I-struve: Automatic linguistic descriptions of visual double stars

Dynamic linguistic descriptions of time series applied to self-track the physical activity

“…Finally, there are some recent works like [22], where authors introduced a new approach to linguistic summarization of time series based on the use of a fuzzy hierarchical partition of the time dimension and the evaluation of quantified sentences. In previous works, we have generated assessing reports in truck driving simulators [23], reports about traffic evolution in roads [24], about the relevant features of the Mars' surface [25] and linguistic descriptions of visual double stars [26]. In addition, we have worked with accelerometer data by automatically generating linguistic reports about human gait quality [27], gesture recognition [28] and activity recognition [29,30].…”

Interpretability of fuzzy linguistic summaries

“…Moreover, it has been shown that information displayed as text to the user is interpreted more swiftly compared to graphs [6]. Finally, a linguistic summary can be read out by a text-to-speech synthesis system when the visual attention must not be disturbed, while executing a complex task for instance [7], or when it is deficient.…”

Proceedings of the 10th International Conference on Natural Language Generation

“…For example: describing big data (Conde-Clemente et al, 2017b); advising how to save energy at home (CondeClemente et al, 2016); describing physical activity (Sanchez-Valdes et al, 2016); describing drivers' behavior in driving simulations (Eciolaza et al, 2013); or describing double stars in astronomy (Arguelles and Trivino, 2013). Figure 1 depicts the LDCP architecture for Natural Language Generation in Data-to-text applications (NLG/D2T).…”