## 6 Discussion  
This study aims to identify the lifelog variable with the most decisive influence on BMI, closely related to health, by utilizing changeable gait and sleep lifelogs. The variables related to increases or decreases in BMI were also analyzed and specified. Although lifelog data have recently been collected more efficiently and constantly by wearable devices, there are still limitations in the accuracy and quality of sleep data. Data collection can be unstable due to various external factors, such as battery and Wi-Fi communication. In addition, the accuracy of the data can be low because it is difficult for users to wear the devices continuously. To improve the quality of sleep data, the study attempted to complement the limitations in accuracy by minimizing bias and anomalies. Various preprocessing and derivative variables such as total daily sleep time, variability in sleep time compared to the previous day, and daily sleep efficiency were generated. Further complementary research on sleep and accurate data collection and preprocessing of lifelogs is needed. To determine the influence of each variable more accurately on BMI, we divided the data into two groups of women and three groups of men, and we compared the data using the representative machine learning regression models Ridge Regression, XGBoost, and CatBoost. We also used the highly effective SHAP method for explainable artificial intelligence to visualize the relative importance of variables to BMI changes. The predicted results were interpreted by combining the machine learning model and SHAP. In the case of a deep learning model, also known as a black box model, the performance of the prediction can be high, but the interpretation can be difficult. Therefore, a SHAP-based interpretable machine learning model was used in this study. The advantages over the black box model include the following:  
1. Improved confidence in the machine learning model, providing a clear explanation of the results of the inference path. 2. Deriving insights by interpreting the results: extracting associations and patterns. 3. Improving overall problem solving and eliminating bias errors: debugging the way predictions are performed can increase predictive power, and the cause of bias can be analyzed and improved. The integrated lifelog data analysis of walking, sleep, and weight with machine learning revealed the key variables and how walking and sleep affect body weight. For middle-aged individuals, lifelogs can inform specifically and individually tailored health analyses beyond simple predictions, and they can influence weight regulation through interpretable techniques and visualization. In this study, the most common influential variables were calories burned, number of steps per day, distance walked per day, and sleep quality. These findings are consistent with  
Figure 7: Local interpretation of arbitrary data in men’s group 1 using SHAP. those of previous studies, indicating that the number of steps [43], walking speed [44; 45], and sleep quality [46; 47] affect BMI. The analysis highlights daily calorie consumption as an essential variable for predicting BMI. In the case of women's group 1 (150-160 cm), calories burned per day, the number of steps per day, distance walked per day, and amount of time walking at night had the most significant effect on BMI. In women's group 2 (160-170 cm), daily calorie consumption by walking, total sleep time, total sleep time variability, and step variability had the highest impact on BMI. For men's group 1 (165-170 cm), average morning walking speed, calorie consumption per day, and total sleep variability had the most substantial effects on BMI. In men's group 2 (170-175 cm), calories burned per day, distance walked per day, and the number of steps per day had the greatest impact on BMI. Finally, in the case of men's group 3 (175-180 cm), gait variability, calorie consumption per day, and average walking speed had the most significant effects on BMI.