Ubiquitous emotion-aware computing

Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people’s emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence--arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p < .001) of the participants’ experienced valence--arousal, with 88% for valence and 99% for arousal (ps < .001). The six basic emotions could also be discriminated (p < .001), although the explained variance was much lower: 18–20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people’s emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing

MIND-BODY RESPONSE AND NEUROPHYSIOLOGICAL CHANGES DURING STRESS AND MEDITATION: CENTRAL ROLE OF HOMEOSTASIS

Stress profoundly impacts quality of life and may lead to various diseases and conditions. Understanding the underlying physiological and neurological processes that take place during stress and meditation techniques may be critical for effectively treating stress-related diseases. The article examines a hypothetical physiological homeostatic response that compares and contrasts changes in central and peripheral oscillations during stress and meditation, and relates these to changes in the autonomic system and neurological activity. The authors discuss how cardiorespiratory synchronization, which occurs during the parasympathetic response and meditation, influences and modulates activity and oscillations of the brain and autonomic nervous system. Evidence is presented on how synchronization of cardiac and respiratory rates during meditation may lead to a homeostatic increase in cellular membrane potentials in neurons and other cells throughout the body. These potential membrane changes may underlie the reduced activity in the amygdala, and other cortical areas during meditation, and research examining these changes may foster better understanding of the restorative properties and health benefits of meditation

Viewing Nature Scenes Positively Affects Recovery of Autonomic Function Following Acute-Mental Stress

A randomized crossover study explored whether viewing different scenes prior to a stressor altered autonomic function during the recovery from the stressor. The two scenes were (a) nature (composed of trees, grass, fields) or (b) built (composed of man-made, urban scenes lacking natural characteristics) environments. Autonomic function was assessed using noninvasive techniques of heart rate variability; in particular, time domain analyses evaluated parasympathetic activity, using root-mean-square of successive differences (RMSSD). During stress, secondary cardiovascular markers (heart rate, systolic and diastolic blood pressure) showed significant increases from baseline which did not differ between the two viewing conditions. Parasympathetic activity, however, was significantly higher in recovery following the stressor in the viewing scenes of nature condition compared to viewing scenes depicting built environments (RMSSD; 50.0 ± 31.3 vs 34.8 ± 14.8 ms). Thus, viewing nature scenes prior to a stressor alters autonomic activity in the recovery period. The secondary aim was to examine autonomic function during viewing of the two scenes. Standard deviation of R-R intervals (SDRR), as change from baseline, during the first 5 min of viewing nature scenes was greater than during built scenes. Overall, this suggests that nature can elicit improvements in the recovery process following a stressor. © 2013 American Chemical Society

Neurophysiological investigations of drug resistant epilepsy patients treated with vagus nerve stimulation to differentiate responders from non-responders

Background and purpose In patients treated with vagus nerve stimulation (VNS) for drug resistant epilepsy (DRE), up to a third of patients will eventually not respond to the therapy. As VNS therapy requires surgery for device implantation, prediction of response prior to surgery is desirable. It is hypothesized that neurophysiological investigations related to the mechanisms of action of VNS may help to differentiate VNS responders from non-responders prior to the initiation of therapy. Methods In a prospective series of DRE patients, polysomnography, heart rate variability (HRV) and cognitive event related potentials were recorded. Polysomnography and HRV were repeated after 1 year of treatment with VNS. Polysomnography, HRV and cognitive event related potentials were compared between VNS responders (>= 50% reduction in seizure frequency) and non-responders. Results Fifteen out of 30 patients became VNS responders after 1 year of VNS treatment. Prior to treatment with VNS, the amount of deep sleep (NREM 3), the HRV high frequency (HF) power and the P3b amplitude were significantly different in responders compared to non-responders (P = 0.007; P = 0.001; P = 0.03). Conclusion Three neurophysiological parameters, NREM 3, HRV HF and P3b amplitude, were found to be significantly different in DRE patients who became responders to VNS treatment prior to initiation of their treatment with VNS. These non-invasive recordings may be used as characteristics for response in future studies and help avoid unsuccessful implantations. Mechanistically these findings may be related to changes in brain regions involved in the so-called vagal afferent network

Overnight weight loss: relationship with sleep structure and heart rate variability

Background: Weight loss can be caused by a loss of body mass due to metabolism and by water loss as unsensible water loss, sweating, or excretion in feces and urine. Although weight loss during sleep is a well-known phenomenon, it has not yet been studied in relation to sleep structure or autonomic tonus during sleep. Our study is proposed to be a first step in assessing the relationship between overnight weight loss, sleep structure, and HRV (heart rate variability) parameters.Methods: Twenty-five healthy volunteers received a 487 kcal meal and 200 ml water before experiment. Volunteers were weighed before and after polysomnography. Absolute and relative weight indices were calculated. Time and frequency domain analysis of heart rate variability was assessed during stages 2, 4, and REM. Nonparametric linear regression analysis was performed between night weight loss parameters, polysomnographic, and HRV ariables. Results: HF correlated positively with weight loss during stage 4. Slow wave sleep duration correlated positively with weight loss and weight loss rate. The duration of Stage 2 correlated negatively with absolute and relative weight loss. Conclusions: Weight loss during sleep is dependent upon sleep stage duration and sleep autonomic tonus. Slow-wave sleep and sleep parasympathetic tonus may be important for weight homeostasis

Biometrics for Emotion Detection (BED): Exploring the combination of Speech and ECG

The paradigm Biometrics for Emotion Detection (BED) is introduced, which enables unobtrusive emotion recognition, taking into account varying environments. It uses the electrocardiogram (ECG) and speech, as a powerful but rarely used combination to unravel people’s emotions. BED was applied in two environments (i.e., office and home-like) in which 40 people watched 6 film scenes. It is shown that both heart rate variability (derived from the ECG) and, when people’s gender is taken into account, the standard deviation of the fundamental frequency of speech indicate people’s experienced emotions. As such, these measures validate each other. Moreover, it is found that people’s environment can indeed of influence experienced emotions. These results indicate that BED might become an important paradigm for unobtrusive emotion detection

Addition of 24‐hour heart rate variability parameters to the Cardiovascular Health Study stroke risk score and prediction of incident stroke: The Cardiovascular Health Study

Background Heart rate variability (HRV) characterizes cardiac autonomic functioning. The association of HRV with stroke is uncertain. We examined whether 24‐hour HRV added predictive value to the Cardiovascular Health Study clinical stroke risk score (CHS‐SCORE), previously developed at the baseline examination. Methods and Results N=884 stroke‐free CHS participants (age 75.3±4.6), with 24‐hour Holters adequate for HRV analysis at the 1994–1995 examination, had 68 strokes over ≤8 year follow‐up (median 7.3 [interquartile range 7.1–7.6] years). The value of adding HRV to the CHS‐SCORE was assessed with stepwise Cox regression analysis. The CHS‐SCORE predicted incident stroke (HR=1.06 per unit increment, P=0.005). Two HRV parameters, decreased coefficient of variance of NN intervals (CV%, P=0.031) and decreased power law slope (SLOPE, P=0.033) also entered the model, but these did not significantly improve the c‐statistic (P=0.47). In a secondary analysis, dichotomization of CV% (LOWCV% ≤12.8%) was found to maximally stratify higher‐risk participants after adjustment for CHS‐SCORE. Similarly, dichotomizing SLOPE (LOWSLOPE <−1.4) maximally stratified higher‐risk participants. When these HRV categories were combined (eg, HIGHCV% with HIGHSLOPE), the c‐statistic for the model with the CHS‐SCORE and combined HRV categories was 0.68, significantly higher than 0.61 for the CHS‐SCORE alone (P=0.02). Conclusions In this sample of older adults, 2 HRV parameters, CV% and power law slope, emerged as significantly associated with incident stroke when added to a validated clinical risk score. After each parameter was dichotomized based on its optimal cut point in this sample, their composite significantly improved prediction of incident stroke during ≤8‐year follow‐up. These findings will require validation in separate, larger cohorts. Keywords: autonomic nervous system, clinical stroke risk model, heart rate variability, prediction, predictors, risk prediction, risk stratification, strok