Post-traumatic stress disorder in patients with systemic lupus erythematosus heavily affects quality of life. A cross-sectional web survey-based study.

OBJECTIVES: Neuropsychiatric symptoms develop in up to 20% of the patients with Systemic Lupus Erythematosus (SLE). Growing evidence is accruing on the association of SLE with Post-traumatic Stress Disorder (PTSD), but little is known about its contribution on patient-reported outcomes. This study focuses on PTSD prevalence in our SLE cohort and on its impact on quality of life. METHODS: Trauma and Loss Spectrum - Self Reported (TALS-SR) and Lupus Quality of Life (Lupus QoL) questionnaires were administered via web to the patients with SLE in our cohort, along with questions on demographical and disease-related aspects. RESULTS: Among 99 patients who completed the questionnaire, fatigue prevalence was 75% and 31% scored TALS-SR test consistently with PTSD. Patients with PTSD achieved lower scores compared to those without PTSD in three Lupus QoL domains: planning (83.3 vs. 100, p = .035), body image (85.0 vs. 95.0, p = .031) and fatigue (66.7 vs. 91.7, p = .001). An inverse correlation was found between TALS-SR domains and Lupus QoL scores, particularly regarding fatigue with reaction to losses or upsetting events (ρ -0.458, p < .001). CONCLUSIONS: PTSD is possibly far more frequent in patients with SLE than in general population and exerts a detrimental influence on quality of life.

Sensorimotor integration within the primary motor cortex by selective nerve fascicle stimulation.

The integration of sensory inputs in the motor cortex is crucial for dexterous movement. We recently demonstrated that a closed-loop control based on the feedback provided through intraneural multichannel electrodes implanted in the median and ulnar nerves of a participant with upper limb amputation improved manipulation skills and increased prosthesis embodiment. Here we assessed, in the same participant, whether and how selective intraneural sensory stimulation also elicits a measurable cortical activation and affects sensorimotor cortical circuits. After estimating the activation of the primary somatosensory cortex evoked by intraneural stimulation, sensorimotor integration was investigated by testing the inhibition of primary motor cortex (M1) output to transcranial magnetic stimulation, after both intraneural and perineural stimulation. Selective sensory intraneural stimulation evoked a low-amplitude, 16 ms-latency, parietal response in the same area of the earliest component evoked by whole-nerve stimulation, compatible with fast-conducting afferent fibre activation. For the first time, we show that the same intraneural stimulation was also capable of decreasing M1 output, at the same time range of the short-latency afferent inhibition effect of whole-nerve superficial stimulation. The inhibition generated by the stimulation of channels activating only sensory fibres was stronger than that due to intraneural or perineural stimulation of channels activating mixed fibres. We demonstrate in a human subject that the cortical sensorimotor integration inhibiting M1 output previously described after the experimental whole-nerve stimulation is present also with a more ecological selective sensory fibre stimulation. KEY POINTS: Cortical integration of sensory inputs is crucial for dexterous movement. Short-latency somatosensory afferent inhibition of motor cortical output is typically produced by peripheral whole-nerve stimulation. We exploited intraneural multichannel electrodes used to provide sensory feedback for prosthesis control to assess whether and how selective intraneural sensory stimulation affects sensorimotor cortical circuits in humans. Activation of the primary somatosensory cortex (S1) was explored by recording scalp somatosensory evoked potentials. Sensorimotor integration was tested by measuring the inhibitory effect of the afferent stimulation on the output of the primary motor cortex (M1) generated by transcranial magnetic stimulation. We demonstrate in humans that selective intraneural sensory stimulation elicits a measurable activation of S1 and that it inhibits the output of M1 at the same time range of whole-nerve superficial stimulation.

Evoking Apparent Moving Sensation in the Hand via Transcutaneous Electrical Nerve Stimulation.

The restoration of sensory feedback in amputees plays a fundamental role in the prosthesis control and in the communication on the afferent channel between hand and brain. The literature shows that transcutaneous electrical nerve stimulation (TENS) can be a promising non-invasive technique to elicit sensory feedback in amputees, especially in the lower limb through the phenomenon of apparent moving sensation (AMS). It consists of delivering a sensation that moves along a specific part of the body. This study proposes to use TENS to elicit tactile sensations and adopt AMS to reproduce moving sensations on the hand, such as those related to an object moving in the hand or slipping upward or downward. To this purpose, the developed experimental protocol consists of two phases: (i) the mapping of the evoked sensations and (ii) the generation of the AMS. In the latter phase, the pulse amplitude variation (PAV), the pulse width variation (PWV), and the interstimulus delay modulation (ISDM) methods were compared. For the comparative analysis, the Wilcoxon-Mann-Whitney test with Bonferroni correction (P < 0.016) was carried out on the success rate and on the ranking of methods expressed by the subjects. Results from the mapping protocol show that the delivered sensations were mostly described by the subjects as almost natural and superficial tingling. Results from the AMS protocol show that, for each movement direction, the success rate of ISDM method is higher than that of PWV and PAV and significantly higher than that of PAV for the ulnar-median direction. It recreates an AMS in the hand that effectively allows discriminating the type of sensation and distinguishing the movement direction. Moreover, ISDM was ranked by the subjects as the favorite method for recreating a well-defined and comfortable moving sensation only in the median-ulnar direction. For the ranking results, there was not a statistically significant difference among the methods. The experiments confirmed the good potential of recreating an AMS in the hand through TENS. This encourages to push forward this study on amputees and integrate it in the closed-loop control of a prosthetic system, in order to enable full control of grasp stability and prevent the objects from slippage.

Restoring Tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands.

Despite previous studies on the restoration of tactile sensation on the fingers and the hand, there are no examples of use of the routed sensory information to finely control the prosthesis hand in complex grasp and manipulation tasks. Here it is shown that force and slippage sensations can be elicited in an amputee subject by means of biologically-inspired slippage detection and encoding algorithms, supported by a stick-slip model of the performed grasp. A combination of cuff and intraneural electrodes was implanted for eleven weeks in a young woman with hand amputation, and was shown to provide close-to-natural force and slippage sensations, paramount for significantly improving the subject's manipulative skills with the prosthesis. Evidence is provided about the improvement of the subject's grasping and manipulation capabilities over time, thanks to neural feedback. The elicited tactile sensations enabled the successful fulfillment of fine grasp and manipulation tasks with increasing complexity. Grasp performance was quantitatively assessed by means of instrumented objects and a purposely developed metrics. Closed-loop control capabilities enabled by the neural feedback were compared to those achieved without feedback. Further, the work investigates whether the described amelioration of motor performance in dexterous tasks had as central neurophysiological correlates changes in motor cortex plasticity and whether such changes were of purely motor origin, or else the effect of a strong and persistent drive of the sensory feedback.

Simultaneous sEMG Classification of Hand/Wrist Gestures and Forces.

Surface electromyography (sEMG) signals represent a promising approach for decoding the motor intention of amputees to control a multifunctional prosthetic hand in a non-invasive way. Several approaches based on proportional amplitude methods or simple thresholds on sEMG signals have been proposed to control a single degree of freedom at time, without the possibility of increasing the number of controllable multiple DoFs in a natural manner. Myoelectric control based on PR techniques have been introduced to add multiple DoFs by keeping low the number of electrodes and allowing the discrimination of different muscular patterns for each class of motion. However, the use of PR algorithms to simultaneously decode both gestures and forces has never been studied deeply. This paper introduces a hierarchical classification approach with the aim to assess the desired hand/wrist gestures, as well as the desired force levels to exert during grasping tasks. A Finite State Machine was introduced to manage and coordinate three classifiers based on the Non-Linear Logistic Regression algorithm. The classification architecture was evaluated across 31 healthy subjects. The "hand/wrist gestures classifier," introduced for the discrimination of seven hand/wrist gestures, presented a mean classification accuracy of 98.78%, while the "Spherical and Tip force classifier," created for the identification of three force levels, reached an average accuracy of 98.80 and 96.09%, respectively. These results were confirmed by Linear Discriminant Analysis (LDA) with time domain features extraction, considered as ground truth for the final validation of the performed analysis. A Wilcoxon Signed-Rank test was carried out for the statistical analysis of comparison between NLR and LDA and statistical significance was considered at p < 0.05. The comparative analysis reports not statistically significant differences in terms of F1Score performance between NLR and LDA. Thus, this study reveals that the use of non-linear classification algorithm, as NLR, is as much suitable as the benchmark LDA classifier for implementing an EMG pattern recognition system, able both to decode hand/wrist gestures and to associate different performed force levels to grasping actions.

EMG and ENG-envelope pattern recognition for prosthetic hand control.

BACKGROUND: This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG). NEW METHOD: The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data. RESULTS: An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 µs for each sample in the data window analyzed). COMPARISON WITH EXISTING METHOD: The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient. CONCLUSIONS: It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics.

Spike detection: The first step towards an ENG-based neuroprosheses.

BACKGROUND: Being able to control an upper limb prosthesis by means of the signals recorded from the peripheral nerves is not a trivial task. New generations of neural electrodes are able to record this information but the quality of the signal can make difficult the extraction of the useful information. Several techniques have been adopted both for central and peripheral acquisitions in order to remove the noise and/or enhance the electrical activity generated by the brain or carried by the nerves. NEW METHODS: In this review, common spike detection algorithms have been tested on both real and simulated recordings to verify which is the best choice to be applied in a neuroprosthetics context. In particular, the moving average algorithm (MAA), the non-linear energy operator (NEO) and the wavelet denoising (WD) have been implemented and their performance have been tested by means of the number of the detected real positives (RPs) and false positives (FPs). RESULTS: MAA outperforms the other techniques because it is capable of detecting a high amount of RPs and, compared to NEO, with a reduced number of FPs. COMPARISON WITH EXISTING METHODS: MAA needs only the information of the duration of the action potential while the NEO and the WD require the frequency and/or the shape of the action potentials. CONCLUSIONS: NEO and WD are algorithms requiring information about the signal, not a priori known. MAA, then, seems most suitable for online applications.

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons.

The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.

Literature Review on Needs of Upper Limb Prosthesis Users.

The loss of one hand can significantly affect the level of autonomy and the capability of performing daily living, working and social activities. The current prosthetic solutions contribute in a poor way to overcome these problems due to limitations in the interfaces adopted for controlling the prosthesis and to the lack of force or tactile feedback, thus limiting hand grasp capabilities. This paper presents a literature review on needs analysis of upper limb prosthesis users, and points out the main critical aspects of the current prosthetic solutions, in terms of users satisfaction and activities of daily living they would like to perform with the prosthetic device. The ultimate goal is to provide design inputs in the prosthetic field and, contemporary, increase user satisfaction rates and reduce device abandonment. A list of requirements for upper limb prostheses is proposed, grounded on the performed analysis on user needs. It wants to (i) provide guidelines for improving the level of acceptability and usefulness of the prosthesis, by accounting for hand functional and technical aspects; (ii) propose a control architecture of PNS-based prosthetic systems able to satisfy the analyzed user wishes; (iii) provide hints for improving the quality of the methods (e.g., questionnaires) adopted for understanding the user satisfaction with their prostheses.

Control of Prosthetic Hands via the Peripheral Nervous System.

This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

Differences between kinematic synergies and muscle synergies during two-digit grasping.

The large number of mechanical degrees of freedom of the hand is not fully exploited during actual movements such as grasping. Usually, angular movements in various joints tend to be coupled, and EMG activities in different hand muscles tend to be correlated. The occurrence of covariation in the former was termed kinematic synergies, in the latter muscle synergies. This study addresses two questions: (i) Whether kinematic and muscle synergies can simultaneously accommodate for kinematic and kinetic constraints. (ii) If so, whether there is an interrelation between kinematic and muscle synergies. We used a reach-grasp-and-pull paradigm and recorded the hand kinematics as well as eight surface EMGs. Subjects had to either perform a precision grip or side grip and had to modify their grip force in order to displace an object against a low or high load. The analysis was subdivided into three epochs: reach, grasp-and-pull, and static hold. Principal component analysis (PCA, temporal or static) was performed separately for all three epochs, in the kinematic and in the EMG domain. PCA revealed that (i) Kinematic- and muscle-synergies can simultaneously accommodate kinematic (grip type) and kinetic task constraints (load condition). (ii) Upcoming grip and load conditions of the grasp are represented in kinematic- and muscle-synergies already during reach. Phase plane plots of the principal muscle-synergy against the principal kinematic synergy revealed (iii) that the muscle-synergy is linked (correlated, and in phase advance) to the kinematic synergy during reach and during grasp-and-pull. Furthermore (iv), pair-wise correlations of EMGs during hold suggest that muscle-synergies are (in part) implemented by coactivation of muscles through common input. Together, these results suggest that kinematic synergies have (at least in part) their origin not just in muscular activation, but in synergistic muscle activation. In short: kinematic synergies may result from muscle synergies.

Restoring natural sensory feedback in real-time bidirectional hand prostheses.

Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user's intentions and the delivery of nearly "natural" sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and "life-like" quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.