Enzo Pasquale Scilingo

Wearable, redundant fabric-based sensor arrays for reconstruction of body segment posture

Posture and gesture analysis, together with the monitoring of body kinematics, is a field of increasing interest in bioengineering and several connected disciplines. In this paper, some typical features of distributed sensing systems are described, as well as a methodology to read signals from such systems. Theory, simulation, results, and some specific applications are shown. Strain gauges have been used as sensors and have been deposited directly onto textile fibers, demonstrating one way to realize a wearable sensor system.

Performance evaluation of sensing fabrics for monitoring physiological and biomechanical variables

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.

Strain-sensing fabrics for wearable kinaesthetic-like systems

In recent years, an innovative technology based on polymeric conductors and semiconductors has undergone rapid growth. These materials offer several advantages with respect to metals and inorganic conductors: lightness, large elasticity and resilience, resistance to corrosion, flexibility, impact strength, etc. These properties are suitable for implementing wearable devices. In particular, a sensitive glove able to detect the position and the motion of fingers and a sensorized leotard have been developed. Here, the characterization of the strain-sensing fabric is presented. In the first section, the polymerization process used to realize the strain sensor is described. Then, the thermal and mechanical transduction properties of the strain sensor are investigated and a geometrical parameter to invariantly codify the sensor response during aging is proposed. Finally, a brief outline of ongoing applications is reported.

Strain sensing fabric for hand posture and gesture monitoring

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.

Haptic discrimination of softness in teleoperation: the role of the contact area spread rate

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.

The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition

This paper reports on a new methodology for the automatic assessment of emotional responses. More specifically, emotions are elicited in agreement with a bidimensional spatial localization of affective states, that is, arousal and valence dimensions. A dedicated experimental protocol was designed and realized where specific affective states are suitably induced while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), ElectroDermal Response (EDR), and ReSPiration activity (RSP), are simultaneously acquired. A group of 35 volunteers was presented with sets of images gathered from the International Affective Picture System (IAPS) having five levels of arousal and five levels of valence, including a neutral reference level in both. Standard methods as well as nonlinear dynamic techniques were used to extract sets of features from the collected signals. The goal of this paper is to implement an automatic multiclass arousal/valence classifier comparing performance when extracted features from nonlinear methods are used as an alternative to standard features. Results show that, when nonlinearly extracted features are used, the percentages of successful recognition dramatically increase. A good recognition accuracy (>;90 percent) after 40-fold cross-validation steps for both arousal and valence classes was achieved by using the Quadratic Discriminant Classifier (QDC).

Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis.

Current clinical practice in diagnosing patients affected by psychiatric disorders such as bipolar disorder is based only on verbal interviews and scores from specific questionnaires, and no reliable and objective psycho-physiological markers are taken into account. In this paper, we propose to use a wearable system based on a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire electrocardiogram, respirogram, and body posture information in order to detect a pattern of objective physiological parameters to support diagnosis. Moreover, we implemented a novel ad hoc methodology of advanced biosignal processing able to effectively recognize four possible clinical mood states in bipolar patients (i.e., depression, mixed state, hypomania, and euthymia) continuously monitored up to 18 h, using heart rate variability information exclusively. Mood assessment is intended as an intrasubject evaluation in which the patients states are modeled as a Markov chain, i.e., in the time domain, each mood state refers to the previous one. As validation, eight bipolar patients were monitored collecting and analyzing more than 400 h of autonomic and cardiovascular activity. Experimental results demonstrate that our novel concept of personalized and pervasive monitoring constitutes a viable and robust clinical decision support system for bipolar disorders recognizing mood states with a total classification accuracy up to 95.81%.

Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

Mood recognition in bipolar patients through the PSYCHE platform: Preliminary evaluations and perspectives

BACKGROUND Bipolar disorders are characterized by a series of both depressive and manic or hypomanic episodes. Although common and expensive to treat, the clinical assessment of bipolar disorder is still ill-defined. OBJECTIVE In the current literature several correlations between mood disorders and dysfunctions involving the autonomic nervous system (ANS) can be found. The objective of this work is to develop a novel mood recognition system based on a pervasive, wearable and personalized monitoring system using ANS-related biosignals. MATERIALS AND METHODS The monitoring platform used in this study is the core sensing system of the personalized monitoring systems for care in mental health (PSYCHE) European project. It is comprised of a comfortable sensorized t-shirt that can acquire the inter-beat interval time series, the heart rate, and the respiratory dynamics for long-term monitoring during the day and overnight. In this study, three bipolar patients were followed for a period of 90 days during which up to six monitoring sessions and psychophysical evaluations were performed for each patient. Specific signal processing techniques and artificial intelligence algorithms were applied to analyze more than 120 h of data. RESULTS Experimental results are expressed in terms of confusion matrices and an exhaustive descriptive statistics of the most relevant features is reported as well. A classification accuracy of about 97% is achieved for the intra-subject analysis. Such an accuracy was found in distinguishing relatively good affective balance state (euthymia) from severe clinical states (severe depression and mixed state) and is lower in distinguishing euthymia from the milder states (accuracy up to 88%). CONCLUSIONS The PSYCHE platform could provide a viable decision support system in order to improve mood assessment in patient care. Evidences about the correlation between mood disorders and ANS dysfunctions were found and the obtained results are promising for an effective biosignal-based mood recognition.

Rendering Softness: Integration of Kinesthetic and Cutaneous Information in a Haptic Device

While it is known that softness discrimination relies on both kinesthetic and cutaneous information, relatively little work has been done on the realization of haptic devices replicating the two cues in an integrated and effective way. In this paper, we first discuss the ambiguities that arise in unimodal touch, and provide a simple intuitive explanation in terms of basic contact mechanics. With this as a motivation, we discuss the implementation and control of an integrated device, where a conventional kinesthetic haptic display is combined with a cutaneous softness display. We investigate the effectiveness of the integrated display via a number of psychophysical tests and compare the subjective perception of softness with that obtained by direct touch on physical objects. Results show that the subjects interacting with the integrated haptic display are able to discriminate softness better than with either a purely kinesthetic or a purely cutaneous display.