Inhomogeneous Point-Processes to Instantaneously Assess Affective Haptic Perception through Heartbeat Dynamics Information

The Role of Haptic Stimuli on Affective Reading: a Pilot Study

The affective role of touch has opened new perspectives in human-machine interaction. This paper presents an emotion recognition algorithm to investigate the role of tactile stimuli conveyed through a wearable haptic system during affective reading. To this end, a group of 32 healthy volunteers underwent an emotional stimulation by reading affective texts, with and without the concurrent presence of pleasant haptic stimuli. Throughout the experiment, autonomic nervous system dynamics was quantified through heart rate variability (HRV) and electrodermal activity (EDA) analyses. EDA and HRV features were then used as input of a SVM-RFE learning algorithm for an automatic recognition of neutral and arousing texts. The affective recognition of the reading was performed in the presence or absence of the haptic stimulation. Results show that the affective perception induced by the neutral and arousing reading were discriminated with a significantly improved accuracy (+14.5%) when a caress-like haptic stimulus was conveyed to the user.

Brain Dynamics Induced by Pleasant/Unpleasant Tactile Stimuli Conveyed by Different Fabrics

In this study, we investigated brain dynamics from electroencephalographic (EEG) signals during affective tactile stimulation conveyed by the dynamical contact with different fabrics. Thirty-three healthy subjects (16 females) were enrolled to interact with a haptic device able to mimic caress-like stimuli conveyed by strips of different fabrics moved back and forth at different velocities. Specifically, two velocity levels (i.e., 9.4 and 65 mm/sec) and two kinds of fabric (i.e., burlap and silk) were selected to deliver pleasant and unpleasant affective elicitations, according to subjects' self-assessment. EEG power spectra and functional connectivity were then calculated and analyzed. Experimental results, reported in terms of p-value topographic maps, demonstrated that caresses administered through unpleasant fabrics increased brain activity in the θ (4-8 Hz), α (8-14 Hz), and β (14-30 Hz) bands, whereas the use of pleasant fabrics enhanced functional connections in specific areas (e.g., frontal, occipital, and temporal cortices) depending on the oscillations frequency and caressing velocity. Furthermore, we adopted K-NN algorithms to automatically recognize the pleasantness of the haptic stimulation at a single-subject level using EEG power spectra, achieving a recognition accuracy up to 74.24%. Finally, we showed how brain oscillation power in the α and β bands over contralateral frontal- and central-cortex were the most informative features characterizing the pleasantness of a tactile stimulus on the forearm.

Investigation of Lagged Poincaré Plot reliability in ultra-short synthetic and experimental Heart Rate Variability series

This study reports on the reliability of Lagged Poincaré Plot (LPP) parameters calculated from ultra-short cardiovascular time series (from 30 to 180 seconds). ity (HRV) signals, whereas a few studies have studied nonlinear approaches. Particularly, methods derived from the phase-space theory, especially the ones employing multi-lag analyses, are usually considered to be inaccurate with a low number of samples. Here we propose a comprehensive study about LPP, using both synthetic and real RR series. Specifically, we considered 109 5-minutes HRV series: 60 synthetic series generated through the Integral Pulse Frequency Modulation (IPFM) model and 49 experimental series acquired from healthy subjects during resting-state. Three parameters have been extracted through the ellipse-fitting method, SD1, SD2 and S, using ten values of lag. All LPP parameters were estimated by averaging estimates gathered from segments of 30, 120 and 180 seconds, and compared with the once from 5-minute series. Results showed Spearman's correlation coefficients higher than 0.9 in both synthetic and real series. In conclusion, SD1 gave promising results in terms of percentage absolute error, when it was extracted from series with a duration less than three minutes.

Heart rate variability analysis during muscle fatigue due to prolonged isometric contraction

Fatigue can be defined as the muscular condition occurring before the inability to perform a task. It can be assessed through the evaluation of the median and mean frequency of the spectrum of the surface electromyography series. Previous studies investigated the relationship between heartbeat dynamics and muscular activity. However, exploitation of such cardiovascular measures to automatically identify muscle fatigue during fatiguing exercises is still missing. To this extent, HRV signals were gathered from 32 subjects during an isometric contraction task, and features defined in the time, frequency and nonlinear domains were investigated. We used surface electromyography to label the occurrence of muscle fatigue. Statistically significant differences were observed by comparing features related to fatigued subjects with the non-fatigued ones. Moreover, a pattern recognition system capable to achieve an average accuracy of 78.24% was implemented. These results confirmed the hypothesis that a relationship between heartbeat dynamics and muscle fatigue might exist.

Characterization of doctor-patient communication using heartbeat nonlinear dynamics: A preliminary study using Lagged Poincaré Plots

Emphatic doctor-patient communication has been associated with an improved psycho-physiological well-being involving cardiovascular and neuroendocrine responses. Nevertheless, a comprehensive assessment of heartbeat linear and nonlinear/complex dynamics throughout the communication of a life-threatening disease has not been performed yet. To this extent, we here study heart rate variability (HRV) series gathered from 17 subjects while watching a video where an oncologist discloses the diagnosis of a cancer metastasis to a patient. Further 17 subjects watched the same video including additional affective emphatic contents. For the assessment of the two groups, linear heartbeat dynamics was quantified through measures defined in the time and frequency domains, whereas nonlinear/complex dynamics referred to measures of entropy, and combined Lagged Poincare Plots (LPP) and symbolic analyses. Considering differences between the beginning and the end of the video, results from non-parametric statistical tests demonstrated that the group watching emphatic contents showed HRV changes in the LF/HF ratio exclusively. Conversely, the group watching the purely informative video showed changes in vagal activity (i.e., HF power), LF/HF ratio, as well as LPP measures. Additionally, a Support Vector Machine algorithm including HRV nonlinear/complex information was able to automatically discern between groups with an accuracy of 76.47%. We therefore propose the use of heartbeat nonlinear/complex dynamics to objectively assess the empathy level of healthy women.

Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics

OBJECTIVE

Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions.

METHODS

We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov-Smirnov distance.

RESULTS AND CONCLUSION

Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling.

SIGNIFICANCE

This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain-heart or, more in general, brain-body interactions).

A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.