Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.

Despite our environment often being uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling.

During haptic communication, the central nervous system compensates distinctly for delay and noise.

Physically connected humans have been shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is important to understand the impact that network features such as lag and noise may have on human behaviour. In this paper, we investigated interaction with a human-like robot controller that provides similar haptic communication behaviour as human-human interaction and examined the influence and compensation mechanisms for delay and noise on haptic communication.

Control limitations in the null-space of the wrist muscle system.

The redundancy present within the musculoskeletal system may offer a non-invasive source of signals for movement augmentation, where the set of muscle activations that do not produce force/torque (muscle-to-force null-space) could be controlled simultaneously to the natural limbs. Here, we investigated the viability of extracting movement augmentation control signals from the muscles of the wrist complex. Our study assessed (i) if controlled variation of the muscle activation patterns in the wrist joint's null-space is possible; and (ii) whether force and null-space cursor targets could be reached concurrently.

A biomechanics and energetics dataset of neurotypical adults walking with and without kinematic constraints.

Numerous studies have explored the biomechanics and energetics of human walking, offering valuable insights into how we walk. However, prior studies focused on changing external factors (e.g.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals.

Background: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention.