Publications

Robotic technology to assist rehabilitation provides practical advantages compared with traditional rehabilitation treatments, but its efficacy is still disputed. This controversial effectiveness is due to different factors, including a lack of guidelines to adapt devices to users’ individual needs. These needs include the specific clinical conditions of people with disabilities, as well as their psychological and cognitive profiles. This pilot study aims to investigate the relationships between psychological, cognitive, and robot-related factors playing a role in human–robot interaction to promote a human-centric approach in robotic rehabilitation. Ten able-bodied volunteers were assessed for their anxiety, experienced workload, cognitive reserve, and perceived exoskeleton usability before and after a task with a lower-limb exoskeleton (i.e., 10 m path walking for 10 trials). Pre-trial anxiety levels were higher than post-trial ones (p < 0.01). While trait anxiety levels were predictive of the experienced effort (Adjusted-r2 = 0.43, p = 0.02), the state anxiety score was predictive of the perceived overall workload (Adjusted-r2 = 0.45, p = 0.02). High–average cognitive reserve scores were predictive of the perception of exoskeleton usability (Adjusted-r2 = 0.45, p = 0.02). A negative correlation emerged between the workload and the perception of personal identification with the exoskeleton (r = −0.67, p-value = 0.03). This study provides preliminary evidence of the impact of cognitive and psychoaffective factors on the perception of workload and overall device appreciation in exoskeleton training. It also suggests pragmatic measures such as familiarization time to reduce anxiety and end-user selection based on cognitive profiles. These assessments may provide guidance on the personalization of training.

Robotic lower limb exoskeletons are wearable devices designed to augment human motor functions and enhance physical capabilities mostly adopted in healthcare and rehabilitation. The field is strongly dominated by rigid exoskeletons driven by electromagnetic actuators constituted by electrical motors, gearboxes, and cylinders. This review focuses on the design and specifications of the actuation systems of lower limb exoskeletons, with the ultimate goal of providing reporting guidelines to allow for full reproducibility. For each paper, we assessed the quality and completeness of technical characteristics with two ad hoc rating scales for motors and reducers; we extracted the main parameters of the actuation unit and a quantitative analysis of the mechanical characteristics of the individual components was carried out considering the exoskeleton application. Overall, we observed a lack of details in reporting on actuation systems equipped on exoskeletons. To overcome this limitation, herein we conclude by proposing a data form and a checklist to provide researchers with a common approach in reporting the mechanical characteristics of the actuation unit of their lower limb exoskeletons. We believe that the convergence of exoskeletons’ literature toward a clearer standardization of design and reporting will boost the development of this technology and its diffusion outside the laboratory.

Despite the recent advancements in the field of wearable robotics, powered lower limb exoskeletons remain a technology strongly restricted to research or clinical settings. Their uptake as a device to assist mobility in the everyday life is still a challenge due to their high-cost and limited adaptability to unconstrained environments. To overcome these limitations, we propose an open-hardware and open-source platform specifically designed for boosting the research on intelligent exoskeletons (InteLLExo). The proposed prototype adopts design solutions based on low-cost materials, standardized components and simple construction aimed at simplifying the replicability of the device and increase its accessibility. In addition, the control architecture of the exoskeleton is developed within the Robot Operating System (ROS) to facilitate the integration of the device with artificial intelligence and neurorobotic techniques. Overall, InteLLExo aims at combining accessible technologies with robotic intelligence with the purpose of accelerating the research on intelligent exoskeletons and their adoption in everyday life.

Powered lower limb exoskeletons (LLEs) have demonstrated significant potential in augmenting mobility and providing rehabilitative support for individuals with gait impairments. However, most assistive exoskeletons rely on predetermined gait trajectories, limiting their effectiveness in unstructured environments. To address this limitation, Environment Adaptive Gait Planning (EAGP) strategies have emerged, focusing on real-time trajectory adaptation based on environmental perception. This work introduces a novel approach to EAGP using Deep Reinforcement Learning (DRL) for generating adaptive foot trajectories, specifically targeting obstacle avoidance during ground walking. The proposed method optimizes trajectory smoothness, environmental interaction, and compliance with exoskeleton kinematic constraints, as validated by simulations. This study advances the state-of-the-art of adaptive gait planning by leveraging the generalization capabilities of DRL, paving the way for enhanced mobility in real-world applications.