In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two ...different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.
In this paper we explore if physiological signals obtained from a person through wearable sensors permit the correct interpretation of human affective states. A crucial aspect within this field of ...research is the capability of inducing
real-life
emotions in laboratory environments. To this end we designed a very strict and regulated experimental protocol. We consider two affective states: a relaxation state and a stressful one. To induce these two states we adopt audio tracks of natural or day-life sounds and math calculations. Moreover, to study different intensities of effectively induced relaxation, we consider two different audio players: a traditional pair of headphones and the Spherison Sound6D pillowⒸ, a special device that provides a complete spherical immersion in what users are listening to. We consider as physiological signals the Galvanic Skin Response (GSR) and the Photoplethysmography (PPG), as they are sensitive measures for emotional arousal. After an extensive analysis on our experimental data, we demonstrate that GSR and PPG signals can successfully distinguish relaxation and stressful states. Moreover, the same physiological signals can discriminate affective state intensity, especially when relaxation is induced adopting the Sound6D technology.
Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning ...techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.
•We theoretically describe the expert systems.•We investigate the fuzzy, medical and wearable expert system variations.•We highlight the expert systems advantages and issues.•We emphasize the ...importance of expert system validation.•We describe in depth some expert system applications in the medical field.
The aim of this review is to provide a broad overview of the state-of-the-art works mainly published in the last ten years on expert systems applied in different medical domains.
Being able to support and sometimes substitute experts, an expert system may be a precious ally for medical diagnoses. Medical expert system applications provide physicians and patients with an immediate access to knowledge and advice, rooting their flexibility into their knowledge bases, rule sets and graphical interfaces. To be trusted by their users, medical expert systems should follow some criteria, which we investigate along with their different realization, from fuzzy logic to wearable solutions for out-of-clinical-environment care. We also consider the advantages of approaching diagnoses and alert systems through an artificial intelligence counterpart, without forgetting the importance of a good validation to assess the system functionality.
Therefore, we show the heterogeneity of the solutions proposed by the literature, bounded to the specific needs a medical expert system is called to answer, the common lack of a system validation and the possible benefits deriving from these systems application.
The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. Pedestrian ...behaviour involves human perception processes, based on subjective and psychological aspects. Following the concept of pedestrian environmental awareness, each walker adapts his/her crossing behaviour according to environmental conditions and his/her perception of safety. Different pedestrian behaviours can be related to subjective mobility and readiness to respond, and these factors are strongly dependent on the subjective interaction with the environment. Having additional inputs about pedestrian behaviour related to their perception processes could be useful in order to develop a more representative pedestrian dynamic model. In particular, the subjective perception of the safeness of crossing should be taken into consideration. In order to focus on the pedestrians’ perception of safe road crossing and walking, an experiment in an uncontrolled urban scenario has been carried out. Besides more conventional self-assessment questionnaires, physiological responses have been considered to evaluate the affective state of pedestrians during the interaction with the urban environment. Results from the analysis of the collected data show that physiological responses are reliable indicators of safety perception while road crossing and interacting with real urban environment, suggesting the design of agent-based models for pedestrian dynamics simulations taking in account the representation of affective states.
Cellular Automata have successfully been successfully applied to the modeling and simulation of pedestrian and crowd dynamics. In particular, the investigated scenarios have often been focused on the ...evaluation of medium–high population density situations, in which the motivation of pedestrians to reach a certain location overcomes their tendency to naturally respect proxemic distances. The global COVID-19 outbreak, though, has shown that sometimes it is crucial to contemplate how proxemic tendencies are emphasized and amplified by the affective state of the individuals involved in the scenario, representing an important factor to take into consideration when investigating the behaviour of a crowd. In this paper we present a research effort aimed at integrating results of quantitative analyses regarding the effects of affective states on the perception of distances maintained by different types of pedestrians with the modeling of pedestrian movement choices in a cellular automata framework.
Physiological responses are nowadays widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning ...techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity is considered in the case of PhotoPlethysmoGraphy (PPG), which is successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is here proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject's heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization is evaluated in comparison with other normalization procedures in a binary classification task, where cognitive load and relaxing state are considered. The results obtained on two different datasets available in the literature confirm that applying the proposed normalization strategy permits to increase classification performance.
