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The motor neuroscience literature suggests that the central nervous system may encode some motor commands in terms of velocity. In this work, we tackle the question: what consequences would velocity commands produce at the behavioral level? Considering the ubiquitous presence of noise in the neuromusculoskeletal system, we predict that velocity commands affected by stationary noise would produce "random walks", also known as Brownian processes, in position. Brownian motions are distinctively characterized by a linearly growing variance and a power spectral density that declines in inverse proportion to frequency. This work first shows that these Brownian processes are indeed observed in unbounded motion tasks e.g., rotating a crank. We further predict that such growing variance would still be present, but bounded, in tasks requiring a constant posture e.g., maintaining a static hand position or quietly standing. This hypothesis was also confirmed by experimental observations. A series of descriptive models are investigated to justify the observed behavior. Interestingly, one of the models capable of accounting for all the experimental results must feature forward-path velocity commands corrupted by stationary noise. The results of this work provide behavioral support for the hypothesis that humans plan the motion components of their actions in terms of velocity., (© 2024. The Author(s).)

The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.

When it comes to robotic-mediated rehabilitation it is mandatory to design a system that guarantees a safe and compliant human-machine interaction. Dealing with rehabilitative upper limb exoskeletons, Series Elastic Actuators offer a potential solution for this purpose. This work proposes four different solutions for SEAs' spring design. After an analysis on the mechanical requirements, four different solutions are explored and presented. The performances of the proposed highly integrated SEAs are compared. An initial static characterization provided insights on the linearity and repeatability of each spring torque-angle performances. The dynamics of the springs and their frequency responses are then analysed to show how it is possible to exploit our system for human-robot interaction applications.

Robotic rehabilitation has demonstrated slight positive effects compared to traditional care, but there is still a lack of targeted high-level control strategies in the current state-of-the-art for minimizing pathological motor behaviors. In this study, we analyzed upper-limb motion capture data from healthy subjects performing a pick-and-place task to identify task-specific variability in postural patterns. The results revealed consistent behaviors among subjects, presenting an opportunity to develop a novel extraction method for variable volume references based solely on observations from healthy individuals. These human-centered references were tested on a simulated 4 degrees-of-freedom upper-limb exoskeleton, showing its compliant adaptation to the path considering the variance in healthy subjects' motor behavior.

Recent human-centred design studies suggest that acoustic noise could affect the physical use and psychological acceptance of a biomedical device. These aspects are especially relevant in the prosthetic field, in which device loudness is often related to rejection. The aim of the study is to inquire on the possibility to reduce the acoustic noise emitted by a robotic leg prosthesis by improving its casing. First, acoustic noise emissions are characterized experimentally using an anechoic chamber, both for the whole prosthesis, and for its actuator (i.e., noise source) in isolation. The characterizations show that the whole prosthesis including its casing amplify the actuator noise, and that noise emissions are concentrated within a certain frequency range. Based on these findings, the prosthesis casing has been redesigned to include a panel of Helmholtz resonator-based acoustic metamaterials as proof of concept, which attenuate respective noise emissions. Experimental validations show that the use of such metamaterials in the prosthesis casing can significantly reduce noise emissions without compromising on prosthesis size and weight.

Introduction: In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction., Methods: Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees., Results: The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bruni, Marinelli, Bucchieri, Boccardo, Caserta, Di Domenico, Barresi, Florio, Canepa, Tessari, Laffranchi and De Michieli.)

The solution of the inverse kinematics problem in multi-degrees of freedom robots has been tackled, through the last three decades, by several different approaches including analytical, geometrical, differential and numerical methods. All these techniques present their own advantages and drawbacks. However, a guideline on which approach is better to follow, depending on the kind of task to perform and the type of robotic device used, is still missing. In this work, a quantitative comparative analysis of three different inverse kinematics methodologies for the control of rehabilitative robotic devices is proposed, with aim of devising best practices and guidelines for the selection of the most suitable approach. The analyzed methodologies are implemented and numerically tested on two actual devices, specifically an upper-limb exoskeleton and an upper-limb prosthetic arm.

Energy storing and return (ESAR) prosthetic feet showed continuous improvements during the last 30 years. Despite this, standard guidelines are still missing to achieve an optimal foot design in terms of performances. One of the most important design parameters in ESAR feet is the Rollover Shape (RoS). This represents the foot Center of Pressure (CoP) path in a shank-based coordinate system during stance. RoS objectively describes the foot behavior according to its stiffness, which depends on foot geometry and material. This work presents the development of a finite element modeling methodology able to predict the stiffness characteristic of an ESAR foot and its RoS. The validation of the model is performed on a well-known commercially available prosthetic foot both in bench tests and realistic walking scenario. The obtained results confirm an error of +6.1% on stiffness estimation and +10.2% on RoS evaluation, which underlines that the proposed method is a powerful tool able to replicate the mechanical behavior of a prosthetic foot.