The integration of robots into human environments is advancing rapidly, driven by the demand for systems that combine robot accuracy and repeatability with human flexibility and adaptability. In this context, human-centered manipulation applications must address uncertainties arising from human, robotic, and environmental factors. As a result, effective robotic manipulation requires both accurate pre-grasping motions and precise grip strength control, especially in tasks where robotic devices are remotely controlled to grip objects with fine, desired, and adjustable grip force. The present work tackles the challenges posed by uncertainties and non-ideal conditions in surface electromyography (sEMG)-driven human-in-the-loop (HITL) robot hand control applications. In this regard, a novel probabilistic shared autonomy framework for fine grip strength regulation is introduced, leveraging Hidden Markov Models (HMMs) applied to tactile data to encode the HITL grasping action into proper, probabilistically consistent phases. These phases are then exploited to modulate the level of shared autonomy between the human operator and the robot hand, enabling precise control over grip strength. The presented shared autonomy framework was evaluated under multiple experimental conditions, testing grip force regulation performance with a group of 10+10 intact-limb participants and a participant with amputation in both static (fixed hand-object configuration) and dynamic (pick-and-place and recipe preparation) grasping tasks, with differentiated goals inspired by real-world requirements. Moreover, to explore generalizability, experiments were conducted with both anthropomorphic and industrial robotic hands, properly equipped with tactile sensors. Experimental outcomes are supported by statistical analysis and show, for the considered sample, the effectiveness of the proposed shared autonomy control architecture in achieving fine, smooth, and controllable grip strength regulation with respect to the baseline case in absence of our approach.

This study presents a sensing system composed of PVDF-based sensing arrays to recognize an object’s hardness and texture. The system is mounted on the fingertip of the Hannes prosthetic hand, which obtains the texture and hardness features by grasping daily-life objects. Time-domain features are extracted from the sensor’s response and evaluated through the Support Vector Machine (SVM) algorithm. Additionally, two deep learning (DL) algorithms including One-Dimensional Convolutional Neural Network (1-D CNN) and Long-Short Term Memory (LSTM) were implemented. Results demonstrated that the 1-D CNN attained the highest recognition accuracy 90% ≈ for both hardness and texture. Moreover, it demonstrated that the hardness information is integrated into the full grasp action, while the texture information can be extracted using the first contact with the object. Deploying the 1-D CNN into a microcontroller proved that the system is energy efficient and capable of extracting tactile information in real-time. This study demonstrated the effectiveness of the proposed system in recognizing object properties highlighting its robustness and suitability for developing prosthetics.

Although many advancements have been made on myoelectric pattern-recognition, the control of poly-articulated upper-limb prostheses remains insufficiently robust. Electrode-shift, sweat or fatigue degrade the performance of classifiers over time, resulting in unfruitful device usage and frequent re-calibration. To tackle this issue, here we introduce two models − µP6 and µP8 − that combine Geometric Algebra with nearest-neighbor classification. We aim at reducing both the necessary training data and training time and, unlike most current state-of-the-art algorithms, we exploit an alternative geometric representation (and visualization) of the EMG signal as different polygons for different types of gestures, facilitating the explanation of the decision-making process to a layman. Moreover, we explore four abstention strategies to reduce the number of misclassifications. We perform an offline analysis on two datasets, alongside two other standard models: nonlinear logistic regression (NLR) and linear discriminant analysis (LDA). Even with few training data, the proposed algorithms achieve high F1-scores (>0.95), significantly higher or non-significantly different from the values obtained with NLR and LDA, while maintaining relatively low abstentions rates and training times (<2 ms). The proposed algorithms allow to reduce the amount of training data and training times without compromising recognition rates. The proposed algorithms may contribute for a faster prosthesis re-calibration procedure while allowing to re-gain high recognition rates. Furthermore, the decision-making process is explainable and interpretable, potentially improving user trust and acceptance.

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.

Robotic lower limb prostheses have the power to revolutionize mobility by enhancing gait efficiency and facilitating movement. While several design approaches have been explored to create lightweight and energy-efficient devices, the potential of underactuation remains largely untapped in lower limb prosthetics. Taking inspiration from the natural harmony of walking, in this article, we have developed an innovative active transfemoral prosthesis. By incorporating underactuation, our design uses a single power actuator placed near the knee joint and connected to a differential mechanism to drive both the knee and ankle joints. We conduct comprehensive benchtop tests and evaluate the prosthesis with three individuals who have above-knee amputations, assessing its performance in walking, stair climbing, and transitions between sitting and standing. Our evaluation focuses on gathering position and torque data recorded from sensors integrated into the prosthesis and comparing these measurements to biomechanical data of able-bodied locomotion. Our findings highlight the promise of underactuation in advancing lower limb prosthetics and demonstrate the feasibility of our knee–ankle underactuated design in various tasks, showcasing its ability to replicate natural movement.

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.

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.

The growth of implantable medical devices demands to establish safety and reliable wireless communication systems. Radio-frequency Identification (RFID) is a key architecture as it avoids the use of battery in the implanted transponder. This paper aims to compare the performance of loop and slotted-patch antennas within a battery-less through-the-body telemetry system based on backscattering modulation in the UHF band. A complete analysis of the possible combinations between the two antennas is discussed accounting for the efficiency of power transfer, the safety regulations (Specific Absorption Rate - SAR) and the quality of the link (Bit Error Rate). The SAR constraint revealed to be the bottleneck of the communication, but it can be released by increasing the reader-skin distance. Loop-loop resulted the best antenna combination, in case of an optimal reader-skin distance of 2 mm. Experimental tests with a semisolid multi-layered human phantom corroborated the numerical results.