This paper presents a method to compute smooth ankle trajectories for lower limb exoskeletons with powered ankle joints. The proposed approach defines ankle trajectories using four polynomial functions, each representing one of the four primary phases of gait. These polynomials are computed according to different safety constraints. During the single support phase, ground contact constraints are enforced. In the swing phase, an optimization problem is solved to achieve minimum jerk planning while respecting a set of equality and inequality constraints designed to minimize the risk of stumbling. The used approach focuses on making the ankle joint able to smoothly adapt in real-time to different walking styles defined by user-selected gait parameters such as step length and clearance. The primary aim is to improve the user experience by producing a secure and comfortable walking pattern. To validate the effectiveness of the proposed method, the new ankle trajectories were tested on a group of healthy volunteers using the TWIN lower limb exoskeleton.

Stability and repeatability of Pattern Recognition (PR) myoelectric control for upper limb prosthetic devices remain unresolved challenges in multi-DoFs systems. In this study, we tested several state-of-the-art classifiers to compare their offline performance in different configurations. Parameters such as realization costs, overall encumbrance, and algorithm complexity were considered for the analysis. The results showed that NLR performed comparably to LDA but with fewer EMG sensors. This study demonstrated that sensor numbers can be reduced to a few units for various algorithms, with NLR being the most tolerant due to its non-linearity. In conclusion, NLR can effectively control the multi-DoFs Hannes system in real-time, offering similar performances to other algorithms while reducing the system's complexity and encumbrance as compared to LDA. It also offers improved tolerance to reduced available information and lower implementation costs.

Upper Limb (UL) Rehabilitation in Multiple Scle- rosis (MS) is an open research field due to the complex interplay between cognitive and physical dysfunctions. Virtual Reality (VR) can face such an issue by enriching physical training with engaging features, including biofeedback strategies to self- regulate autonomic functions according to the visualisation of indices like heart rate variability (HRV). In the present work, HRV biofeedback is introduced in a VR-based exergame (a game designed to promote exercising), tailored to rehabilitation of the dominant upper limb in Persons with MS (PwMS). The exergame is based on a dual-task paradigm, integrating a UL motor rehabilitative task with a breathing task. The aim is to investigate how the design developed for the HRV biofeedback affects engagement and performance during the exergame session. As a preliminary study, sixteen able-bodied subjects are tested in a within-subjects design, to assess the quality of the game features and design, before approaching MS patients. Two conditions are presented, with and without biofeedback. The proposed HRV biofeedback has two possible levels, depending on whether or not the desired respiratory rate of six breaths/min is successfully maintained. It is used to control game elements and change difficulty of the session. The main finding of this study is that biofeedback improves both user performance and experience in healthy subjects. These results underline the great potential of this technique to promote engagement. Thus, they point to fostering the rehabilitative effectiveness of repetitive motor tasks and encouraging adherence to the long- term training. Future studies will encompass fine tuning of the experimental setup and include PwMS to further adjust the game to patients' needs and observe the setup compliance to rehabilitation settings.

Literature highlights how virtual and augmented settings offer engaging solutions to improve one's feeling of an artificial limb embodiment. In this paper, we explored the potential of a setting for Spatial Augmented Reality (SAR, where a display augments a surface without making the user wear any visor) in two conditions of a lower limb ownership training involving subjects without disabilities. In the first condition, the subject must contract the quadriceps of a leg for commanding (through electromyography, EMG) a virtual leg (a 3D model of the Hybrid Knee prosthesis) to kick a virtual wall: each collision corresponds to a vibratory feedback on the thigh (a position defined for upcoming tests with transfemural amputees). The second condition adds a social context to engage the user: the subject is asked to cooperate with another (fictional) player to kick on the same virtual wall. Subjective (through questionnaires) and objective (according to the number of kicks as a performance index, and the proprioceptive drift as an embodiment index) assessments have been performed before a rubber leg illusion test. Overall, we observed how the cooperative task can engage the subject to be more active. However, this condition can reduce the impact of the training on the embodiment itself, probably because the social task generates a distraction. Nevertheless, such findings suggest the possibility to alternate these two tasks in the same session to increase the duration of a prosthetic embodiment training.

A pressing challenge in the design of actuated knee prostheses is the ability to address the high variation of speed and torque requirements for the different types and phases of locomotion. This manuscript presents a novel over-actuated knee prosthesis which makes use of a dual motor actuation architecture to address this issue. It utilizes a high speed/low torque motor to enable natural and highly dynamical motion, as required for swing phases of walking, which is permanently engaged. In addition to this motor, a clutchable uni-directional low dynamics high torque motor is present to assist during the execution of tasks which demand active torque. Preliminary experimental validations have been performed on a healthy subject provided with an able-bodied adapter to demonstrate natural walk patterns and power-assisted sit-to-stand activities.

Recent literature suggests that self-regulation techniques like biofeedback can be used to enhance the embodiment of artificial limbs. In this study, we developed and preliminarily tested an embodiment training protocol based on a Spatial Augmented Respiratory Cardiofeedback (SARC) implemented through a computer screen - visualizing a 3D model of a prosthetic hand (Hannes) - and a thoracic band for monitoring the Heart Rate Variability (HRV) of the users. The feedback was based on the respiratory-driven modulation of a composite index of the individuals’ cardiac autonomic state after an initial calibration based on slow breathing (at a rate perceived as “comfortable”). Alongside the assessment of the SARC use feasibility, this pilot study evaluates the virtual hand embodiment obtained in two task conditions. In both conditions, the virtual limb gradually appears when the cardiofeedback exercise is performed correctly. Otherwise, the virtual limb parts gradually disappear (“unstable” condition) or they remain visible (cumulative” condition). In the latter case, the virtual hand maintains its “reality-based” stability, supporting the subject’s motivation. Ten volunteers without disabilities were presented both conditions on 10 trials each (2min per trial). Their experience and their proprioceptive drift (estimating their real hand position as close to the artificial one) were assessed as measures of virtual prosthesis embodiment. The questionnaire results preliminarily highlight the feasibility of the SARC. Furthermore, a significantly stronger drift for the virtual prosthesis occurred in the cumulative condition, orienting further investigations.

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex multi-degrees of freedom (DoFs) movements. However, there is currently a trade-off between the intuitiveness of the control and the number of active DoFs. We here address this challenge by performing simultaneous multi-joint control of the Hannes system and testing several state-of-the-art classifiers to decode hand and wrist movements. The algorithms discriminated multi-DoF movements from forearm EMG signals of 10 healthy subjects reproducing hand opening-closing, wrist flexion-extension and wrist pronation-supination. We first explored the effect of the number of employed EMG electrodes on device performance through the classifiers optimization in terms of F1Score. We further improved classifiers by tuning their respective hyperparameters in terms of the Embedding Optimization Factor. Finally, three mono-lateral amputees tested the optimized algorithms to intuitively and simultaneously control the Hannes system. We found that the algorithms performances were similar to that of healthy subjects, particularly identifying the Non-Linear Regression classifier as the ideal candidate for prosthetic applications.

Since the uprising of new biomedical orthotic devices, exoskeletons have been put in the spotlight for their possible use in rehabilitation. Even if these products might share some commonalities among them in terms of overall structure, degrees of freedom and possible actions, they quite often differ in their approach on how to generate a feasible, stable and comfortable gait trajectory pattern. This paper introduces three proposed trajectories that were generated by using a basis function interpolation method and by working closely with two major rehabilitation centers in Italy. The whole procedure has been focused on the concepts of a configurable walk for patients that suffer from spinal cord injuries. We tested the solutions on a group of healthy volunteers and on a spinal-cord injury patient with the use of the new TWIN exoskeleton developed at the Rehab Technologies Lab at the Italian Institute of Technology.

This paper presents electro-hydrostatic actuation as a valid substitute of electro-mechanical devices for powered knee prostheses. The work covers the design of a test rig exploiting linear electro-hydrostatic actuation. Typical control laws for prosthesis actuators are discussed, implemented and validated experimentally. Particularly, this work focuses on position and admittance control syntheses enhanced with feed-forward friction compensation. Finally, the efficiency of the test rig is characterized experimentally and compared to that of classical electro-mechanical designs. It is demonstrated that the electro-hydrostatic prototype is able to fulfill its targets from a control perspective, while also having the potential to outperform electro-mechanical actuation in efficiency.