Force perturbations during gait training can increase movement variability, which may support motor exploration and learning. However, when such perturbations are delivered through a robotic exoskeleton, they can also reduce perceived exoskeleton transparency, potentially hindering user acceptance. We tested whether visualizing continuous upper leg-level perturbations in immersive virtual reality (VR) could preserve their variability-enhancing effect while mitigating the cost to perceived transparency in a pelvis-centered walking task. Thirty healthy adults walked on a treadmill while wearing a robotic exoskeleton and performed a ball-in-cup task, requiring continuous mediolateral control of the pelvis. Participants trained under one of three conditions: Control (no perturbations), Perturbation (continuous noise-like multisine forces applied at the thighs), or Perturbation + Visual (same forces with a real-time, body-referenced force-beam visualization). Step-width variability was evaluated during Training. Task performance, intrinsic motivation, and perceived transparency were assessed across Baseline, Training, Retention, and a faster-speed Transfer test (120% of preferred speed). Both perturbation conditions significantly increased step-width variability during Training relative to Control, with no detectable difference between Perturbation and Perturbation + Visual. Task performance improved from Baseline to Retention and Transfer across all conditions, with no significant differences across conditions. Motivation did not differ between conditions either. Critically, perceived transparency decreased only in the non-visualized Perturbation condition and remained stable in both Control and Perturbation + Visual. Our results show that continuous leg-level perturbations reliably enrich lateral gait variability and that simple visual force cues can prevent a perceived transparency cost without compromising the variability manipulation. Future work should test adaptive dosing, multi-session training, and clinical cohorts with impaired lateral stability.

Robotic rehabilitation can deliver high-dose gait therapy and improve motor function after a stroke. However, for many devices, high costs and lengthy setup times limit clinical adoption. Thus, we designed, built, and evaluated the Passive Mechanical Add-on for Treadmill Exercise (P-MATE), a low-cost passive end-effector add-on for treadmills that couples the movement of the paretic and non-paretic legs via a reciprocating system of elastic cables and pulleys. Two human-device mechanical interfaces were designed to attach the elastic cables to the user. The P-MATE and two interface prototypes were tested with a physical therapist and eight unimpaired participants. Biomechanical data, including kinematics and interaction forces, were collected alongside standardized questionnaires to assess usability and user experience. Both interfaces were quick and easy to attach, though user experience differed, highlighting the need for personalization. We also identified areas for future improvement, including pretension adjustments, tendon derailing prevention, and understanding long-term impacts on user gait. Our preliminary findings underline the potential of the P-MATE to provide effective, accessible, and sustainable stroke gait rehabilitation.

Gait variability, the subtle fluctuations in walking patterns, is crucial for adaptation and motor learning. While existing methods to increase gait variability often rely on force-based perturbations, these can reduce motivation. This study explored if a subtle visual feedback distortion (VFD), applied to a first-person avatar's foot position in an immersive virtual reality environment, could increase gait variability without such a drawback. Twenty healthy adults walked on a treadmill wearing a head-mounted display and motion trackers, performing a stepping task under two conditions: with and without VFD. The VFD introduced a continuously changing, noise-like offset to the displayed foot positions, designed to be minimally noticeable. We quantified gait variability through the standard deviation of step width and step length and collected self-report measures on embodiment, motivation, and simulator sickness. We found that VFD significantly increased step width variability by about 15%, indicating enhanced lateral adaptability. In contrast, step length variability remained unchanged. Participants adjusted their foot placement in the opposite direction of the visual distortion, supporting the idea that proprioceptive recalibration underpinned the observed changes. Notably, this increase in variability occurred without any significant effects on embodiment, motivation, or simulator sickness. These findings suggest that subtle VFD can enhance gait variability - potentially facilitating motor learning and adaptability - while preserving user experience and motivation. Future research should determine whether such VFD-based interventions yield lasting functional improvements and investigate their applicability in rehabilitation contexts, potentially offering a noninvasive, user-friendly approach to promoting healthy gait dynamics.

Exoskeletons that assist in ankle plantarflexion can improve energy economy in locomotion. Characterizing the joint-level mechanisms behind these reductions in energy cost can lead to a better understanding of how people interact with these devices, as well as to improved device design and training protocols. We examined the biomechanical responses to exoskeleton assistance in exoskeleton users trained with a lengthened protocol. Kinematics at unassisted joints were generally unchanged by assistance, which has been observed in other ankle exoskeleton studies. Peak plantarflexion angle increased with plantarflexion assistance, which led to increased total and biological mechanical power despite decreases in biological joint torque and whole-body net metabolic energy cost. Ankle plantarflexor activity also decreased with assistance. Muscles that act about unassisted joints also increased activity for large levels of assistance, and this response should be investigated over long-term use to prevent overuse injuries.

From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human-robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human-robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.

Research on motor learning has found evidence that learning rate is positively correlated with the learner's motor variability. However, it is still unclear how to robotically promote that variability without compromising the learner's sense of agency and motivation, which are crucial for motor learning. We propose a novel method to enhance motor variability during learning of a dynamic task by applying pseudo-random perturbing forces to the internal degree of freedom of the dynamic system rather than directly applying the forces to the learner's limb. Twenty healthy participants practiced swinging a virtual pendulum to hit oncoming targets, either with the novel method or without disturbances, to evaluate the effect of the method on motor learning, skill transfer, motivation, and agency. We evaluated skill transfer using two tasks, changing either the target locations or the task dynamics by shortening the pendulum rod. The indirect haptic disturbance method successfully increased participants' motor variability during training compared to training without disturbance. Although we did not observe group-level differences in learning, we observed divergent effects on skill generalization. The indirect haptic disturbances seemed to promote skill transfer to the altered task dynamics but limited transfer in the task with altered target positions. Motivation was not affected by the haptic disturbances, but future work is needed to determine if indirect haptic noise negatively impacts sense of agency. Increasing motor variability by indirect haptic disturbance is promising for enhancing skill transfer in tasks that incorporate complex dynamics. However, more research is needed to make indirect haptic disturbance a valuable tool for real-life motor learning situations.

To design effective rehabilitative technology, stakeholders (e.g., professionals from hospitals, universities, and industries) must empathize with end-user experiences and actively involve them throughout the design process. This approach can ensure the understanding of their complex needs. Yet end-user involvement is often limited to testing only. Technology developers often underestimate the valuable insights end-users gain during their recovery, which extend beyond technical knowledge. To address this, our international team of designers, engineers, and clinical personnel proposes a participatory design workshop involving acquired brain injury patients and their caregivers. Patients and caregivers work in groups with workshop participants to address specific needs and use methods like personas, MoSCoW prioritization, and prototyping to co-create solutions to meet those needs. We aim to illustrate the benefits of this approach and encourage participants to adopt participatory design in their future developments.

Trunk motor control is essential for the proper functioning of the upper extremities and is an important predictor of gait capacity in children with delayed development. Early diagnosis and intervention could increase the trunk motor capabilities in later life, but current tools used to assess the level of trunk motor control are largely subjective and many lack the sensitivity to accurately monitor development and the effects of therapy. Inertial measurement units could yield an objective quantitative assessment that is inexpensive and easy-to-implement. We hypothesized that root mean square of jerk, a proxy for movement smoothness, could be used to distinguish age and thereby presumed motor development. We attached a sensor to the trunks of six young children with no known developmental deficits. Root mean square of jerk decreases with age, up to 24 months, and is correlated to a more established method, i.e., center-of-pressure velocity, as well as other standard inertial measurement unit outputs. This metric therefore shows potential as a method to differentiate trunk motor control levels.