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The Relationships Between Behavioral Patterns and Emotions in Daily Life

机译:日常生活中行为模式与情绪的关系

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Emotions have been recognized from physiological and behavioral responses, however, in daily life these methods are less practical due to the measurement burden. This study was to minimize the measurement burden by using smartphones and to determine the behavioral patterns relevant to daily emotions through the global positioning system (GPS) locations. Seven participants (5 males) were asked to carry their smartphones and evaluate subjective emotions for six weeks. The participants' GPS locations were measured with their smartphones and then analyzed to determine their behavioral patterns. The emotions were categorized into valence and arousal dimensions, and the behavioral patterns were tested by the Kruskal-Wallis method. As a result, the valence dimension implied significant behavioral patterns such as location variance (p = .006), number of cluster (p = .015), and entropy (p = .044). The arousal dimension implied significant behavioral patterns such as location variance (p = .003), circadian movement (p = .008), and transition time (p = .016). These behavioral patterns are expected to be useful in recognizing emotions in daily life.
机译:然而,从生理和行为反应中得到了情绪,然而,在日常生活中,由于测量负担,这些方法不太实际。本研究是通过使用智能手机来最小化测量负担,并通过全球定位系统(GPS)位置确定与日常情绪相关的行为模式。 7名参与者(5名男性)被要求携带智能手机并评估主观情绪六周。与他们的智能手机测量参与者的GPS位置,然后分析以确定其行为模式。情绪分为价值和焦点尺寸,并通过Kruskal-Wallis方法测试行为模式。结果,价维暗示了显着的行为模式,例如位置方差(p = .006),簇数(p = .015)和熵(p = .044)。唤醒尺寸暗示了显着的行为模式,例如位置方差(P = .003),昼夜运动(p = .008)和转换时间(p = .016)。这些行为模式预计将有助于识别日常生活中的情绪。

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