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Tactile feedback is relevant in a broad range of human-machine interaction systems (e.g. teleoperation, virtual reality and prosthetics). The available tactile feedback interfaces comprise few sensing and stimulation units, which limits the amount of information conveyed to the user. The present study describes a novel technology that relies on distributed sensing and stimulation to convey comprehensive tactile feedback to the user of a robotic end effector. The system comprises six flexible sensing arrays (57 sensors) integrated on the fingers and palm of a robotic hand, embedded electronics (64 recording channels), a multichannel stimulator and seven flexible electrodes (64 stimulation pads) placed on the volar side of the subject's hand. The system was tested in seven subjects asked to recognize contact positions and identify contact sliding on the electronic skin, using distributed anode configuration (DAC) and single dedicated anode configuration. The experiments demonstrated that DAC resulted in substantially better performance. Using DAC, the system successfully translated the contact patterns into electrotactile profiles that the subjects could recognize with satisfactory accuracy ([Formula: see text] for static and [Formula: see text] for dynamic patterns). The proposed system is an important step towards the development of a high-density human-machine interfacing between the user and a robotic hand. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

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The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.

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As the technology moves towards more human-like bionic limbs, it is necessary to develop a feedback system that provides active touch feedback to a user of a prosthetic hand. Most of the contemporary sensory substitution methods comprise simple position and force sensors combined with few discrete stimulation units, and hence they are characterized with a limited amount of information that can be transmitted by the feedback. The present study describes a novel system for tactile feedback integrating advanced multipoint sensing (electronic skin) and stimulation (matrix electrodes). The system comprises a flexible sensing array (16 sensors) integrated on the index finger of a Michelangelo prosthetic hand mockup, embedded interface electronics and multichannel stimulator connected to a flexible matrix electrode (24 pads). The developed system conveys contact information (binary detections) to the user. To demonstrate the feasibility, the system was tested in six able-bodied subjects who were asked to recognize static patterns (contact position) with two different spatial resolutions and dynamic movement patterns (i.e., sliding along and/or across the finger) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into electrotactile profiles, which the subjects could recognize with good performance. The success rates (mean ± standard deviation) for the static patterns were 91 ± 4% and 58 ± 10% for low and high spatial resolution, respectively, while the success rate for sliding touch was 94 ± 4%. These results demonstrate that the developed system is an important step towards a new generation of tactile feedback interfaces that can provide high-bandwidth connection between the user and his/her bionic limb. Such systems would allow mimicking spatially distributed natural feedback, thereby facilitating the control and embodiment of the artificial device into the user body scheme.

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Stroke patients suffer from impairments of both motor and somatosensory functions. The functional recovery of upper extremities is one of the primary goals of rehabilitation programs. Additional somatosensory deficits limit sensorimotor function and significantly affect its recovery after the neuromotor injury. Sensory substitution systems, providing tactile feedback, might facilitate manipulation capability, and improve patient's dexterity during grasping movements. As a first step toward this aim, we evaluated the ability of healthy subjects in exploiting electrotactile feedback on the shoulder to determine the number of perceived stimuli in numerosity judgment tasks. During the experiment, we compared four different stimulation patterns (two simultaneous: short and long, intermittent and sequential) differing in total duration, total energy, or temporal synchrony. The experiment confirmed that the subject ability to enumerate electrotactile stimuli decreased with increasing the number of active electrodes. Furthermore, we found that, in electrotactile stimulation, the temporal coding schemes, and not total energy or duration modulated the accuracy in numerosity judgment. More precisely, the sequential condition resulted in significantly better numerosity discrimination than intermittent and simultaneous stimulation. These findings, together with the fact that the shoulder appeared to be a feasible stimulation site to communicate tactile information via electrotactile feedback, can serve as a guide to deliver tactile feedback to proximal areas in stroke survivors who lack sensory integrity in distal areas of their affected arm, but retain motor skills.

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Human skin has remarkable features such as self-healing ability, flexibility, stretchability, high sensitivity and tactile sensing capability [...].

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This paper proposes a validation method of the fabrication technology of a screen-printed electronic skin based on polyvinylidene fluoride-trifluoroethylene P(VDF-TrFE) piezoelectric polymer sensors. This required researchers to insure, through non-direct sensor characterization, that printed sensors were working as expected. For that, we adapted an existing model to non-destructively extract sensor behavior in pure compression (i.e., the d33 piezocoefficient) by indentation tests over the skin surface. Different skin patches, designed to sensorize a glove and a prosthetic hand (11 skin patches, 104 sensors), have been tested. Reproducibility of the sensor response and its dependence upon sensor position on the fabrication substrate were examined, highlighting the drawbacks of employing large A3-sized substrates. The average value of d33 for all sensors was measured at incremental preloads (1-3 N). A systematic decrease has been checked for patches located at positions not affected by substrate shrinkage. In turn, sensor reproducibility and d33 adherence to literature values validated the e-skin fabrication technology. To extend the predictable behavior to all skin patches and thus increase the number of working sensors, the size of the fabrication substrate is to be decreased in future skin fabrication. The tests also demonstrated the efficiency of the proposed method to characterize embedded sensors which are no more accessible for direct validation.

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Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 µ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices.

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Among most challenging open issues in prosthetic research is the development of a robust bidirectional interface between a prosthesis and its user. Commercially available prosthetic systems are mechanically advanced, but they do not provide somatosensory feedback. Here, we present a novel non-invasive interface for multichannel electrotactile feedback, comprising a matrix of 24 pads, and we investigate the ability of able-bodied human subjects to localize the electrotactile stimulus delivered through the matrix. For this purpose, we tested conventional stimulation (same frequency for all pads) and a novel dual-parameter modulation scheme (interleaved frequency and intensity) designed to facilitate the spatial localization over the electrode. Electrotactile stimulation was also compared to mechanical stimulation of the same locations on the skin. Experimental results on eight able-bodied subjects demonstrated that the proposed interleaved coding substantially improved the spatial localization compared to same-frequency stimulation. The results also showed that same-frequency stimulation was equivalent to mechanical stimulation, whereas the performance with dual-parameter modulation was significantly better. These are encouraging outcomes for the application of a multichannel interface for the restoration of feedback in prosthetics. The high-resolution augmented interfaces might be used to explore novel scenarios for effective communication with the prosthesis user enabled by maximizing information transmission.

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Tactile sensors are widely employed to enable the sense of touch for applications such as robotics and prosthetics. In addition to the selection of an appropriate sensing material, the performance of the tactile sensing system is conditioned by its interface electronic system. On the other hand, due to the need to embed the tactile sensing system into a prosthetic device, strict requirements such as small size and low power consumption are imposed on the system design. This paper presents the experimental assessment and characterization of an interface electronic system for piezoelectric tactile sensors for prosthetic applications. The interface electronic is proposed as part of a wearable system intended to be integrated into an upper limb prosthetic device. The system is based on a low power arm-microcontroller and a DDC232 device. Electrical and electromechanical setups have been implemented to assess the response of the interface electronic with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system.

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Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

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Myoelectric prostheses are successfully controlled using muscle electrical activity, thereby restoring lost motor functions. However, the somatosensory feedback from the prosthesis to the user is still missing. The sensory substitution methods described in the literature comprise mostly simple position and force sensors combined with discrete stimulation units. The present study describes a novel system for sophisticated electrotactile feedback integrating advanced distributed sensing (electronic skin) and stimulation (matrix electrodes). The system was tested in eight healthy subjects who were asked to recognize the shape, trajectory, and direction of a set of dynamic movement patterns (single lines, geometrical objects, letters) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into the moving electrotactile profiles, which the subjects could recognize with a good performance (shape recognition: 86±8% lines, 73±13% geometries, 72±12% letters). In particular, the subjects could identify the movement direction with a high confidence. These results are in accordance with previous studies investigating the recognition of moving stimuli in human subjects. This is an important development towards closed-loop prostheses providing comprehensive and sophisticated tactile feedback to the user, facilitating the control and the embodiment of the artificial device into the user body scheme.

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Tactile sensing helps robots interact with humans and objects effectively in real environments. Piezoelectric polymer sensors provide the functional building blocks of the robotic electronic skin, mainly thanks to their flexibility and suitability for detecting dynamic contact events and for recognizing the touch modality. The paper focuses on the ability of tactile sensing systems to support the challenging recognition of certain qualities/modalities of touch. The research applies novel computational intelligence techniques and a tensor-based approach for the classification of touch modalities; its main results consist in providing a procedure to enhance system generalization ability and architecture for multi-class recognition applications. An experimental campaign involving 70 participants using three different modalities in touching the upper surface of the sensor array was conducted, and confirmed the validity of the approach.

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This work presents the transmission line equivalent model for lossy piezoelectric polymers and its SPICE implementation. The model includes the mechanical/viscoelastic, dielectric/electrical, and piezoelectric/electromechanical losses in a novel way by using complex elastic, dielectric, and piezoelectric constants obtained from the measured impedances of PVDF and PVDF-TrFE samples by nonlinear regression technique. The equivalent circuit parameters are derived from analogies between a lossy electrical transmission line and acoustic wave propagation. The simulated impedance and phase plots of various samples, working in thickness mode, have been shown to agree well with the measured data.

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In the last decade, fundamental advances in whole cell based sensors and microsystems have established the extracellular acidification rate monitoring of cell cultures as an important indicator of the global cellular metabolism. Innovative approaches adopting advanced integrated sensor array-based microsystems represent an emerging technique with numerous biomedical applications. This paper reports a cell-based microsystem, for multisite monitoring of the physiological state of cell populations. The functional components of the microsystem are an ion sensitive field effect transistor (ISFET) array-based sensor chip and a CMOS integrated circuit for signal conditioning and sensor signal multiplexing. In order to validate the microsystem capabilities for in-vitro toxicity screening applications, preliminary experimental measurements with Cheratinocytes, and CHO cells are presented. Variations in the acidification rate, imputable to the inhibitory effect of the drug on the metabolic cell activity have been monitored and cell viability during long term measurements has been also demonstrated.

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