Dynamic Balance Ability Estimation Method Using Plantar Pressure Measurement for Developing Shoes to Assess Daily Living Walking Ability

In Japan's super-aged society, the increasing proportion of people requiring nursing care is partly attributed to falls caused by a decline in walking ability. Early detection of walking ability deterioration can potentially reduce the risk of falls. Although individuals tend to perform better in laboratory settings than in daily life, this finding highlights the importance of monitoring walking in daily living, which has not been sufficiently explored. This study aimed to develop shoes to evaluate the walking ability in daily life by focusing on balance, which is a key indicator of walking deterioration. Balance ability is divided into static balance, which maintains stability, and dynamic balance, which adjusts posture under unstable conditions; dynamic balance is crucial for walking. Common dynamic balance evaluation methods, such as the one-leg stand test, timed up-and-go test, and Berg balance scale (BBS), often suffer from ceiling effects, which make it challenging to accurately assess differences in balance ability among individuals with moderate or high abilities. To address this, we employed the index of postural stability (IPS) and the modified IPS, which correlate with the BBS and are unaffected by ceiling effects. These indices require specialized equipment such as stabilometers; thus, simpler and more efficient evaluation methods are desirable. We propose estimating the dynamic balance ability by analyzing walking motion using shoes equipped with force sensors to measure the plantar pressure. We developed and validated a compact, lightweight plantar pressure measurement device suitable for shoe integration. We used the device to measure the plantar pressure while walking and to estimate the dynamic balance ability.Finally, a 20-m walking experiment was conducted using the device. Machine learning methods were employed to estimate the dynamic balance ability by processing the plantar pressure data obtained during walking. The model using 16 plantar pressure data points showed the highest estimation accuracy, suggesting the potential of this system for evaluating walking ability in daily life.