Analysis of Clothing Features Improving Self-Esteem through Measuring Stress According to Activity Contexts

Abstract: In general, it is known that people's mental moods can be affected by changing their clothes.Though low self-esteem and mental disorders related to it have become a social problem in recent years, a simple way to change clothes may contribute to improving depressive symptoms. This study targets to improve self-esteem by changing clothing.There have already been a lot of studies to improve self-esteem. They include the use of social networking sites focusing on praise and the development of interactive technologies to improve young people's self-esteem. However, no method has been proposed to objectively judge whether people spend a daily life with themselves satisfied.Stress values are constantly changing. Their means and variances vary with activities. It implies there is a suitable stress value for each of them. We want to engage in some activities initiatively and are obliged to do others. The former has intrinsic stress while the latter an extrinsic. Even in the same activity, some people want to do it under high tension, while others want to do it in a relaxed manner. The paper introduces activity contexts classified with 2 dimensions related to stress. One dimension shows whether they are intrinsic or extrinsic, while the other presents whether the stress value is appropriate. The paper proposes an experimental method to discover the features of clothing that can improve self-esteem through the analysis of data collected from an activity tracker. Appropriate stress levels are considered to differ from person to person. The proposed method records stress values per subject and activity context, to calculate the appropriate stress value from the mean and variance of the stress values. The method regards items of clothing that bring long-lasting suitable stress values as ones that increase the self-esteem of the users and improve their performance. If the characteristics of clothing that improve self-esteem can be objectively identified from sensor data, a change of clothing into identified one can help reduce feelings of hopelessness and depression caused by low self-esteem. This method uses a multidimensional emotional scale of clothing to represent the characteristics of clothing. The experiment is carried out by collecting data using four questionnaires on the multiple affective states generated by dressing, the activity context, Rosenberg's self-esteem scale, and the apathy scale. The stress values, heart rate, and activity intensity are also collected from an activity tracker. The analysis is mainly based on the sensor data from the activity tracker. It reduces the effort on the subjects during the experiment.Sensor data on stress values from activity trackers are used as an indicator related to self-esteem. The stress state appropriate to activity status and clothes is predicted by a random forest model constructed from real data. As a result, it is found that people wearing clothing that makes them feel 'fulfilled' according to their assessment of self-esteem are more likely to be in an appropriate state of stress.The result is expected to play an effective means in alleviating symptoms of low self-esteem and depression.

Keywords: activity tracker, Machine Learning, clothing behavior, mental health, stress

DOI: 10.54941/ahfe1004085

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