# Prediction of Lower Leg Swelling in Driving Posture
## 4 Conclusions  
This study focused on lower leg swelling as a typical physical load in prolonged sitting postures such as driving. The study aimed to obtain prediction equations for lower leg swelling from thigh pressure distribution, participants' physical characteristics, and sitting time. Lower leg impedance and thigh pressure distribution were measured in participants sitting on an automobile seat for a long time. Multiple regression analysis was performed to construct prediction equations for lower leg swelling in the males, the females, and all the participants. The adjusted \(R^{2}\) for the prediction equations for the males, the females, and all the participants were 0.561, 0.512, and 0.551, respectively. Furthermore, the standardized partial regression coefficients for each variable were similar for the three groups. In all the groups, the most positive effect on the progression of lower leg swelling was the thigh pressure distribution. This may be because the greater the thigh pressure, the more the poplilest vein is compressed and the blood flow to the lower limb decreases. Conversely, height had a negative effect. This might be because thigh length is correlated with height. Since the same automobile seat was assigned to all the participants in this experiment, those with shorter thighs tended to have more compression on the poplilest vein. These suggest that the obtained prediction equations may be applicable regardless of sex. The limitations of this study were as follows: First, age was not included as a factor for the lower leg swelling. In general, lymphatic function declines with age, which may influence the progression of leg swelling. However, the participants in this study had a small age variation, ranging from 20 to 23 years old, and this effect may not have been fully included in the study. Second, body fat percentage was used as an explanatory variable in the prediction equation, but the adjusted partial regression coefficient for body fat percentage was small. Therefore, it is possible that the variation in body fat percentage was not adequately reflected in the results. Third, the female participants' menstrual schedules were not considered. The lower leg swelling may have been overestimated due to changes in estrogen secretion caused by the menstrual schedule. Finally, driving behavior (e.g., operating the axel and brake, sitting up, etc.) may differ depending on driving conditions (e.g., city driving, highway driving, suburban driving, etc.) and type of vehicle (manual or automatic transmission). However, the experiments in this study were conducted in a static sitting posture and did not account for changes in these behaviors. These points require further investigation in future studies. Future studies aiming to improve prediction accuracy should consider modifying the variables (e.g., image analysis of thigh pressure distribution to calculate features, physiological factors affecting lower leg swelling), conducting measurements of seated postures in actual driving conditions (e.g., movements and vibrations during driving) and different chairs (e.g., on office chairs), adjusting the measurement schedule based on the menstrual cycle for females, and including more participants and stratifying them based on age and physique. A further potential application is the development of highly accurate fatigue estimation systems using digital technology. In recent years, studies on fatigue estimation using Digital Twin technology have gained attention. Several reports have explored fatigue estimation through digital models of the human body, such as during sitting work in offices or standing work in factories [40; 41]. However, these studies primarily focus on fatigue caused by musculoskeletal loads. By incorporating lower leg swelling due to contact load, as identified in this study, it becomes possible to achieve more accurate fatigue estimation.