Background: Flow experience is a specific positive and affective state that occurs when humans are completely absorbed in an activity and forget everything else. This state can lead to high performance, well-being, and productivity at work. Few studies have been conducted to determine the human flow experience using physiological wearable sensor devices. Other studies rely on self-reported data. Methods: In this article, we use physiological data collected from 25 subjects with multimodal sensing devices - the Empatica E4 wristband, the Emotiv Epoc X electroencephalography (EEG) headset, and the Biosignalplux RespiBAN - in arithmetic and reading tasks to automatically discriminate between flow and non-flow states using feature engineering and deep feature learning approaches. The most meaningful wearable device for flow detection is determined by comparing the performances of each device. We also investigate the connection between emotions and flow by testing transfer learning techniques involving an emotion recognition-related task on the source domain. Results: The EEG sensor modalities yielded the best performances with an accuracy of 64.97%, and a macro Averaged F1 (AF1) score of 64.95%. An accuracy of 73.63% and an AF1 score of 72.70% were obtained after fusing all sensor modalities from all devices. Additionally, our proposed transfer learning approach using emotional arousal classification on the DEAP dataset led to an increase in performances with an accuracy of 75.10% and an AF1 score of 74.92%. Conclusion: The results of this study suggest that effective discrimination between flow and non-flow states is possible with multimodal sensor data. The success of transfer learning using the DEAP emotion dataset as a source domain indicates that emotions and flow are connected, and emotion recognition can be used as a latent task to enhance the performance of flow recognition.

This paper presents relationships between personality traits and stress levels in light of the transactional model of stress. The framework of the transactional model was applied to determine the significance of work with a robot for primary and secondary stress appraisal made by an individual. We decided to use the Big Five personality traits model as one which integrates the dimensions of personality and had been previously applied to research on stress. The participants in our three-wave study were 105 students doing an industrial robots programming course. Using Ten Item Personality Inventory (TIPI) and Questionnaire for Primary and Secondary Appraisal (PASA) questionnaires, we gathered information about the students’ personality, the level of anticipated stress, and the stress experienced while working with a robot after 6 and 12 weeks. The obtained results prove that emotional stability is significant for secondary appraisal of anticipated stress. The results also show that openness to experience is a negative predictor, whereas conscientiousness is a positive predictor of primary stress appraisal. The ability to cope with stress after 12 weeks of work with a robot is appraised as higher by older, more conscientious, and introverted people. The obtained results are discussed from the psychological perspective of stress and personality, which complements earlier studies in technical sciences. The limitations of the study are also indicated.

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%).

Over the past decade work engagement has gained both business and academia attention. With growing number of studies and meta-analyses the concept of work engagement is one of the pillars of positive work and organizational psychology. This systematic review presents the current state of research on work engagement in Poland. Results confirmed that work-engagement studies have not yet reached the threshold to conduct meta-analysis. The review of measurement methods and synthesis of findings allows to identify strengths and gaps in Polish studies. Discussion of limitations and biases in current research is accompanied with urge to overcome them and develop thriving stream of research on work engagement.