A Fabric-based Approach for Softness Rendering

In this chapter we describe a softness display based on the contact area spread rate (CASR) paradigm. This device uses a stretchable fabric as a substrate that can be touched by users, while contact area is directly measured via an optical system. By varying the stretching state of the fabric, different stiffness values can be conveyed to users. We describe a first technological implementation of the display and compare its performance in rendering various levels of stiffness with the one exhibited by a pneumatic CASR-based device. Psychophysical experiments are reported and discussed. Afterwards, we present a new technological implementation for the fabric-based display, with reduced dimensions and faster actuation, which enables rapid changes in the fabric stretching state. These changes are mandatory to properly track typical force/area curves of real materials. System performance in mimicking force-area curves obtained from real objects exhibits a high degree of reliability, also in eliciting overall discriminable levels of softness

Robust Head Mounted Wearable Eye Tracking System for Dynamical Calibration

In this work, a new head mounted eye tracking system is presented. Based on computer vision techniques, the system integrates eye images and head movement, in real time, performing a robust gaze point tracking. Nystagmus movements due to vestibulo-ocular reflex are monitored and integrated. The system proposed here is a strongly improved version of a previous platform called HATCAM, which was robust against changes of illumination conditions. The new version, called HAT-Move, is equipped with accurate inertial motion unit to detect the head movement enabling eye gaze even in dynamical conditions. HAT-Move performance is investigated in a group of healthy subjects in both static and dynamic conditions, i.e. when head is kept still or free to move. Evaluation was performed in terms of amplitude of the angular error between the real coordinates of the fixed points and those computed by the system in two experimental setups, specifically, in laboratory settings and in a 3D virtual reality (VR) scenario. The achieved results showed that HAT-Move is able to achieve eye gaze angular error of about 1 degree along both horizontal and vertical direction

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

ComEDA: A new tool for stress assessment based on electrodermal activity

Non-specific sympathetic arousal responses to different stressful elicitations can be easily recognized from the analysis of physiological signals. However, neural patterns of sympathetic arousal during physical and mental fatigue are clearly not unitary. In the context of physiological monitoring through wearable and non-invasive devices, electrodermal activity (EDA) is the most effective and widely used marker of sympathetic activation. This study presents ComEDA, a novel approach for the characterization of complex dynamics of EDA. ComEDA overcomes the methodological limitations related to the application of nonlinear analysis to EDA dynamics, is not parameter-sensitive and is suitable for the analysis of ultra-short time series. We validated the proposed algorithm using synthetic series of white noise and 1/f noise, varying the number of samples from 50 to 5000. By applying our approach, we were able to discriminate a statistically significant increase of complexity in the 1/f noise with respect to white noise, obtaining p-values in the range [4.35 × 10−6, 0.03] after the Mann–Whitney test. Then, we tested ComEDA on both EDA signal and its tonic and phasic components, acquired from healthy subjects during four experimental protocols: two inducing a sympathetic activation through physical efforts and two based on mentally stressful tasks. Results are encouraging and promising, outperforming state of the art metrics such as the Sample Entropy. ComEDA shows good performance not only in discriminating between stressful tasks and resting state (p-value < 0.01 after the Wilcoxon non-parametric statistical test applied to EDA signals of all the four datasets), but also in differentiating different trends of complexity of EDA dynamics when induced by physical and mental stressors. These findings suggest future applications to automatically detect and selectively identify threats due to overwhelming stress impacting both physical and mental health or in the field of telemedicine to monitor autonomic diseases correlated to atypical sympathetic activation. The Matlab code implementing the ComEDA algorithm is available online

cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing

This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. EDA can be considered one of the most common observation channels of sympathetic nervous system activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization and sparsity. The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation. Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g. in the field of affective computing

Complexity index from a personalized wearable monitoring system for assessing remission in mental health

This study discusses a personalized wearable monitoring system, which provides information and communication technologies to patients with mental disorders and physicians managing such diseases. The system, hereinafter called the PSYCHE system, is mainly comprised of a comfortable t-shirt with embedded sensors, such as textile electrodes, to monitor electrocardiogram-heart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a smartphone-based interactive platform for electronic mood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions from a pathological mood state (HM, DP, or MX - where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients' clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders

Assessment of muscle fatigue during isometric contraction using autonomic nervous system correlates

Muscle fatigue is a complex phenomenon that results in a reduction of the maximal voluntary force. Measuring muscle fatigue can be a challenging task that may involve the use of intramuscular electrodes (i.e., intramuscular electromyography (EMG)) or complex acquisition techniques. In this study, we propose an alternative non-invasive methodology for muscle fatigue detection relying on the analysis of two autonomic nervous system (ANS) correlates, i.e., the electrodermal activity (EDA) and heart rate variability (HRV) series. Based on standard surface EMG analysis, we divided 32 healthy subjects performing isometric biceps contraction into two groups: a fatigued group and a non-fatigued group. EDA signals were analyzed using the recently proposed cvxEDA model in order to derive phasic and tonic components and extract effective features to study ANS dynamics. Furthermore, HRV series were processed to derive several features defined in the time and frequency domains able to estimate the cardiovascular autonomic regulation. A statistical comparison between the fatigued and the non-fatigued groups was performed for each ANS feature, and two EDA features, i.e., the tonic variability and the phasic response rate, showed significant differences. Moreover, a pattern recognition procedure was applied to the combined EDA-HRV feature-set to automatically discern between fatigued and non-fatigued subjects. The proposed SVM classifier, following a recursive feature elimination stage, exhibited a maximal balanced accuracy of 83.33%. Our results demonstrate that muscle fatigue could be identified in a non-invasive fashion through effective EDA and HRV processing