{'fraction_non_alphanumeric': 0.030303030303030304, 'fraction_numerical': 0.0, 'mean_word_length': 5.836206896551724, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
COLLABORATIVE CAPTURING AND DETECTION OF HUMAN INTERACTIONS IN MEETINGS

This paper proposes a collaborative approach to capture and detect human interactions in meetings by employing multiple sensors, such as video cameras, microphones, and motion sensors. Different from physical interactions (e.g. turn-taking and addressing), the human interactions here are incorporated with semantics, i.e. user intention or attitude towards a topic. The contexts used for interaction detection include head gesture, attention from others, speech tone, speaking time, interaction occasion, and information about previous interaction. The support vector machines (SVM) classifier is adopted to recognize human interactions based on these features. The experimental results verified the proposed approach.