User-Tailored Learning to Forecast Walking Modes for Exosuits

Assistive robotic devices, like soft lower-limb exoskeletons or exosuits, are widely spreading with the promise of helping people in everyday life. To make such systems adaptive to the variety of users wearing them, it is desirable to endow exosuits with advanced perception systems. However, exosuits have little sensory equipment because they need to be light and easy to wear. This paper presents a perception module based on machine learning that aims at estimating 3 walking modes (i.e., ascending or descending stairs and walking on level ground) of users wearing an exosuit. We tackle this perception problem using only inertial data from two sensors. Our approach provides an estimate for both future and past timesteps that supports control and enables a self-labeling procedure for online model adaptation. Indeed, we show that our estimate can label data acquired online and refine the model for new users. A thorough analysis carried out on real-life datasets shows the effectiveness of our user-tailored perception module. Finally, we integrate our system with the exosuit in a closed-loop controller, validating its performance in an online single-subject experiment.

Point Cloud-based Grasping for Soft Hand Exoskeleton

Grasping is a fundamental skill for interacting with and manipulating objects in the environment. However, this ability can be challenging for individuals with hand impairments. Soft hand exoskeletons designed to assist grasping can enhance or restore essential hand functions, yet controlling these soft exoskeletons to support users effectively remains difficult due to the complexity of understanding the environment. This study presents a vision-based predictive control framework that leverages contextual awareness from depth perception to predict the grasping target and determine the next control state for activation. Unlike data-driven approaches that require extensive labelled datasets and struggle with generalizability, our method is grounded in geometric modelling, enabling robust adaptation across diverse grasping scenarios. The Grasping Ability Score (GAS) was used to evaluate performance, with our system achieving a state-of-the-art GAS of 91% across 15 objects and healthy participants, demonstrating its effectiveness across different object types. The proposed approach maintained reconstruction success for unseen objects, underscoring its enhanced generalizability compared to learning-based models.

Environment-based Assistance Modulation for a Hip Exosuit via Computer Vision

Just like in humans vision plays a fundamental role in guiding adaptive locomotion, when designing the control strategy for a walking assistive technology, Computer Vision may bring substantial improvements when performing an environment-based assistance modulation. In this work, we developed a hip exosuit controller able to distinguish among three different walking terrains through the use of an RGB camera and to adapt the assistance accordingly. The system was tested with seven healthy participants walking throughout an overground path comprising of staircases and level ground. Subjects performed the task with the exosuit disabled (Exo Off), constant assistance profile (Vision Off ), and with assistance modulation (Vision On). Our results showed that the controller was able to promptly classify in real-time the path in front of the user with an overall accuracy per class above the 85%, and to perform assistance modulation accordingly. Evaluation related to the effects on the user showed that Vision On was able to outperform the other two conditions: we obtained significantly higher metabolic savings than Exo Off, with a peak of about -20% when climbing up the staircase and about -16% in the overall path, and than Vision Off when ascending or descending stairs. Such advancements in the field may yield to a step forward for the exploitation of lightweight walking assistive technologies in real-life scenarios.