Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees.

Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.

Comparative evaluation of different wavelet thresholding methods for neural signal processing.

Neural signal decoding is the basis for the development of neuroprosthetic devices and systems. Depending on the part of the nervous system these signals are picked up from, different signal-to-noise ratios (SNR) can be experienced. Wavelet denoising is often adopted due to its capability of reducing, to some extent, the noise falling within the signal spectrum. Several variables influence the denoising quality, but usually the focus in on the selection of the best performing mother wavelet. However, the threshold definition and the way it is applied to the signal have a significant impact on the denoising quality, determining the amount of noise removed and the distortion introduced on the signal. This work presents a comparative analysis of different threshold definition and thresholding mechanisms on neural signals, either largely adopted for neural signal processing or not. In order to evaluate the quality of the denoising in terms of the introduced distortion, which is important when decoding is implemented through spike-sorting algorithms, a synthetic dataset built on real action potentials was used, creating signals with different SNR and characterized by an additive white Gaussian noise (AWGN). The obtained results reveal the superiority of an approach, originally conceived for noisy non-linear time series, over the more typical ones. When compared to the original signal, a correlation above 0.9 was obtained, while in terms of root mean square error (RMSE) an improvement of 13% and 33% was reported with respect to the Minimax and Universal thresholds respectively.

A 64-channels neural interface for biopotentials recording and PNS stimulation.

A biomedical interface that combines into a single and compact device the recording of biopotentials and the electrical stimulation of neural fibres is presented. It is intended for enabling the control over a robotic hand and for restoring the sensory feedback in amputees by directly interfacing the peripheral nervous system (PNS) in closed-loop. A modular system consisting in one or more independent 16-channels bidirectional units was conceived. Each module is based on three 0.35µm bulk-CMOS integrated circuits (ICs): a recording unit, a High-Voltage (HV) stimulator and a HV booster. A tunable bandwidth (10Hz-8kHz) allows the recording IC to acquire both electroneurographyc (ENG) and electromiographyc (EMG) signals with a programmable gain up to 43.5dB. The signals are then converted into a digital domain by means of a ΣΔ converter. Due to the typical high impedance at the electrode-tissue interface, a programmable HV booster that increases the stimulation voltage up to 19V was designed. It is directly controlled by the stimulation module that generates current-based pulses with a programmable amplitude and pulse-width. The whole system was validated by means of in-vivo experiments in rats.

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

Fully Textile, PEDOT:PSS Based Electrodes for Wearable ECG Monitoring Systems.

GOAL: To evaluate a novel kind of textile electrodes based on woven fabrics treated with PEDOT: PSS, through an easy fabrication process, testing these electrodes for biopotential recordings. METHODS: Fabrication is based on raw fabric soaking in PEDOT: PSS using a second dopant, squeezing and annealing. The electrodes have been tested on human volunteers, in terms of both skin contact impedance and quality of the ECG signals recorded at rest and during physical activity (power spectral density, baseline wandering, QRS detectability, and broadband noise). RESULTS: The electrodes are able to operate in both wet and dry conditions. Dry electrodes are more prone to noise artifacts, especially during physical exercise and mainly due to the unstable contact between the electrode and the skin. Wet (saline) electrodes present a stable and reproducible behavior, which is comparable or better than that of traditional disposable gelled Ag/AgCl electrodes. CONCLUSION: The achieved results reveal the capability of this kind of electrodes to work without the electrolyte, providing a valuable interface with the skin, due to mixed electronic and ionic conductivity of PEDOT: PSS. These electrodes can be effectively used for acquiring ECG signals. SIGNIFICANCE: Textile electrodes based on PEDOT: PSS represent an important milestone in wearable monitoring, as they present an easy and reproducible fabrication process, very good performance in wet and dry (at rest) conditions and a superior level of comfort with respect to textile electrodes proposed so far. This paves the way to their integration into smart garments.

Evaluation of novel textile electrodes for ECG signals monitoring based on PEDOT:PSS-treated woven fabrics.

Despite surface electrodes technology for biopotential recording is well established, different researches are aimed at overcoming the limitations exhibited by the available solutions. In this paper, a proposal for the low-cost development of textile electrodes based on woven fabrics treated with polymer poly-3,4-ethylenedioxythiophene doped with poly(styrene sulfonate) ( PEDOT: PSS), is presented. Compared to other approaches, the proposed one can be exploited on any finished fabric. An accurate analysis of the electrodes performance, based on impedance measurements and signal processing techniques, both in wet and dry conditions, reveals the virtues and vices of the proposed solution, when used for electrocardiogram recording. In particular, the potentialities of these electrodes clearly emerge, in terms of ability to work without any electrolyte, providing a valuable interface between the skin and the electrode, in some cases achieving better performance than commercial disposable electrodes.

Telemedicine applied to kinesiotherapy for hand dysfunction in patients with systemic sclerosis and rheumatoid arthritis: recovery of movement and telemonitoring technology.

OBJECTIVE: To describe a feasibility study focused on a telemonitoring approach to self-managed kinesiotherapy sessions for the rehabilitation of hand function in patients with systemic sclerosis (SSc) and rheumatoid arthritis (RA). METHODS: Ten patients with SSc and 10 with RA were enrolled in a 3-month controlled trial (approval no. 9751/2012 - Italian Department of Health) to perform a home kinesiotherapy protocol, consisting of strengthening and mobility exercises, using a newly developed telemedicine system (a portable device and the related telemonitoring infrastructure). A further 10 patients with SSc and 10 with RA were enrolled as controls to perform a similar home kinesiotherapy protocol with the aid of common daily-life objects. Both groups were evaluated at baseline and at followup, after 6 and 12 weeks. The primary outcome of the trial was hand function measured by Dreiser's index (Functional Index for Hand OA, FIHOA), Health Assessment Questionnaire (HAQ), and the Hand Mobility in Scleroderma (HAMIS) test (only for SSc). RESULTS: Patients with SSc showed an improvement of FIHOA in both arms (p < 0.01) but the HAQ (p = 0.016) and the HAMIS test (right hand p = 0.016, left hand p = 0.075) improved significantly only in the experimental arm. Patients with RA showed a statistically significant improvement of FIHOA (p = 0.013) and HAQ (p = 0.015) in the experimental arm, while patients in the control arm did not significantly improve. However, no statistically significant differences in outcome measures between treatment methods were observed. Withdrawals were higher in control arms (SSc 20%; RA 30%) than in experimental arms (SSc 10%; RA 10%). CONCLUSION: Telemonitoring of self-administered kinesiotherapy programs is a promising approach to the rehabilitation of hand functions in patients with rheumatic disease.

A Device for Local or Remote Monitoring of Hand Rehabilitation Sessions for Rheumatic Patients.

Current clinical practice suggests that recovering the hand functionality lost or reduced by injuries, interventions and chronic diseases requires, beyond pharmacological treatments, a kinesiotherapic intervention. This form of rehabilitation consists of physical exercises adapted to the specific pathology. Its effectiveness is strongly dependent on the patient's adhesion to such a program. In this paper we present a novel device with remote monitoring capabilities expressly conceived for the needs of rheumatic patients. It comprises several sensorized tools and can be used either in an outpatient clinic for hand functional evaluation, connected to a PC, or afforded to the patient for home kinesiotherapic sessions. In the latter case, the device guides the patient in the rehabilitation session, transmitting the relevant statistics about his performance to a TCP/IP server exploiting a GSM/GPRS connection for deferred analysis. An approved clinical trial has been set up in Italy, involving 10 patients with Rheumatoid Arthritis and 10 with Systemic Sclerosis, enrolled for 12 weeks in a home rehabilitation program with the proposed device. Their evaluation has been performed with traditional methods but also with the proposed device. Subjective (hand algofunctional Dreiser's index) and objective (ROM, strength, dexterity) parameters showed a sustained improvement throughout the follow-up. The obtained results proved that the device is an effective and safe tool for assessing hand disability and monitoring kinesiotherapy exercise, portending the potential exploitability of such a methodology in clinical practice.

NInFEA: an embedded framework for the real-time evaluation of fetal ECG extraction algorithms.

Fetal electrocardiogram (ECG) extraction from non-invasive biopotential recordings is a long-standing research topic. Despite the significant number of algorithms presented in the scientific literature, it is difficult to find information about embedded hardware implementations able to provide real-time support for the required features, bridging the gap between theory and practice. This article presents the NInFEA (non-invasive fetal ECG analysis) tool, an embedded hardware/software framework based on the hybrid dual-core OMAP-L137 low-power processor for the real-time evaluation of fetal ECG extraction algorithms. The hybrid platform, including a digital signal processor (DSP) and a general-purpose processor (GPP), allows achieving the best performance compared with single-core architectures. The GPP provides a portable graphical user interface, whereas the DSP is extensively used for advanced signal processing tasks. As a case study, three state-of-the-art fetal ECG extraction algorithms have been ported onto NInFEA, along with some support routines needed to provide the additional information required by the clinicians and supported by the user interface. NInFEA can be regarded both as a reference design for similar applications and as a common embedded low-power testbed for real-time fetal ECG extraction algorithms.