Claudio Castellini

Friedrich-Alexander-University of Erlangen-Nürnberg | FAU · Artificial Intelligence in Biomedical Engineering

Objective: in recent years, Functional Electrical Stimulation has found many applications both within and outside the medical field. However, most available wearable FES devices are not easily adaptable to different users, and most setups rely on task-specific control schemes. Approach: in this article, we present a peripheral stimulation protot...

Background Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the use...

Background: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the us...

In recent years, neuromuscular electrical stim-ulation (NMES) has found many applications both within the medical field and outside. While this technology has been widely recognized as a valid tool for rehabilitative and assistive applications, most solutions presented in the literature seem to focus on highly specific cases and facili-tate very se...

Neuromuscular functional electrical stimulation represents a valid technique for functional rehabilitation or, in the form of a neuroprosthesis, for the assistance of neurological patients. However, the selected stimulation of single muscles through surface electrodes remains challenging particularly for the upper extremity. In this paper, we prese...

Repetitive or tiring tasks and movements during manual work can lead to serious musculoskeletal disorders and, consequently, to monetary damage for both the worker and the employer. Among the most common of these tasks is overhead working while operating a heavy tool, such as drilling, painting, and decorating. In such scenarios, it is desirable to...

The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, we present a non-invasive neural interface technology that will change the lives of individuals living with cervical SCI (C4-C6). We demonstrate that eight motor- and sensory-complete SCI individuals (...

Electromyography is the gold-standard among sensors for prosthetic control. However, stable and reliable myocontrol remains an unsolved problem in the community. Amid improvements currently under investigation, one focuses on alternative or complementary sensors. In this study, we compare different techniques, recording surface and deep muscle acti...

Achieving robust, intuitive, simultaneous and proportional control over multiple degrees of freedom (DOFs) is an outstanding challenge in the development of myoelectric prosthetic systems. Since the priority in myoelectric prosthesis solutions is robustness and stability, their number of functions is usually limited. Objective: Here, we introduce...

Applications of simultaneous and proportional control for upper-limb prostheses typically rely on supervised machine learning to map muscle activations to prosthesis movements. This scheme often poses problems for individuals with limb differences, as they may not be able to reliably reproduce the training activations required to construct a natura...

Neuromuscular functional electrical stimulation represents a valid technique for functional rehabilitation or, in the form of a neuroprosthesis, for the assistance of neurological patients. However, the selected stimulation of single muscles through surface electrodes remains challenging particularly for the upper extremity. In this paper, we prese...

Editorial on the Research Topic Current Trends in Deep Learning for Movement Analysis and Prostheses Control

Traditionally, robotics and psychology have little to share; at least, if we think of robotics as an endeavor to build machines able to autonomously perform tasks that are undesirable or impossible for human beings. Nevertheless, besides addressing safety requirements for close physical interaction, which are tackled by approaches like soft robotic...

In the field of rehabilitation robotics, transparent, precise and intuitive control of hand exoskeletons still represents a substantial challenge. In particular, the use of compliant systems often leads to a trade-off between lightness and material flexibility, and control precision. In this paper, we present a compliant, actuated glove with a cont...

While simultaneous and proportional activation of multiple degrees of freedom (DOFs) is supported by novel prosthetic hands, there are still no commercial controllers to appropriately enable it. Here, we test a ridge regression based myocontrol method in two real-time scenarios: 13 subjects with an extended high-density EMG electrode set (192 chann...

Muscle synergies have been widely used as a compact description of the neuromuscular motor control strategies. The online detection of synergistic activations might therefore improve the feasibility of sEMG-based control algorithms. In this study, a simple online detector of time-varying muscle synergies activation timings is proposed, and its perf...

With the advent of highly dexterous robotic arms, assistive platforms for home healthcare are gaining increasing attention from the research community. Control of the many degrees of freedom of such platforms, however, must be ensured uniformly, both for non-disabled and disabled users, in order to give them as much autonomy as possible. Nine users...

Modern myocontrol of prosthetic upper limbs employs pattern recognition models to map the muscular activity of the residual limb onto control commands for the prosthesis. The quality of pattern-recognition-based myocontrol, and that of the resulting user experience, depend on the quality of the data used to build the model. Surprisingly, the prosth...

Spinal Muscular Atrophy (SMA) is a neuromuscular disease characterized by the degeneration of the \(\alpha \)-motor neurons in the spinal cord, resulting in progressive proximal muscle weakness and paralysis. It is the second most common fatal autosomal recessive disorder after cystic fibrosis in the world. In the context of assistive robotics for...

Objective. Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dexterity, makes them ideal assistive devices for upper-limb disabled persons, too. Indeed, t...

The day seems not too far away, in which robots will be an active part of our daily life, just like electric appliances already are. Hence, there is an increasing need for paradigms, tools, and techniques to design proper human-robot interaction in a human-centered fashion (Beckerle et al., 2017). To this end, appropriate Human-Machine Interfaces (...

Despite decades of research, muscle-based control of assistive devices (myocontrol) is still unreliable; for instance upper-limb prostheses, each year more and more dexterous and human-like, still provide hardly enough functionality to justify their cost and the effort required to use them. In order to try and close this gap, we propose to shift th...

Objective: Pattern-recognition-based myocontrol can be unreliable, which may limit its use in the clinical practice and everyday activities. One cause for this is the poor generalization of the underlying machine learning models to untrained conditions. Acquiring the training data and building the model more interactively can reduce this problem....

Novel multi-modal and closed-loop myoelectric control strategies may yield more robust, capable prostheses which improve quality of life for those affected by upper-limb loss. However, the translation of such systems from an experimental setting towards daily use by persons with limb loss is limited by the cost and complexity of assessing all the p...

Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A consequence of this approach is that the reliability of a myo...

Myocontrol, that is, control of a prosthesis via muscle signals, is still a surprisingly hard problem. Recent research indicates that surface electromyography (sEMG), the traditional technique used to detect a subject's intent, could proficiently be replaced, or conjoined with, other techniques (multi-modal myocontrol), with the aim to improve both...

Objective: Despite numerous recent advances in the field of rehabilitation robotics, simultaneous, and proportional control of hand and/or wrist prostheses is still unsolved. In this work we concentrate on myocontrol of combined actions, for instance power grasping while rotating the wrist, by only using training data gathered from single actions....

In rehabilitation, assistive and space robotics, the capability to track the body posture of a user in real time is highly desirable. In more specific cases, such as teleoperated extra-vehicular activity, prosthetics and home service robotics, the ideal posture-tracking device must also be wearable, light and low-power, while still enforcing the be...

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, a direct and real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emoti...

Myocontrol is control of a prosthetic device using data obtained from (residual) muscle activity. In most myocontrol prosthetic systems, such biological data also denote the subject's intent: reliably interpreting what the user wants to do, exactly and only when she wants, is paramount to avoid instability, which can potentially lead to accidents,...

Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat. Hence, such controllers need to be constantly updated and heavily rely on user feedback. In this paper, we present an automatic failure detection method which learns wh...

Natural myocontrol employs pattern recognition to allow users to control a robotic limb intuitively using their own voluntary muscular activations. The reliability of myocontrol strongly depends on the signals initially collected from the users, which must appropriately capture the variability encountered later on during operation. Since myoelectri...

Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat. Hence, such controllers need to be constantly updated and heavily rely on user feedback. In this paper, we present an automatic failure detection method which learns wh...

Despite the increasing popularity of Machine Learning methods, their usage in safety-critical applications is sometimes limited by the impossibility of providing formal guarantees on their behaviour. In this work we focus on one such application, where Kernel Ridge Regression with Random Fourier Features is used to learn controllers for a prosthet...

Objective: Currently, there are some 95 000 people in Europe suffering from upper-limb impairment. Rehabilitation should be undertaken right after the impairment occurs and should be regularly performed thereafter. Moreover, the rehabilitation process should be tailored specifically to both patient and impairment. Approach: To address this, we h...

Despite the increasing popularity of Machine Learning methods, their usage in safety-critical applications is sometimes limited by the impossibility of providing formal guarantees on their behaviour. In this work we focus on one such application, where Kernel Ridge Regression with Random Fourier Features is used to learn controllers for a prostheti...

In the context of assistive robotics, myocontrol is one of the so-far unsolved problems of upper-limb prosthetics. It consists of swiftly, naturally and reliably converting biosignals, non-invasively gathered from an upper-limb disabled subject, into control commands for an appropriate self-powered prosthetic device. Despite decades of research, tr...

The cover image is based on the Focus Article Robotic interfaces for cognitive psychology and embodiment research: a research roadmap, by Philipp Beckerle, Claudio Castellini, and Bigna Lenggenhager, https://doi.org/10.1002/wcs.1486. The cover image is based on the Focus Article Robotic interfaces for cognitive psychology and embodiment research: a...

The standard paradigm in Affective Computing involves acquiring one/several markers (e.g., physiological signals) of emotions and training models on these to predict emotions. However, due to the internal nature of emotions, labelling/annotation of emotional experience is done manually by humans using specially developed annotation tools. To effect...

The contemporary myoelectric prostheses are advanced mechatronic systems, but human-machine interfacing for robust control of these devices is still an open challenge. We present a novel method for the recognition of user intention based on pattern classification which is inspired by the natural coordination of multiple muscles during hand and wris...

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (C...

Advanced human–machine interfaces render robotic devices applicable to study and enhance human cognition. This turns robots into formidable neuroscientific tools to study processes such as the adaptation between a human operator and the operated robotic device and how this adaptation modulates human embodiment and embodied cognition. We analyze bid...

Dexterous upper-limb myoelectric prostheses can, to some extent, restore the motor functions lost after an amputation. However, ensuring the reliability of myoelectric control is still an open challenge. In this study, we propose a classification method that exploits the regularity in muscle activation patterns (uniform scaling) across different fo...

A better understanding of human body experience and the embodiment of arti- ficial limbs could path the way towards a novel generation of robotic hands. Such hands could serve as assistive devices, which closely matching users’ expectations and needs. Knowledge about the users sensory-motor skills can additionally guide techni- cal developments, e....

Human-machine interfaces to control prosthetic devices still suffer from scarce dexterity and low reliability; for this reason, the community of assistive robotics is exploring novel solutions to the problem of myocontrol. In this work, we present experimental results pointing in the direction that one such method, namely Tactile Myography (TMG), c...

C ontext • In this article we match machine learning (ML) and interactive machine learning (iML) with radical constructivism (RC) to build a tentative radical constructivist framework for iML; we then present a pilot study in which RC-framed iML is applied to assistive robotics, namely upper-limb prosthetics (myocontrol). P roblem • Despite more th...

Human-machine interfaces to control prosthetic devices still suffer from scarce dexterity and low reliability; for this reason, the community of assistive robotics is exploring novel solutions to the problem of myocontrol. In this work, we present experimental results pointing in the direction that one such method, namely Tactile Myography (TMG), c...

Emotion labels are usually obtained via either manual annotation, which is tedious and time-consuming, or questionnaires, which neglect the time-varying nature of emotions and depend on human's unreliable introspection. To overcome these limitations, we developed a continuous, real-time, joystick-based emotion annotation framework. To assess the sa...

Despite recent advances in prosthetics and assistive robotics in general, robust simultaneous and proportional control of dexterous prosthetic devices remains an unsolved problem, mainly because of inadequate sensorization. In this paper, we study the application of regression to muscle activity, detected using a flexible tactile sensor recording m...

Objective: Dexterous upper-limb prostheses are available today to restore grasping, but an effective and reliable feed-forward control is still missing. The aim of this work was to improve the robustness and reliability of myoelectric control by using context information from sensors embedded within the prosthesis. Approach: We developed a conte...

Myocontrol, that is control of prostheses using bodily signals, has proved in the decades to be a surprisingly hard problem for the scientific community of assistive and rehabilitation robotics. In particular, traditional surface electromyography (sEMG) seems to be no longer enough to guarantee dexterity (i.e., control over several degrees of freed...

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper...

In myoelectric prosthesis control, one of the hottest topics nowadays is enforcing simultaneous and proportional (s/p) control over several degrees of freedom. This problem is particularly hard and the scientific community has so far failed to provide a stable and reliable s/p control, effective in daily-life activities. In order to improve the rel...

Simultaneous and proportional control of a prosthetic hand and wrist is still a challenging issue, although giant steps have lately been made in this direction. In this paper , we study the application of a novel machine learning method to the problem, with the aim to potentially improve such control. Namely we apply diffe