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. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a characteristic relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success.

SUMMARY: Over the past decade, research on human–robot collaboration has grown exponentially, motivated by appealing applications to improve the daily life of patients/operators. A primary requirement in many applications is to implement highly “transparent” control laws to reduce the robot impact on human movement. This impact may be quantified through relevant motor control indices. In this paper, we show that control laws based on careful identification procedures improve transparency compared to classical closed-loop position control laws. A new performance index based on the ratio between electromyographic activity and limb acceleration is also introduced to assess the quality of human exoskeleton interaction.

Time and effort are thought to be subjectively balanced during the planning of goal-directed actions, thereby setting the vigor of volitional movements. Theoretical models predicted that the value of time should then amount to high levels of effort. However, the time-effort trade-off has so far only been studied for a narrow range of efforts. To investigate the extent to which humans can invest in a time-saving effort, we used a robotic exoskeleton to substantially vary the energetic cost associated with a certain vigor during reaching movements. In this situation, minimizing the time-effort trade-off should lead to high and low human efforts for upward and downward movements, respectively. Consistently, all participants expended substantial amounts of energy upward and remained essentially inactive by harnessing the work of gravity downward, while saving time in both cases. A common time-effort trade-off may therefore determine the vigor of reaching movements for a wide range of efforts.

Despite exoskeletons becoming widespread tools in industrial applications, the impact of the design of human–exoskeleton physical interfaces has received little attention. This study aims at thoroughly quantifying the influence of different physical human–exoskeleton interfaces on subjective and objective biomechanical parameters. To this aim, 18 participants performed elbow flexion/extension movements while wearing an active exoskeleton with three different physical interfaces: a strap without any degree of freedom, a thermoformed orthosis with one (translation) and three degrees of freedom (translation and rotations). Interaction efforts, kinematic parameters, electromyographic activities, and subjective feelings were collected and examined during the experiment. Results showed that increasing the interaction area is necessary to improve the interaction quality at a subjective level. The addition of passive degrees of freedom allows significant improvements on both subjective and objective measurements. Outcomes of this study may provide fundamental insights to select physical interfaces when designing future exoskeletons.

Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency. In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different “transparent” controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics. In other words, very different residual interaction efforts do not necessarily induce very different movement kinematics. Such a behavior could be explained by a natural human tendency to expend effort to preserve their preferred kinematics, which should be taken into account in future transparent controllers evaluation.