensorimotor control to an upside-down posture. OSF preprints, ver.3 peer-reviewed and recommended by PCI Health & Movement Sciences. https://doi.org/10.17605/OSF.IO/D9JPF.

Berret B, Darlot C, Jean F, Pozzo T, Papaxanthis C, Gauthier JP (2008) The inactivation principle: mathematical solutions minimizing the absolute work and biological implications for the planning of arm movements. PLoS computational biology, 4, e1000194. https://doi.org/10.1371/journal.pcbi.1000194

Gaveau J, Paizis C, Berret B, Pozzo T, Papaxanthis C (2011) Sensorimotor adaptation of point-to-point arm movements after spaceflight: the role of internal representation of gravity force in trajectory planning. Journal of Neurophysiology, 106, 620–629. https://doi.org/10.1152/jn.00081.2011

Izawa J, Rane T, Donchin O, Shadmehr R (2008) Motor adaptation as a process of reoptimization. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 28, 2883–2891. https://doi.org/10.1523/JNEUROSCI.5359-07.2008

Feedback-driven adaptation of gravity-related sensorimotor control to an upside-down postureDenis Barbusse, Sarah Amoura, Jérémie Gaveau, Olivier White<p>The ability to move is a vital and essential feature of human existence. &nbsp;We are experts at producing a variety of movements and have refined their control through evolution. As gravity is a major feature of our every-day environment, we h...Biomechanics, Sensorimotor ControlAnne KoelewijnAnonymous, Anonymous2023-12-14 11:47:30 View
09 Jan 2025
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Improved accuracy of the whole body Center of Mass position through Kalman filtering

 Improved estimation of the whole-body center of mass, a step ahead in biomechanical analyses of balance control.  

Recommended by ORCID_LOGO based on reviews by Maarten Afschrift, Guillaume Durandeau and 1 anonymous reviewer
Estimation of the whole-body center of mass (CoM) is crucial in many biomechanical studies of human and animal movement. It is especially important in studies on the control of balance. For example, it has been assumed that sensory information is used to correct the horizontal position and velocity of the CoM (van Dieën et al., 2024; Wang and Srinivasan, 2014; Welch and Ting, 2008), to stabilize standing and walking against gravity. The studies cited have used more-or-less sophisticated estimates of the CoM, derived from kinematic, in some cases combined with anthropometric data, to predict motor outputs. These studies have provided support for the notion that the position and velocity of the CoM are controlled. This holds promise for the diagnosis of the quality of such feedback control as a cause of balance impairments and fall risk. However, such applications will suffer from errors in outcomes at the individual level, for example due to a poor fit of the anthropometrical model to a certain individual.
 
Le Mouel (Le Mouel, 2025) presents a novel approach to estimate the position of the CoM. The author proposes that CoM estimation can be improved by optimally combining kinematic and kinetic data through a Kalman filter. The Kalman-filter-based method was indeed shown to effectively addresses the inherent limitations of both kinematic and kinetic methods used in isolation. The author used an innovative approach to validate CoM estimates, based on incorrect CoM estimates violating Newton's laws of motion. The new method substantially reduced errors compared to conventional approaches based on kinematic (and anthropometric) or kinetic data only. The paper presents a clear and comprehensive description of the method and code implementation is provided such that the method can be easily adopted by colleagues in the field. The author also shows how the new method improves the analysis of stabilizing feedback control of walking, demonstrating the promise it holds for the analysis of balance control. 
 
The method was tested on a small data set and further testing, preferably with participant pool showing large variance in anthropometrical properties, seems warranted. This may also lead to further improvement of the approach. For example, the anthropometrical model used could be refined by using regression equations that take into account segment circumferences of the individual tested (Zatsiorsky, 2002) or even by using individual imaging data. However, the proposed optimal combination of kinematic and kinetic data is likely to become a cornerstone of future methods for accurate CoM estimation.
 

References
- Le Mouel, C., 2025. Improved accuracy of the whole body Center of Mass position through Kalman filtering. bioRxiv, ver.3 peer-reviewed and recommended by PCI Health & Movement Sciences. https://doi.org/10.1101/2024.07.24.604923
- van Dieën, J.H., Bruijn, S.M., Afschrift, M., 2024. Assessment of stabilizing feedback control of walking: a tutorial. J Electromyogr Kinesiol 78, 102915. https://doi.org/10.1016/j.jelekin.2024.102915
- Wang, Y., Srinivasan, M., 2014. Stepping in the direction of the fall: the next foot placement can be predicted from current upper body state in steady-state walking. Biol Lett 10(9), 20140405. https://doi.org/ 10.1098/rsbl.2014.0405
- Welch, T.D., Ting, L.H., 2008. A feedback model reproduces muscle activity during human postural responses to support-surface translations. J Neurophysiol 99, 1032-1038. https://doi.org/ 10.1152/jn.01110.2007
- Zatsiorsky, V., 2002. Kinetics of Human Motion. Human Kinetics, Champaign, Illinois.
Improved accuracy of the whole body Center of Mass position through Kalman filtering Charlotte Le Mouel<p>The trajectory of the body center of mass (CoM) is critical for evaluating balance. The position of the CoM can be calculated using either kinematic or kinetic methods. Each of these methods has its limitations, and it is difficult to evaluate ...BiomechanicsJaap van Dieen2024-07-25 10:58:37 View