Simulating how humans stand up from a chair

What is it about?

Sit-to-stand (STS) is the movement we make to reach an upright posture from a sitting position. Even though is a gesture that most of us can do easily several times every day, people with neuromuscular impairments can find difficulties when trying to rise from a chair or a bed. Thus, researching the physical and neurophysiological components of the STS is essential for occupational therapies and assistive devices. Schenkman et al. (1990), described the movements sequentially organized in four phases according to the position of the torso and the joints. In a first phase, the torso is leaned forward, in a second phase, the acceleration created in the previous phase is use to raise the torso from the chair, in a third phase, knees and hips are extended to reach a standing position, and in the fourth, the new position is stabilized. Based on that, Caruthers et al. (2016), described the muscle contractions needed for those movements. Unfortunately, up to date conventional methods cannot describe the underlying nervous activity to those muscle contractions. To overcome such problems, predictive modelling can replicate movements and muscle contractions in simulations where a model of the human body is animated by a hypothetical representation of the nervous system. In the present work, we proposed the nervous control for the STS consists of vestibular and muscle-length reflexes. We also tested if this control should be organized following the four phases previously described or if they should be organized in a simplified two-phases schema. Our results are clear, both, the four-phases and the two-phases versions of the reflexes can generally replicate the movement and the muscle patterns of the STS. We concluded that even though STS can be decomposed in a sequence of motor tasks, they can be the result of the superposition of several reflexes acting without being organized by the complexity of a higher structure.

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Why is it important?

The description of the positions, forces, and muscle contraction along the duration of the STS are essential for the development of new advancements in rehabilitation engineering. Any deviation from the trajectories or dynamics shown by healthy subjects can be used as negative feedback for devices that assist or train people with impairments. However, when the origin of the impairment is neurological, like strokes or cerebral palsy, most of these devices fail in improving the performance of the subjects. Unfortunately, the structures of the nervous system are hardly accessible by conventional methods. The electroencephalogram (EEG) can assess the most superficial layer of brain, but the motor control is related to lower structures, like the cerebellum, the midbrain, or the spinal cord. In the case of magnetic resonance imaging (MRI), the subject needs to be introduced inside a cumbersome machine, where mobility is reduced. Predictive modelling can be the solution. Hypothetical configurations of the nervous system are implemented as controllers and which plausibility is related to the accuracy of the predicted movements. This technique has been widely used for gait, but never for STS. The present work represents the first attempt to decipher the underlying neurophysiological activity to the STS by using predictive modelling.

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