Tsurumoto Masanobu, Shimazaki Takunori, Hyry Jaakko, Kawakubo Yoshifumi, Yokoyama Takeshi, Anzai Daisuke
Department of Clinical Engineering, Faculty of Health and Welfare, Tokushima Bunri University, Kagawa 760-8542, Japan.
Department of Clinical Engineering, Faculty of Health Care, Jikei University of Health Care Sciences, Osaka 532-0003, Japan.
Sensors (Basel). 2025 Jul 16;25(14):4441. doi: 10.3390/s25144441.
Peripheral circulatory failure refers to a condition in which the blood flow through superficial capillaries is markedly reduced or completely occluded. In clinical practice, nurses strictly adhere to regular repositioning protocols to prevent peripheral circulatory failure, during which the skin condition is evaluated visually. In this study, skin colour changes resulting from pressure application were continuously captured using a camera, and supervised machine learning was employed to classify the data into two categories: before and after pressure. The evaluation of practical colour space components revealed that the h component of the JCh colour space demonstrated the highest discriminative performance (Area Under the Curve (AUC) = 0.88), followed by the a* component of the CIELAB colour space (AUC = 0.84) and the H component of the HSV colour space (AUC = 0.83). These findings demonstrate that it is feasible to quantitatively evaluate skin colour changes associated with pressure, suggesting that this approach can serve as a valuable indicator for dimensionality reduction in feature extraction for machine learning and is potentially an effective method for preventing pressure-induced skin injuries.
外周循环衰竭是指流经浅表毛细血管的血流量显著减少或完全阻塞的一种状况。在临床实践中，护士严格遵循定期翻身方案以预防外周循环衰竭，在此期间通过肉眼评估皮肤状况。在本研究中，使用相机连续捕捉施加压力后引起的皮肤颜色变化，并采用监督式机器学习将数据分为两类：施压前和施压后。对实际颜色空间分量的评估表明，JCh颜色空间的h分量表现出最高的判别性能（曲线下面积（AUC）=0.88），其次是CIELAB颜色空间的a*分量（AUC = 0.84）和HSV颜色空间的H分量（AUC = 0.83）。这些发现表明，定量评估与压力相关的皮肤颜色变化是可行的，这表明该方法可作为机器学习特征提取中降维的一个有价值指标，并且可能是预防压力性皮肤损伤的一种有效方法。
