模糊准确率补偿可穿戴光体积描记（PPG）信号肤色分类中的标签主观性
Fuzzy Accuracy Compensates for Label Subjectivity in Classification of Skin Tone Using Wearable Photoplethysmography Signals
- National Physical Laboratory（国家物理实验室）
- Mittelhessen University of Applied Sciences（中黑森应用科学大学）
- Institute of Metrology of Bosnia and Herzegovina（波斯尼亚和黑塞哥维那计量研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对PPG信号肤色分类准确率低的问题，采用模糊准确率结合三种机器学习方法，发现PPG信号可在1类误差内准确预测肤色，为肤色与PPG信号的关联提供了有力证据。
AI中文摘要：
我们研究使用光体积描记（PPG）信号对肤色进行分类的问题，采用有序六类Fitzpatrick肤色标签。该任务的典型准确率仅为40%-55%，表现较差。然而，这些标签是通过将皮肤与色卡对比主观确定的，因此存在大量小规模误差。通过采用“模糊准确率”——即若预测的肤色类别与标注类别差值不超过1，则视为预测正确——可获得高得多的准确率，这为PPG信号可准确预测肤色提供了更有力的证据。研究使用三种机器学习方法：对原始PPG信号的深度学习或基于树的方法、对通过对称投影吸引子重构（SPAR）方法生成的信号图像的深度学习，以及对从信号中提取特征的机器学习。第一种方法还采用了模糊版本的交叉熵损失函数，取得了最佳结果。基于原始信号的树模型准确率最高达55%，模糊准确率最高达96%；对SPAR图像的深度学习模型准确率为44%，模糊准确率为85%；对PPG特征的机器学习结果与SPAR方法相近，准确率为42%，模糊准确率为87%。我们证明使用PPG信号进行肤色分类可达到高模糊准确率，这表明我们的建模方法能在观察者所选类别至多相差1类的范围内准确预测肤色类别，由此得出PPG信号受肤色影响且这种影响可被辨识的结论。
英文摘要：
We consider the problem of classification of skin tone using photoplethysmography (PPG) signals with labels of the ordinal six-class Fitzpatrick skin tones. A typical accuracy for this task is a poor 40-55 %. However, the labels are subjectively determined by comparing the skin with a colour chart, and hence contain widespread small-scale inaccuracies. By working with a "fuzzy accuracy", which deems a prediction of skin tone class to be correct if its difference from the labelled class is not greater than one, much higher accuracy is obtained which provides more convincing evidence that skin tone can be accurately predicted from PPG signals. Three machine learning approaches were used, namely deep learning or tree-based approaches on raw PPG signals, deep learning on image representations of the signals generated by the Symmetric Projection Attractor Reconstruction (SPAR) method, and machine learning on features extracted from the signals. The first method also employed a fuzzy version of the cross entropy loss function, which gave the best results. Tree-based models on raw signals give accuracies up to 55 % and higher fuzzy accuracies up to 96 %, while deep learning models on the SPAR images obtained lower results of 44 % accuracy and 85 % fuzzy accuracy. The machine learning on PPG features gave similar results to the SPAR method with accuracy of 42 % and fuzzy accuracy of 87 %. We have shown that classification of skin tone using PPG signals is possible with high fuzzy accuracy which implies that our modelling approach enables accurate prediction of skin tone class within at most one class of the observer's choice of class, from which we conclude that PPG signals are affected by skin tone in a discernible way.
