In this paper, we discuss some evidences provided by neuroscience and psychology studies on human time perception, in terms of its representation and its psychological distortion due to emotional state variations. We propose, then, a novel model inspired by these recent findings to be applied in social robotics control architectures, with a specific reference to an existing and already tested bio-inspired cognitive architecture called SEAI (Social Emotional Artificial Intelligence). An hypothesis on how to represent emotional state influence on time perception in SEAI will be presented, discussing the consequent potential of the system with this integrated feature.

This paper reports the design and assessment of a neuro-fuzzy model to support clinicians during virtual reality therapy. The implemented model is able to automatically recognize the perceived stress levels of the patients by analyzing physiological and behavioral data during treatment. The model, consisting of a self-organizing map and a fuzzy-rule-based module, was trained unobtrusively recording electrocardiogram, breath rate and activity during stress inoculation provided by the exposure to virtual environments. Twenty nurses were exposed to sessions simulating typical stressful situations experienced at their workplace. Four levels of stress severity were evaluated for each subject by gold standard clinical scales administered by trained personnel. The model's performances were discussed and compared with the main machine learning algorithms. The neuro-fuzzy model shows better performances in terms of stress level classification with 83% of mean recognition rate. RESEARCH HIGHLIGHTS Stress levels were predicted on the basis of physiological computing using a neuro-fuzzy model during virtual reality therapy. Features were extracted from ECG and respiration obtaining high accuracy and optimization of computational costs. The neuro-fuzzy model shows better performance than the more frequently adopted classifiers. This approach may enhance the use of physiological computing for stress treatment in clinical practice.

This paper describes a context-dependent social gaze-control system implemented as part of a humanoid social robot. The system enables the robot to direct its gaze at multiple humans who are interacting with each other and with the robot. The attention mechanism of the gaze-control system is based on features that have been proven to guide human attention: nonverbal and verbal cues, proxemics, the visual field of view, and the habituation effect. Our gaze-control system uses Kinect skeleton tracking together with speech recognition and SHORE-based facial expression recognition to implement the same features. As part of a pilot evaluation, we collected the gaze behavior of 11 participants in an eye-tracking study. We showed participants videos of two-person interactions and tracked their gaze behavior. A comparison of the human gaze behavior with the behavior of our gaze-control system running on the same videos shows that it replicated human gaze behavior 89% of the time.

Human-Robot Interaction (HRI) studies have recently received increasing attention in various fields, from academic communities to engineering firms and the media. Many researchers have been focusing on the development of tools to evaluate the performance of robotic systems and studying how to extend the range of robot interaction modalities and contexts. Because people are emotionally engaged when interacting with computers and robots, researchers have been focusing attention on the study of affective human-robot interaction. This new field of study requires the integration of various approaches typical of different research backgrounds, such as psychology and engineering, to gain more insight into the human-robot affective interaction.

This paper presents an innovative wearable kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove is conceived to capture hand movement and gesture by using KPF in a double-layer configuration working as angular sensors (electrogoniometers). The sensing glove prototype is endowed by three KPF goniometers, used to track flexion and extension movement of metacarpophalangeal joint of thumb, index, and middle fingers. The glove is devoted to the continuous monitoring of patients during their daily-life activities, in particular for stroke survivors during their rehabilitation. The prototype performances have been evaluated in comparison with an optical tracking system considered as a gold standard both for relieving static and dynamic posture and gesture of the hand. The introduced prototype has shown very interesting figures of merit. The angular error, evaluated through the standard Bland Altman analysis, has been estimated in ±3° which is slightly less accurate than commercial electrogoniometers. Moreover, a new conceptual prototype design, preliminary evaluated within this study, is presented and discussed in order to solve actual limitations in terms of number and type of sensor connections, avoiding mechanical constraints given by metallic inextensible wires and improving user comfort.

There is a close correlation between stress and health risk factors such as poor immune function and cardiovascular problems. Various researches showed that long-term exposure to stress and its related diseases are responsible of dramatic increase of mortality in the Western Countries. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors. In this paper a wearable system able to implement the acquisition and the real-time elaboration of the ECG signal for stress management purposes will be described. A novel and robust algorithm for QRS complex detection has been developed. Robust QRS detection is fundamental to evaluate Heart Rate and Heart Rate Variability that are relevant parameters used as quantitative marker related to mental stress. In comparison to existing solutions the realized algorithm presents many advantages: an adaptive optimal filtering technique that avoids the use of thresholds and empirical rules for R peaks detection, low computational cost for real time elaboration and good tollerance with noisy ECG signal.

Stress and its related comorbid diseases are responsible for a large proportion of disability worldwide. In particular, chronic stress is the main responsible for the dramatic increase of premature mortality in the Western countries. However, advanced simulation and sensing technologies, such as virtual reality and mobile biosensors offer interesting opportunities for innovative personal health-care solutions to stress. In this work, we describe a technology-based approach to the assessment and treatment of stress that is based on the vision of Interreality. The main feature of interreality is the creation of a hybrid-augmented experience merging the physical and virtual world. This is achieved through: a) an extended sense of presence: in interreality advanced simulations (3-D virtual worlds) are used to transform health guidelines and provisions into experience; b) an extended sense of community (social presence): interreality uses hybrid social interaction and dynamics of group sessions to provide each user with targeted social support in both the physical and virtual world; c) real-time feedback between the physical and virtual worlds: interreality uses bio and activity sensors and devices (e.g. smartphones) to track both the behavior and the health status of the user in real time and to provide targeted suggestions and guidelines. The feedback activity is twofold: (1) behavior in physical world influences the experience in the virtual one, and (2) behavior in the virtual world influences the experience in the real one.

People with autism are known to possess deficits in processing emotional states, both their own and of others. A humanoid robot, FACE (Facial Automation for Conveying Emotions), capable of expressing and conveying emotions and empathy has been constructed to enable autistic children and adults to better deal with emotional and expressive information. We describe the development of an adaptive therapeutic platform which integrates information deriving from wearable sensors carried by a patient or subject as well as sensors placed in the therapeutic ambient. Through custom developed control and data processing algorithms the expressions and movements of FACE are then tuned and modulated to harmonize with the feelings of the subject postulated by their physiological and behavioral correlates. Preliminary results demonstrating the potential of adaptive therapy are presented.

Handshaking represents a complex motor and cognitive task that poses several challenges from both engineering and neuroscientific viewpoints. In particular, it is an intriguing application which can be profitably studied in the field of Human Robot Interaction (HRI). In this work an experimental paradigm is proposed to investigate the neural correlates of handshaking between humans and between humans and robots using functional Magnetic Resonance Imaging. More specifically the role of visual and haptic components during handshaking interaction will be studied. A wearable sensing glove will be used to monitor hand finger position and movement. Preliminary results will be reported and discussed.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting.

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

The purpose of this study is to comparatively evaluate the performance of different wearable systems based on indirect breathing monitoring in terms of susceptibility to motion artifacts. These performances are compared with direct respiratory measurements using a spirometer, which is accurate, reliable, and less sensitive to movement artifacts, but cannot be integrated into truly wearable form. Experiments were carried out on four indirect methods implemented into wearable systems, inductive plethysmography, impedance plethysmography, piezoresistive pneumography, and piezoelectric pneumography, to ascertain the performance of each of them in terms of noise due to movement artifacts, as well as to study the effects of different movements or gestures during each test. A group of volunteers was asked to wear all of the breath monitoring systems simultaneously along with the face mask of the spirometer while carrying out four physical exercises in a gym under controlled conditions. Data are analyzed in the time and frequency domain to estimate the frequency respiration from each wearable system and compare it with those of the spirometer. Results confirmed that all the wearable systems are somehow affected by movement artifacts, but statistical investigation showed that for most of the physical exercises, three out of four, piezoelectric pneumography provided best performance in terms of robustness and reduced susceptibility to movement artifacts.

This paper is concerned with a new wearable system, which is able to monitor several vital signals and physiological variables in order to determine the cardiopulmonary activity status during emergencies. The innovative system consists of a multimodal broadband piezoelectric transducer based on polyvinylidene fluoride polymer integrated into a textile belt wrapped around the chest. An advanced electronic control unit, floating power supply, and wireless communication support make it suitable for portable monitoring during critical cardiopulmonary failures. The multimodal transducer is innovative in that only one sensitive element is employed to work as either an ultrasound (US) transceiver or piezoelectric sensor. The US transceiver is enabled to work at high frequency, i.e., it is excited by suitable pulses to emit an ultrasonic wave, which penetrates the body and receives the echo signals bouncing off the biological interfaces having different acoustic impedances. The piezoelectric sensor works at low frequency and acquires both signals generated by heart apex movements and the mechanical movement of the chest induced by respiration. This multimodality is allowed by a broadband of sensitivity jointly at a low value of the figure of merit (Q). Moreover, the transducer thickness is thin enough to assure a good adaptability to the biological site, and it is equipped with an advanced control unit enabling to switch from a high to a low working frequency. If jointly used along with an ECG wearable Holter, this transducer can be used to provide an exhaustive picture of the health status of the subject in the diagnostic and prognostic domains.

The aim of this paper is constituted by the feasibility study and development of a system based on Field Programmable Gate Array for the most significant cardiac arrhythmias recognition by means of Kohonen Self-Organizing Map. The feasibility study on an implementation on the XILINX Virtex®-4 FX12 FPGA is proposed, in which the QRS complexes are extracted and classified in real time between normal or pathologic classes. The whole digital implementation is validated to be integrated in wearable cardiac monitoring systems.

The present work is focused on the improvement of a Sensing Seat system previously developed by the authors for the initial authentication purpose in office and car scenarios. The goal is to obtain an event-related continuous authentication system, where the human subject should not take care of the system itself so that he is free to perform his normal actions. The system is realized by means of a sensing cover where conductive elastomers are used as strain sensors. The deformation of the cover caused by the body shape while actions are performed by the subject are used to obtain time-dependent relevant features. Such information are then analyzed by suitable classifiers that are able to perform the real-time continuous authentication task. A measurement campaign was carried out using data from 24 human subjects employed in an office scenario while a set of 22 actions were performed. The authentication capabilities of the system are reported in terms of acceptance and rejection rates, showing a high degree of correct classification.

The profound, pervasive and enduring consequences of ageing population present enormous challenges as well as enormous opportunities for Information and Communication Technology. The EU funded OASIS project, a Large Scale Integrated Project, is aimed to develop an open and innovative reference architecture, based upon ontologies and semantic services, that will allow plug and play and cost-effective interconnection of existing and new services in all domains required for the independent and autonomous living of the elderly and their enhanced quality of life. Among other technological advances, in OASIS we are developing a smart multisensorial platform for monitoring the lower limbs movements, as well as the muscular activations. We are using unobtrusive integrated sensors to transduce posture and kinematic variables and to acquire surface Electromiography (sEMG). The platform is able to analyze and merge the sEMG signals and kinematics variables to provide a single coherent dynamic information of the acquired movements. A Predictive Dynamic Model (PDM) based on machine learning techniques assesses the physiological muscular recruitments as well as muscular fatigue and physiological conditions.

This work is focused on the design and the realization of a sensing seat system for human authentication. Such a system may be used for security purposes in trucks, cars, offices, and scenarios where human subject authentication is needed and a seat is available. The sensing seat is realized by a seat coated with a removable Lycra sensing cover equipped with a piezoresistive sensor network. Since each sensor consists of a conductive elastomer composite rubber screen printed onto a cotton Lycra fabric, the sensing cover is able to respond to simultaneous deformations in different areas. This technology avoids the use of rigid electronic components and enables the realization of different cover layouts according to different types of seats. The algorithms for the enrollment, authentication, and monitoring tasks are discussed. A measurement campaign was carried out using data from 40 human subjects. The authentication capabilities of the system are reported in terms of acceptance and rejection rates, showing a high degree of correct classification.

New actuators are greatly demanded today in order to develop magnetic resonance imaging (MRI)-compatible mechatronic systems capable of extended and improved capabilities. They are particularly needed for MRI-guided interventional or rehabilitation procedures. Actuators based on dielectric elastomers, a specific class of electroactive polymers, appear as suitable candidates for new MRI-compatible technologies, due to their intrinsic material properties and working principle. This paper presents the first investigation on the MRI compatibility of a recently developed linear contractile actuator made of a silicone elastomer. The assessed absence of any degradation of both the actuator electromechanical performance in the MRI environment and the quality of images acquired from a phantom demonstrated the MRI compatibility of the actuator. These results suggest the suitability of this soft actuation technology as a possible new entry in the class of MRI compatible mechatronic systems.

In human-machine social interaction, the intersection between biology and engineering needs a context which allows for the development of adapting dynamics. The android FACE is able to interact with the external environment, interpreting and conveying emotions through non verbal communication. FACE captures expressive and psychophysical correlates from its interlocutor and actuates behaviours following two communicative modalities of semeiology. FACE interacts with kinesics, non verbal communication conveyed by body part movements, or facial expressions, and so on, taking into account the proxemic space. FACE'S goal is define and test a therapeutic protocol for autism in order to enhance social and emotive abilities in people with autism. Data regarding our latest clinical study are reported. The clinical study was aimed at verifying FACE'S capabilities in enhancing imitative skills and shared attention in subjects with autism. In particular, we monitored their attention towards FACE and then we checked if the android remains a restricted and repetitive interest or an object to share with the therapist. The study involved an experimental group, composed of 4 children with autism. The participants were diagnosed using ADI-R and ADOS-G, two specific diagnostic instruments. The experimental set up consisted of a specially equipped room provided with two remotely orientable cameras in which the subject, under the supervision of a therapist, can interact with FACE. During twenty minutes sessions, we observed both spontaneous behavior of the participants and their reactions to therapists presses in correlation with the time course of the physiological and behavioural data. The participants showed a spontaneous ability of imitation of the head and facial movements of the android. Moreover we observed that the children with autism focused their attention towards FACE'S eye movements following verbal suggestions from the therapist.

In this paper we investigate the possibility of processing the tactile perception by using a novel biomimetic approach for the pattern recognition module. The goal is to enhance the perception in complex virtual environments deriving from haptic displays mimicking human tactile discrimination. To do this we explored a Minimally Invasive Surgery application where the tactile information are strictly limited. In fact, this promising technique suffers from some evident limitations due to the surgeon loss of tactile perception during palpation of internal organs. This is basically due to the mechanical transmission of the elongated tools used during operation. We propose to integrate an Artificial Neural Network in an electronic board capable of processing data provided by a sensorized laparoscopic tool. The capabilities of several pattern recognition techniques present in literature, the Principal Component Analysis (PCA), a Multilayer Perceptron (MLP) and a Kohonen Self-Organising Map (KSOM) are investigated. The results are compared with that obtained psychophysically on five viscoelastic materials.

In this paper, several issues concerning the development of textiles endowed with electronic functions will be discussed. In particular, issues concerning materials, structures, electronic models, and the mechanical constraints due to textile technologies will be detailed. The idea starts from an already developed organic field-effect transistor that is realized on a flexible film that can be applied, after the assembly, on whatever kind of substrate, in particular, on textiles. This could pave the way to a variety of applications aimed to conjugate the favorable mechanical properties of textiles with the electronic functions of transistors. Furthermore, a possible perspective for the developments of organic sensors based on this structure are described.

This paper describes the early conception and latest developments of electroactive polymer (EAP)-based sensors, actuators, electronic components, and power sources, implemented as wearable devices for smart electronic textiles (e-textiles). Such textiles, functioning as multifunctional wearable human interfaces, are today considered relevant promoters of progress and useful tools in several biomedical fields, such as biomonitoring, rehabilitation, and telemedicine. After a brief outline on ongoing research and the first products on e-textiles under commercial development, this paper presents the most highly performing EAP-based devices developed by our lab and other research groups for sensing, actuation, electronics, and energy generation/storage, with reference to their already demonstrated or potential applicability to electronic textiles.

In this paper, we report on a new technology used to implement strain sensors to be integrated in usual garments. A particular conductive mixture based on commercial products is realized and directly spread over a piece of fabric, which shows, after the treatment, piezoresistive properties, i.e., a change in resistance when it is strained. This property is exploited to realize sensorized garments such as gloves, leotards, and seat covers capable of reconstructing and monitoring body shape, posture, and gesture. In general, this technology is a good candidate for adherent wearable systems with excellent mechanical coupling with body surface. Here, we mainly focused on a sensorized glove able to detect posture and movements of the fingers. It could be used in several fields of application. We report on experimental results of a sensorized glove used as movements recorder for rehabilitation therapies and medicine. Furthermore, we describe a dedicated methodology used to read the output sensors which allowed to avoid using metallic wires for the connections. The price to be paid for all these advantages is a nonlinear electric response of the fabric sensor and a too long settling time, that in principle, make these sensors not suitable for real-time applications. Here we propose a hardware and computational solution to overcome this limitation.

In the last few years, the smart textile area has become increasingly widespread, leading to developments in new wearable sensing systems. Truly wearable instrumented garments capable of recording behavioral and vital signals are crucial for several fields of application. Here we report on results of a careful characterization of the performance of innovative fabric sensors and electrodes able to acquire vital biomechanical and physiological signals, respectively. The sensing function of the fabric sensors relies upon newly developed strain sensors, based on rubber-carbon-coated threads, and mainly depends on the weaving topology, and the composition and deposition process of the conducting rubber-carbon mixture. Fabric sensors are used to acquire the respitrace (RT) and movement sensors (MS). Sensing features of electrodes, instead rely upon metal-based conductive threads, which are instrumental in detecting bioelectrical signals, such as electrocardiogram (ECG) and electromyogram (EMG). Fabric sensors have been tested during some specific tasks of breathing and movement activity, and results have been compared with the responses of a commercial piezoelectric sensor and an electrogoniometer, respectively. The performance of fabric electrodes has been investigated and compared with standard clinical electrodes.

In this paper we propose an innovative prototype of a haptic display for whole-hand immersive exploration. We envision a new concept of haptic display, the Haptic Black Box, which can be imagined as a box where the operator can poke his/her bare hand, and interact with the virtual object by freely moving the hand without mechanical constraints. In this way sensory receptors on the whole operator's hand would be excited, rather than restricting to just one or few fingertips or phalanges. To progress towards such a challenging goal, magnetorheological (MR) fluids represent a very interesting and completely innovative technology. These fluids are composed of micronsized, magnetizable particles immersed in a synthetic oil. Exposure to an external magnetic field induces in the fluid a change in rheological behaviour turning it into a near-solid in few milliseconds. By removing the magnetic field, the fluid quickly returns to its liquid state. We briefly report on the design of this device, describe psychophysical experiments to assess performance for softness and shape exploration, and report on the experimental results.

Many applications in teleoperation and virtual reality call for the implementation of effective means of displaying to the human operator information on the softness and other mechanical properties of objects being touched. The ability of humans to detect softness of different objects by tactual exploration is intimately related to both kinesthetic and cutaneous perception, and haptic displays should be designed so as to address such multimodal perceptual channel. In this paper, we investigate the possibility of surrogating detailed tactile information for softness discrimination, with information on the rate of spread of the contact area between the finger and the specimen as the contact force increases. Devices for implementing such a perceptual channel are described, and a practical application to a mini-invasive surgery tool is presented. Psychophysical test results are reported, validating the effectiveness and practicality of the proposed approach.

Many applications in teleoperation and virtual reality call for the implementation of effective means of displaying to the human operator information on the softness and other mechanical properties of objects being touched. The ability of humans to detect softness if different objects by tactual exploration is intimately related to both kinesthetic and cutaneous perception, and haptic displays should be designed so as to address such multimodal perceptual channel. In this paper we investigate the possibility of surrogating detailed tactile information for softness discrimination, with information on the rate of spread of the contact area between the finger and the specimen. Devices for implementing this new perceptual channel are described, and some preliminary psychophysical test results reported, validating the effectiveness and practicality of the proposed approach.

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network used to detect incipient slippage and on a skin-like sensor sensible to normal and shear stresses. Normal and shear stresses components inside the sensor are the input data of the neural net. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. To validate the method we worked on both simulated and experimental data. In the first case, the finite element method is used to solve the direct problem of elastic contact in its full nonlinearity by resorting to the lowest number of approximations regarding the real problem. Simulation has shown that the network learns and is robust to noise. Then an experimental test was carried out. Experimental results show that, in a simple case, the method is able to detect the insipiency of slippage between an object and the sensor.

Nowadays, the surgeon who is using minimally invasive tools loses almost completely the haptic perception of the manipulated tissue. In particular, he or she loses the perception of the tissue elastic properties. It is possible to modify the actual mini-invasive surgical tools in such a way that they may give a reliable estimation of the manipulated tissue properties for recognition and characterization purpose. In this paper we present a first attempt to realize a prototype of sensor-based surgical tool using a modified commercial tool. Experimental tests have shown that using such a tool could enhance surgeon's haptic perception of the manipulated tissue.

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multilayer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data.>

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network devoted to detecting incipient slippage of a body pressing on a skin-like sensor. Normal and shear stress components inside the sensor are the input data. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. The finite element method is used to solve the direct problem of elastic contact in its full non-linearity by resorting to the lowest number of approximations with respect to the real problem. Simulations show that the network learns and is robust with respect to noise.>

The authors report the implementation and testing of a neural network algorithm to solve the fine-form discrimination problem. The skin-like sensor design is briefly reviewed, and the fine-form discrimination task is stated as an inverse problem of contact mechanics. The inverse problem is discussed using a singular value decomposition argument, to enlighten the basic regularity assumptions that are needed in the inversion, and the class of shapes that it is possible to recover from the data. The network design is given, along with the information about training and testing sets and learning rate. Simulated experimental results are reported.>

Techniques and methodologies being developed to investigate tactile fine-form discrimination through machine perception are described. A tactile sensor array capable of resolving the six independent components of the stress tensor field is briefly described. Robust numerical solutions of inverse elastic contact problems, through discrete inverse operators, are reported.>

This paper describes a composite transducer for tactile sensing, whose skin-like structure makes it potentially useful in prosthetics as well as in robotics. Such transducer comprises an "epidermal", thin film, polyvinylidene fluoride (PVF2) sensor and two inner layers consisting of a conductive rubber sheet and of an array of 128 PVF2sensors, which are intended to reproduce in part some mechanical features and sensing capabilities of the human dermis. The proposed transducer has been tested and its ability to detect, by touch, hardness, thermal conductivity and surface texture of objects, as well as contact pressure has been assessed. Although much work is still needed to obtain a really usable device, sensitivity, linearity and bandwidth of the present tactile transducer, along with its wide range of sensorial capabilities, strongly encourage further development.