Showing papers by "Christian Cipriani published in 2018"

Ultraconformable Temporary Tattoo Electrodes for Electrophysiology.

Abstract: Electrically interfacing the skin for monitoring personal health condition is the basis of skin-contact electrophysiology. In the clinical practice the use of stiff and bulky pregelled or dry electrodes, in contrast to the soft body tissues, imposes severe restrictions to user comfort and mobility while limiting clinical applications. Here, in this work dry, unperceivable temporary tattoo electrodes are presented. Customized single or multielectrode arrays are readily fabricated by inkjet printing of conducting polymer onto commercial decal transfer paper, which allows for easy transfer on the user's skin. Conformal adhesion to the skin is provided thanks to their ultralow thickness (<1 µm). Tattoo electrode-skin contact impedance is characterized on short- (1 h) and long-term (48 h) and compared with standard pregelled and dry electrodes. The viability in electrophysiology is validated by surface electromyography and electrocardiography recordings on various locations on limbs and face. A novel concept of tattoo as perforable skin-contact electrode, through which hairs can grow, is demonstrated, thus permitting to envision very long-term recordings on areas with high hair density. The proposed materials and patterning strategy make this technology amenable for large-scale production of low-cost sensing devices.

Comparison of linear frequency and amplitude modulation for intraneural sensory feedback in bidirectional hand prostheses.

Abstract: Recent studies have shown that direct nerve stimulation can be used to provide sensory feedback to hand amputees. The intensity of the elicited sensations can be modulated using the amplitude or frequency of the injected stimuli. However, a comprehensive comparison of the effects of these two encoding strategies on the amputees’ ability to control a prosthesis has not been performed. In this paper, we assessed the performance of two trans-radial amputees controlling a myoelectric hand prosthesis while receiving grip force sensory feedback encoded using either linear modulation of amplitude (LAM) or linear modulation of frequency (LFM) of direct nerve stimulation (namely, bidirectional prostheses). Both subjects achieved similar and significantly above-chance performance when they were asked to exploit LAM or LFM in different tasks. The feedbacks allowed them to discriminate, during manipulation through the robotic hand, objects of different compliances and shapes or different placements on the prosthesis. Similar high performances were obtained when they were asked to apply different levels of force in a random order on a dynamometer using LAM or LFM. In contrast, only the LAM strategy allowed the subjects to continuously modulate the grip pressure on the dynamometer. Furthermore, when long-lasting trains of stimulation were delivered, LFM strategy generated a very fast adaptation phenomenon in the subjects, which caused them to stop perceiving the restored sensations. Both encoding approaches were perceived as very different from the touch feelings of the healthy limb (natural). These results suggest that the choice of specific sensory feedback encodings can have an effect on user performance while grasping. In addition, our results invite the development of new approaches to provide more natural sensory feelings to the users, which could be addressed by a more biomimetic strategy in the future.

Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses

Abstract: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.

Discrete Vibro-Tactile Feedback Prevents Object Slippage in Hand Prostheses More Intuitively Than Other Modalities

Abstract: In the case of a hand amputation, the affected can use myoelectric prostheses to substitute the missing limb and regain motor functionality. Unfortunately, these prostheses do not restore sensory feedback, thus users are forced to rely on vision to avoid object slippage. This is cognitively taxing, as it requires continuous attention to the task. Thus, providing functionally effective sensory feedback is pivotal to reduce the occurrence of slip events and reduce the users’ cognitive burden. However, only a few studies investigated which kind of feedback is the most effective for this purpose, mostly using unrealistic experimental scenarios. Here we attempt a more realistic simulation of involuntary hand opening and subsequent recovery of a stable grasp of the slipping object using a robotic hand operated by the subjects through a standard myoelectric control interface. We compared three stimulation modalities (vision, continuous grip force feedback, and discrete slip feedback) and found that the discrete feedback allowed subjects to have higher success rates (close to 100%) in terms of objects recovered from slippage, basically requiring no learning. These results suggest that this simple yet effective feedback can be used to reduce grasp failures in prosthetic users, increasing their confidence in the device.

Improving internal model strength and performance of prosthetic hands using augmented feedback.

Abstract: The loss of an arm presents a substantial challenge for upper limb amputees when performing activities of daily living. Myoelectric prosthetic devices partially replace lost hand functions; however, lack of sensory feedback and strong understanding of the myoelectric control system prevent prosthesis users from interacting with their environment effectively. Although most research in augmented sensory feedback has focused on real-time regulation, sensory feedback is also essential for enabling the development and correction of internal models, which in turn are used for planning movements and reacting to control variability faster than otherwise possible in the presence of sensory delays. Our recent work has demonstrated that audio-augmented feedback can improve both performance and internal model strength for an abstract target acquisition task. Here we use this concept in controlling a robotic hand, which has inherent dynamics and variability, and apply it to a more functional grasp-and-lift task. We assessed internal model strength using psychophysical tests and used an instrumented Virtual Egg to assess performance. Results obtained from 14 able-bodied subjects show that a classifier-based controller augmented with audio feedback enabled stronger internal model (p = 0.018) and better performance (p = 0.028) than a controller without this feedback. We extended our previous work and accomplished the first steps on a path towards bridging the gap between research and clinical usability of a hand prosthesis. The main goal was to assess whether the ability to decouple internal model strength and motion variability using the continuous audio-augmented feedback extended to real-world use, where the inherent mechanical variability and dynamics in the mechanisms may contribute to a more complicated interplay between internal model formation and motion variability. We concluded that benefits of using audio-augmented feedback for improving internal model strength of myoelectric controllers extend beyond a virtual target acquisition task to include control of a prosthetic hand.

Humans adjust their grip force when passing an object according to the observed speed of the partner's reaching out movement.

Abstract: The way an object is released by the passer to a partner is fundamental for the success of the handover and for the experienced fluency and quality of the interaction. Nonetheless, although its apparent simplicity, object handover involves a complex combination of predictive and reactive control mechanisms that were not fully investigated so far. Here, we show that passers use visual-feedback based anticipatory control to trigger the beginning of the release, to launch the appropriate motor program, and adapt such predictions to different speeds of the receiver's reaching out movements. In particular, the passer starts releasing the object in synchrony with the collision with the receiver, regardless of the receiver's speed, but the passer's speed of grip force release is correlated with receiver speed. When visual feedback is removed, the beginning of the passer's release is delayed proportionally with the receiver's reaching out speed; however, the correlation between the passer's peak rate of change of grip force is maintained. In a second study with 11 participants receiving an object from a robotic hand programmed to release following stereotypical biomimetic profiles, we found that handovers are experienced as more fluent when they exhibit more reactive release behaviours, shorter release durations, and shorter handover durations. The outcomes from the two studies contribute understanding of the roles of sensory input in the strategy that empower humans to perform smooth and safe handovers, and they suggest methods for programming controllers that would enable artificial hands to hand over objects with humans in an easy, natural and efficient way.

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Abstract: We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.

The S-Finger : A Synergetic Externally Powered Digit With Tactile Sensing and Feedback

Abstract: Partial hand amputation is by far the most common type of amputation worldwide. Nevertheless, regardless of their potential clinical and socioeconomic impact, battery-powered partial hand prostheses, namely, powered digits, have modestly progressed so far, and very few clinical solutions are available today. Here, we present a mechanical architecture, an alternative to state-of-the-art solutions, which exploits a high efficiency, non-back drivable mechanical transmission based on a face-gear pair and a miniaturized clutch. We took inspiration from the synergetic prehension approach proposed by Childress for whole hand amputation. The finger was equipped with a myoelectric controller and a tactile sensor able to provide users with discrete event sensory feedback. Measured speed (90°/s) and force (6.5 N) of the newly dubbed S-Finger proved comparable with those of clinically available prostheses. The design demonstrated to be compact and rugged enough to undergo a clinical viability test with two partial hand amputees, fitted with custom three-fingered research prostheses using the S-Finger . The subjects successfully completed several dexterity tests and gave relevant feedback for the development of a second-generation device. These results contribute to the increasing research endeavors in the field of partial hand amputation.

Electro-cutaneous stimulation on the palm elicits referred sensations on intact but not on amputated digits.

Abstract: Objective Grasping and manipulation control critically depends on tactile feedback. Without this feedback, the ability for fine control of a prosthesis is limited in upper limb amputees. Early studies have shown that non-invasive electro-cutaneous stimulation (ES) can induce referred sensations that are spread to a wider and/or more distant area, with respect to the electrodes. Building on this, we sought to exploit this effect to provide somatotopically matched sensory feedback to people with partial hand (digital) amputations. Approach For the first time, this work investigated the possibility of inducing referred sensations in the digits by activating the palmar nerves. Specifically, we electrically stimulated 18 sites on the palm of non-amputees to evaluate the effects of sites and stimulation parameters on modality, magnitude, and location of the evoked sensations. We performed similar tests with partial hand amputees by testing those sites that had most consistently elicited referred sensations in non-amputees. Main Results We demonstrated referred sensations in non-amputees from all stimulation sites in one form or another. Specifically, the stimulation of 16 of the 18 sites gave rise to reliable referred sensations. Amputees experienced referred sensations to unimpaired digits, just like non-amputees, but we were unable to evoke referred sensations in their missing digits: none of them reported sensations that extended beyond the tip of the stump. Significance The possibility of eliciting referred sensations on the digits may be exploited in haptic systems for providing touch sensations without obstructing the fingertips or their movements. The study also suggests that the phenomenon of referred sensations through ES may not be exploited for partial hand prostheses, and it invites researchers to explore alternative approaches. Finally, the results seem to confirm previous studies suggesting that the stumps in partial hand amputees partially acquire the role of the missing fingertips, physiologically and cognitively.

A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback

Abstract: Current myoelectric prostheses allow upper-limb amputees to regain voluntary motor control of their artificial limb by exploiting residual muscle function in the forearm 1 . However, the over-reliance on visual cues resulting from a lack of sensory feedback is a common complaint 2,3 . Recently, several groups have provided tactile feedback in upper-limb amputees by using implanted electrodes 4,5,6,7,8 , surface nerve stimulation 9,10 or sensory substitution 11,12 . These approaches have led to improved function and prosthesis embodiment 4,5,6,7,13,14 . Nevertheless, the provided information remains limited to a subset of the rich sensory cues available to healthy individuals. More specifically, proprioception, the sense of limb position and movement, is predominantly absent from current systems. Here we show that sensory substitution based on intraneural stimulation can deliver position feedback in real-time and in conjunction with somatotopic tactile feedback. This approach allowed two trans-radial amputees to regain high and close-to-natural remapped proprioceptive acuity, with a median joint angle reproduction accuracy of 9.1° and a median threshold to detection of passive movements of 9.5°, which was compatible with results obtained in healthy subjects 15,16,17 . The simultaneous delivery of position information and somatotopic tactile feedback allowed both amputees to discriminate object size and compliance with high levels of accuracy (75.5%). These results demonstrate that touch information delivered via somatotopic neural stimulation and position information delivered via sensory substitution can be exploited simultaneously and efficiently by trans-radial amputees. This study paves the way towards more sophisticated bidirectional bionic limbs conveying rich, multimodal sensations.

Compliant Prosthetic Wrists Entail More Natural Use Than Stiff Wrists During Reaching, Not (Necessarily) During Manipulation

Abstract: Developing an artificial arm with functions equivalent to those of the human arm is one of the challenging goals of bioengineering. State-of-the-artprostheses lack several degrees of freedom and force the individuals to compensate for them by means of compensatory movements, which often result in residual limb pain and overuse syndromes. Passive wristsmay reduce such compensatory actions, nonethelessto date their actual efficacy, associated to conventional myoelectric hands is a matter of debate. We hypothesized that a transradial prosthesiswould allow a simpler operation if its wrist behaved compliant during the reaching and grasping phase, and stiff during the holding andmanipulation phase. To assess this, we compared a stiff and a compliant wrist and evaluating the extent of compensatory movements in the trunk and shoulder, with unimpaired subjects wearing orthoses, while performing nine activities of daily living taken from the southampton hand assessment procedure. Our findings show indeed that the optimal compliance for a prosthetic wrist is specific to the phase of the motor task: the compliant wrist outperforms the stiff wrist during the reaching phase, whereas the stiff wrist exhibits more natural movements during the manipulation phase of heavy objects. Hence, this paper invites rehabilitation engineers to develop wrists with switchable compliance.

Principal Orientations of the Wrist During ADLs: Towards the Design of a Synergetic Wrist Prosthesis

Abstract: To design an underactuated wrist prosthesis, a preliminary study has been conducted to identify the relationship between the Degrees of Freedom (DoFs) of the wrist during the execution of tasks of daily living. After the identification of the principal orientations of the wrist describing the tasks, polynomial functions were used to define a synergetic relationship between the DoFs. The latter can be implemented in a prosthetic wrist featuring one actuator to obtain motion along three DoFs, with the purpose of reducing compensatory movements.

Corrections to “Treatment of the Partial Hand Amputation: An Engineering Perspective”

Abstract: In the above paper, by Imbinto et al ( IEEE Rev Biomed Eng , vol 9, pp 32–48, 2016), typographical errors appear in (1) (2) (3), whereas (4) is missing of a factor; all the equations are reported within Section III-C of the paper The modifications are provided in this paper

Digital Extensions with Bi-axial Fingertip Sensors for Supplementary Tactile Feedback Studies

Abstract: Using a hand prosthesis means grasping without tactile information. Although supplementary sensory feedback has been investigated extensively, few study results could translate into clinical applications. Unreliable and imprecise feedforward control of current hand prostheses hinders the investigation of supplementary sensory feedback, so an ideal feedforward tool should be used. Thus, we aimed to create a device that would allow to use the sensory deprived human hand as an ideal tool without the need for local anesthesia. For this, we fashioned silicone digit extensions with integrated force sensors and tested the performance of 12 volunteers in grasping with these extensions. Two tests were performed: a simple pick and lift test to compare performance to anesthetized digits, and a virtual egg test to assess grasping efficiency. We found that the extensions significantly alter grasping. In future studies, these extensions will help us investigate how to artificially restore the information necessary for successful and efficient grasping with an ideal feedforward tool.

Progress Towards the Development of the DeTOP Hand Prosthesis: A Sensorized Transradial Prosthesis for Clinical Use

Abstract: This paper presents the progress towards the development of a sensorized transradial prosthesis designed and developed to be a reliable and easy to maintain prosthesis, aimed at sustaining long-term home studies. The hand builds around a transmission mechanism that implements a semi-independent actuation of the abduction/adduction of the thumb and of the flexion/extension of the index, by means of a single actuator. The new version of the hand includes three bi-axial force sensors in the thumb, index and middle fingers, respectively, allowing to measure the grasping and load forces at the fingertips. The mechanical transmission was revised increasing the grip force to 70 N (maximum single fingertip force) and still guaranteeing fast grasps (closing time ~500 ms). The current version of the prosthesis underwent durability tests and a preclinical evaluation involving one trans-radial amputee.

Online Prediction of Robot to Human Handover Events Using Vibrations

Abstract: One of the main issues for a robotic passer is to detect the onset of a handover, in order to avoid the object from being released when the human partner is not ready or if some impact occurs. This paper presents the methodology for a robotic passer, that is potentially able to estimate the interaction forces by the receiver on the object, thus to achieve fluent and safe handovers. The proposed system uses a vibrator that energizes the object and an accelerometer that monitors vibration propagation through the object during the handover. We focused on the machine-learning technique to classify between four states during object handover. A neural network was trained for these four states and tested online. In experimental trials an accuracy of 85.2 % and 93.9% were obtained respectively for four classes and two classes of actions by a neural network classifier.

Grasp Force Estimation from HD-EMG Recordings with Channel Selection Using Elastic Nets: Preliminary Study

Abstract: Ahstract- The force applied with a prosthetic device is fundamental for the correct handling of objects in daily tasks. However, it is also a factor that normally gets relegated to a secondary plane, as researchers mainly focus on decoding the users intent in terms of movements to be performed. Continuous estimates of the grasp force from the electromyographic (EMG) signals were proposed in the past. As motor actions are preplanned in humans, we hypothesized that it would be possible to decode the intended grasp force from the transient state of the EMG signal. We tested this hypothesis by using features extracted from surface HD-EMG recordings from forearm muscles, classified using artificial neural networks. Data from 6 able-bodied subjects were collected. They were trained and tested at segments of 120 ms with 20 ms overlap, starting 1 s before and ending 0.5 s after the detection of the onset with different subsets of channels. The results obtained showed that the transient phase contains information about the target grasp force, achieving predictions of 2.62 % MVC average absolute errors within 430 ms from the onset of the EMG.

Improvements on the Design of the S - Finger Prosthetic Digit

Abstract: Partial hand amputations are the most common amputations worldwide, yet their prostheses, especially battery-powered ones, are only slowly progressing. As a result, only few clinical solutions are available. We developed a prototype of a powered prosthetic finger (dubbed S-Finger) that is equipped with a mechanical transmission alternative to the already available solutions which comprises of a miniaturized non-back drivable mechanism. Here we present the design criteria and the details of an optimized design that comprises of a non-backdrivable mechanism and a miniaturized Oldham joint.

Method for Optimal Digit Alignment for the Fitting of Partial Hand Powered Prostheses: A Preliminary Study

Abstract: Current fitting procedures of powered partial hand prostheses heavily rely on manual “trial & error” approaches by skilled prosthetists without taking advantage of more modern engineering enabling technologies. Here we preliminary present an optimization method for the placement of the powered digits based on a biomechanical simplified model. The mathematical model is built taking into account the geometry of the residual limb, the functional conditions of the stump and the kinematics of the prosthesis. The optimization refers to the distance, on the palm plane, between the basements of two opposing digits. In particular it focused on the opposition capability of a prosthetic index digit with an unpaired thumb in a pinch grasp. The optimal posture is the one that allows the maximum control accuracy of the force along the direction of the grasp. We argue that the proposed method could be used to predict the quality of the fitting and to standardize the process.