Exploring Flow Psychophysiology in Knowledge Work

Knierim, Michael Thomas; Rissler, Raphael; Hariharan, Anuja; Nadj, Mario; Weinhardt, Christof

doi:10.1007/978-3-030-01087-4_29

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…In their study, Knierim et al (2019) explored the experience of flow in arithmetic (boredom, flow, overload) and scientific writing tasks. They analyzed RMSSD (lower values imply higher stress) and HF features of HRV.…”

Section: Background and Related Workmentioning

confidence: 99%

Flow in human-robot collaboration—multimodal analysis and perceived challenge detection in industrial scenarios

Prajod,

Lavit Nicora,

Mondellini

et al. 2024

Front. Robot. AI

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Introduction: Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However, its occurrence in industrial settings has remained relatively unexplored. Notably, the literature predominantly focuses on Flow within mentally demanding tasks, which differ significantly from industrial tasks. Consequently, our understanding of emotional and physiological responses to varying challenge levels, specifically in the context of industry-like tasks, remains limited.Methods: To bridge this gap, we investigate how facial emotion estimation (valence, arousal) and Heart Rate Variability (HRV) features vary with the perceived challenge levels during industrial assembly tasks. Our study involves an assembly scenario that simulates an industrial human-robot collaboration task with three distinct challenge levels. As part of our study, we collected video, electrocardiogram (ECG), and NASA-TLX questionnaire data from 37 participants.Results: Our results demonstrate a significant difference in mean arousal and heart rate between the low-challenge (Boredom) condition and the other conditions. We also found a noticeable trend-level difference in mean heart rate between the adaptive (Flow) and high-challenge (Anxiety) conditions. Similar differences were also observed in a few other temporal HRV features like Mean NN and Triangular index. Considering the characteristics of typical industrial assembly tasks, we aim to facilitate Flow by detecting and balancing the perceived challenge levels. Leveraging our analysis results, we developed an HRV-based machine learning model for discerning perceived challenge levels, distinguishing between low and higher-challenge conditions.Discussion: This work deepens our understanding of emotional and physiological responses to perceived challenge levels in industrial contexts and provides valuable insights for the design of adaptive work environments.

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Section: Background and Related Workmentioning

confidence: 99%

Flow in human-robot collaboration—multimodal analysis and perceived challenge detection in industrial scenarios

Prajod,

Lavit Nicora,

Mondellini

et al. 2024

Front. Robot. AI

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“…Case studies were selected based on several similarities and differences in relation to the agrienvironmental governance systems. As EU Member States, these countries have shared priorities in relation to achieving EU agri-environmental targets but exhibit different levels of implementation and AKIS integration (Knierim and Prager, 2015). The context specific nature of networks means that the social networks are reflective of these local networks but not necessarily representative of the national pictures.…”

Section: Selection and Overview Of Case Studiesmentioning

confidence: 99%

Functional land management for a sustainable land base in the EU

O’Sullivan¹

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Discussions on soil policies are framed by the Thematic Strategy for Soil Protection (COM (2006)231 final), launched by the European Commission (EC) in 2006. Eight main threats to soil were recognised within the EU. A proposal for a Soil Framework Directive (SFD) (COM (2006)232 final) was finally withdrawn in May 2014. The concept of Functional Land Management (FLM) has been developed as a utilitarian framework that specifically tries to optimise the delivery of five soil functions delivered through agricultural landscapes: 1) primary productivity, 2) water purification and regulation, 3) carbon storage and regulation, 4) provision of a habitat for biodiversity and 5) cycling of nutrients. Supply of soil functions is governed by the complex interaction of the inherent capacity of soil, the environment and the management. Within the FLM framework, demand is framed by agri-environmental policies which are considered a proxy for the societal demands on land. A case study of Ireland is presented to show the applicability of this framework. In relation to matching the supply of soil functions with demand, three distinguishing pathways have been identified: 1) Implement a change in a dynamic soil property at local scale, such as nutrient concentrations. 2) Alter a static soil property to enhance one or more functions. In Ireland, this could be the implementation of subsurface land drainage systems to change the soil moisture dynamics of the soil. 3) Land use change, such as from grassland to arable. Functional Land Management does not equate to a legislative tool for the zoning of land for individual functions but rather that individual soil functions may be incentivised through existing instruments available under the EU CAP.

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