Flow is an affective state of optimal experience, total immersion and high productivity. While often associated with (professional) sports, it is a valuable information in several scenarios ranging from work environments to user experience evaluations, and we expect it to be a potential reward signal for human-in-the-loop reinforcement learning systems. Traditionally, flow has been assessed through questionnaires which prevents its use in online, real-time environments. In this work, we present our findings towards estimating a user's flow state based on physiological signals measured using wearable devices. We conducted a study with participants playing the game Tetris in varying difficulty levels, leading to boredom, stress, and flow. Using an end-to-end deep learning architecture, we achieve an accuracy of 67.50% in recognizing high flow vs. low flow states and 49.23% in distinguishing all three affective states boredom, flow, and stress.

We describe ferrocene-based N-heterocyclic germylenes and stannylenes of the type [Fe{(η -C H )NR} E:] (1 RE; E=Ge, Sn; R=neopentyl (Np), mesityl (Mes), trimethylsilyl (TMS)), which constitute the first examples of redox-functionalised N-heterocyclic tetrylenes (NHTs). These compounds are thermally stable and were structurally characterised by means of X-ray diffraction studies, except for the neopentyl-substituted stannylene 1 NpSn, the decomposition of which afforded the aminoiminoferrocene [fc(NHCH tBu)(N=CHtBu)] (2) and the spiro tin(IV) compound (1 Np) Sn (3). DFT calculations show that the HOMO of the NHTs of our study is localised on the ferrocenylene backbone. A one-electron oxidation process affords ions of the type 1 RE . In contrast to the NHC system 1 RC, the localised ferrocenium-type nature of the oxidised form does not compromise the fundamental tetrylene character of 1 RE .

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