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    Audio-driven human body motion analysis and synthesis
    (IEEE, 2008) Canton-Ferrer, C.; Tilmanne, J.; Bozkurt, E.; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Ofli, Ferda; Demir, Yasemin; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 107907; 34503; 26207
    This paper presents a framework for audio-driven human body motion analysis and synthesis. We address the problem in the context of a dance performance, where gestures and movements of the dancer are mainly driven by a musical piece and characterized by the repetition of a set of dance figures. The system is trained in a supervised manner using the multiview video recordings of the dancer. The human body posture is extracted from multiview video information without any human intervention using a novel marker-based algorithm based on annealing particle filtering. Audio is analyzed to extract beat and tempo information. The joint analysis of audio and motion features provides a correlation model that is then used to animate a dancing avatar when driven with any musical piece of the same genre. Results are provided showing the effectiveness of the proposed algorithm.
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    Obsessive-compulsive tendencies and action sequence complexity: an information theory analysis
    (Springer, 2019) Zeki, Mustafa; Moustafa, Ahmed A.; Department of Psychology; Department of Psychology; Balcı, Fuat; Öztel, Tutku; Faculty Member; Teaching Faculty; Department of Psychology; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Social Sciences and Humanities; College of Social Sciences and Humanities; 51269; N/A
    Obsessive-compulsive disorder (OCD) is a psychiatric condition that is primarily associated with anxiety provoking repetitive thoughts (i.e., obsessions) and actions that are manifested to neutralize the resultant anxiety (i.e., compulsions). Interestingly, OCD patients continue compulsive behaviors (e.g., repeatedly rechecking if the door is locked) although they are typically aware of the irrationality of these behaviors. This suggests that compulsive behaviors have habit-like features. We predicted that the motor actions (e.g., sequence of goalless key presses) would deviate from randomness in individuals with stronger obsessive-compulsive (OC) tendencies and thus expected to observe more rigid sequential action patterns in these individuals (e.g., pressing keys according to a motif). We applied entropy theory approach, defined as the rate of change of information in a given sequence, to test this hypothesis. We collected two different types of sequential behavioral data from healthy individuals and scored their obsessive-compulsive tendencies based on the Padua Inventory. In the first method, we asked participants to press one of the two buttons sequentially. In the second method, participants were asked to mark one of the four different options sequentially (on a multiple-choice optic form). The behavioral characterization was carried out by quantifying the entropy in the sequence of two sets of behavioral data using the Shannon metric entropy and Lempel-Ziv complexity measures. Our results revealed a negative relationship between the degree of washing tendencies and the level of information contained in action sequences. These results held only for the data collected with key presses and not for the choice sequences in the paper-pencil task. Based on these results, we conclude that the behavioral rigidity observed in the form of compulsive actions may generalize to some other behaviors of the individual.