This paper focuses on a sleep cycle, and improves the problem which an estimation accuracy of Real-Time Sleep Stage Estimation Method(RSSE) when it estimates a sleep stage on real time. Concretely, the proposed method re-estimates the sleep stage immediately after first sleep cycle since going to bed for the problem which decreases the correct rate of the sleep stage estimated by RSSE as time passes since going to bed. From the human subject experiments, the following implications have been revealed: (1) the correct rate improved by re-estimation in 8 cases out of 9 cases. (2) when the sleep cycle is long, it is possible to calculate the sleep cycle from the same subject's past sleeping information and if it is used, the estimation accuracy is improved for all cases.

This position paper describes an AI application for game spectators, e.g., those watching Twitch. The aim of this application is to automatically generate game plays by nonplayer characters -- not human players -- and recommend those plays to spectators. The generation part leads to development of a new field: procedural play generation (PPG). The recommendation part requires new techniques in recommender systems (RS) for incorporation of play content into RS to obtain promising recommendation results. Rather than proposing solutions to all relevant topics, this paper aims at drawing attention to this new field and serves as a seed for discussion and collaboration among the readers, workshop participants, and authors.

This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in real-time, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.