Comparative Evaluation of the Autonomic Response to Cognitive and Sensory Stimulations through Wearable Sensors

Chemosensory analysis of emotional wines: Merging of explicit and implicit methods to measure emotions aroused by red wines

Wine is a cultural product capable of arousing emotions. Measuring emotions and figuring out how much they could influence preferences or purchase decisions of consumers is a new trend in sensory and consumer research. However, the complexity of feelings makes the measure of emotions extremely challenging. Thus, a comprehensive understanding of emotions related to sensory stimuli in wine tasting is still missing. The purpose of the study is to evaluate the emotional power of tasting red wines using a multidisciplinary approach, combining sensory analysis performed by trained panelists, implicit and explicit measurements of emotions and chemical analysis of the wines tasted. Various red wines, renowned for their high polyphenol content and expected to exhibit rich texture, mouthfeel, and aging potential, have been utilized to this extent. The results obtained showed that the emotions measured were well-correlated with quantitative and hedonic attributes obtained using classic sensory analysis. Some aromatic molecules can be identified as markers capable of eliciting positive and negative emotional reactions. While increasing literature in the topic is recently available, our study appears to be the first highlighting the presence of autonomic nervous system (ANS) differences verified by means of electrocardiogram (ECG) features, related to explicit and complete sensory patterns, in response to sensory stimuli related to emotional wine, with higher sympathetic values at extrema and vagal increase in the presence of neutral sensory compounds.

PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.

Wearable Sensors to Evaluate Autonomic Response to Olfactory Stimulation: The Influence of Short, Intensive Sensory Training

In the last few decades, while the sensory evaluation of edible products has been leveraged to make strategic decisions about many domains, the traditional descriptive analysis performed by a skilled sensory panel has been seen to be too complex and time-consuming for the industry needs, making it largely unsustainable in most cases. In this context, the study of the effectiveness of different methods for sensory training on panel performances represents a new trend in research activity. With this purpose, wearable sensors are applied to study physiological signals (ECG and skin conductance) concerned with the emotions in a cohort of volunteers undergoing a short, two-day (16 h) sensory training period related to wine tasting. The results were compared with a previous study based on a conventional three-month (65 h) period of sensory training. According to what was previously reported for long panel training, it was seen that even short, intensive sensory training modulated the ANS activity toward a less sympathetically mediated response as soon as odorous compounds become familiar. A large-scale application of shorter formative courses in this domain appears possible without reducing the effectiveness of the training, thus leading to money saving for academia and scientific societies, and challenging dropout rates that might affect longer courses.

A scoping review on monitoring mental health using smart wearable devices

With the continuous development of the times, social competition is becoming increasingly fierce, people are facing enormous pressure and mental health problems have become common. Long-term and persistent mental health problems can lead to severe mental disorders and even death in individuals. The real-time and accurate prediction of individual mental health has become an effective method to prevent the occurrence of mental health disorders. In recent years, smart wearable devices have been widely used for monitoring mental health and have played an important role. This paper provides a comprehensive review of the application fields, application mechanisms, common signals, common techniques and results of smart wearable devices for the detection of mental health problems, aiming to achieve more efficient and accurate prediction for individual mental health, and to achieve early identification, early prevention and early intervention to provide a reference for improving the level of individual mental health.

Gesunde erhöhen unter Fischgeruch die Herzfrequenz nur schwach und nur einen Variabilitätsparameter

Einleitung Kardiale Riechreizfolgereaktionen könnten die Palette objektivierender Testmethoden zur Überprüfung des Riechvermögens erweitern. In einer Pilotstudie wurde untersucht, ob und wie durch angenehme und unangenehme Gerüche die Herzfrequenz (HR) und deren Variabilität verändert werden.

Methodik 32 Gesunde mit normalem Riechvermögen atmeten nasal 7 Minuten den Duft von 4 Sniffin’ Sticks (Banane, Fisch, Knoblauch und Rose) im Wechsel mit Leer-Reizen ein. Gleichzeitig wurden das EKG registriert und offline die NN-Intervalle (Abstand der R-Zacken) und 6 Parameter der Herzfrequenzvariabilität bestimmt und mit den Werten einer Leer-Reizung mittels generalisierter Schätzgleichungen (GEE) verglichen. Zusätzlich wurden die Werte der subjektiven Einschätzung von Intensität und hedonischer Valenz der 4 Gerüche mit der Herzfrequenz korreliert.

Ergebnisse Der unangenehme Fischgeruch steigerte in der ersten Reiz-Minute gering, aber signifikant die Herzfrequenz (HR). Außerdem vergrößerte er während des Dauerreizes lediglich einen Variabilitätsparameter (LF/HF) und zeigte in der ersten Reiz-Minute eine signifikante moderate Korrelation zwischen der HR und der Skalierung von Intensität bzw. der Hedonik. Die angenehmen Gerüche und der Knoblauchgeruch hatten keinen Einfluss auf die analysierten kardialen Parameter.

Schlussfolgerungen Olfaktorisch ausgelöste HR-kardiale Riechreizfolgereaktionen sind es wert, als Indikator für die Intaktheit des Riechvermögens weiter untersucht zu werden. Am erfolgversprechendsten und zeit- und kostengünstigsten ist dabei wahrscheinlich eine 1-minütige Reizung mit dem unangenehmen Fischgeruch aus dem Identifikationsset der Sniffin’ Sticks bei gleichzeitiger EKG-Ableitung.

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Wearable Sensors for Assessing the Role of Olfactory Training on the Autonomic Response to Olfactory Stimulation

Wearable sensors are nowadays largely employed to assess physiological signals derived from the human body without representing a burden in terms of obtrusiveness. One of the most intriguing fields of application for such systems include the assessment of physiological responses to sensory stimuli. In this specific regard, it is not yet known which are the main psychophysiological drivers of olfactory-related pleasantness, as the current literature has demonstrated the relationship between odor familiarity and odor valence, but has not clarified the consequentiality between the two domains. Here, we enrolled a group of university students to whom olfactory training lasting 3 months was administered. Thanks to the analysis of electrocardiogram (ECG) and galvanic skin response (GSR) signals at the beginning and at the end of the training period, we observed different autonomic responses, with higher parasympathetically-mediated response at the end of the period with respect to the first evaluation. This possibly suggests that an increased familiarity to the proposed stimuli would lead to a higher tendency towards relaxation. Such results could suggest potential applications to other domains, including personalized treatments based on odors and foods in neuropsychiatric and eating disorders.

Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.