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Body-controlled trampoline training games based on computer vision., , , , and . CHI Extended Abstracts, page 1143-1148. ACM, (2013)Martial arts in artificial reality., , , , and . CHI, page 781-790. ACM, (2005)Predicting Game Engagement and Difficulty Using AI Players., , , , , and . CoRR, (2021)Automatic Recognition of Playful Physical Activity Opportunities of the Urban Environment., , , and . MindTrek, page 49-59. ACM, (2021)Recognizing Emotional Expression in Game Streams., , , , and . CHI PLAY, page 301-311. ACM, (2019)Learning High-Risk High-Precision Motion Control., , and . MIG, page 9:1-9:10. ACM, (2022)Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement Learning., , , , , and . CHI, page 1-13. ACM, (2020)Novel applications of real-time audiovisual signal processing technology for art and sports education and entertainment ; Uusia reaaliaikaiseen kuvan- ja äänenkäsittelyteknologiaan perustuvia järjestelmiä taiteen ja liikunnan harjoitteluun.. Aalto University, Espoo, Finland, (2007)base-search.net (ftaaltouniv:oai:aaltodoc.aalto.fi:123456789/2854).Understanding the Design of Emotionally Impactful Game Feel., , , and . CHI PLAY (Companion), page 24-30. ACM, (2024)PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation., , , and . MLSP, page 1-6. IEEE, (2020)