Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea.
Department of Information Convergence, Kwangwoon University, Seoul 01897, Korea.
Sensors (Basel). 2022 Jul 7;22(14):5101. doi: 10.3390/s22145101.
Heart and respiration rates represent important vital signs for the assessment of a person's health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.
心率和呼吸率是评估人体健康状况的重要生命体征。为了准确估计这些生命体征，我们提出了一种多任务暹罗网络模型（MTS），它结合了暹罗网络和多任务学习架构的优势。MTS 模型通过包括鼻子和嘴以及额头区域的脸颊图像进行训练，同时在暹罗网络之间共享相同的参数，以提取有关心脏和呼吸信息的特征。所提出的模型使用少量参数构建，能够获得与从单任务学习模型获得的相当的高生命信号预测准确性，并且优于传统的多任务学习模型。结果，我们可以使用 MTS 模型同时预测心脏和呼吸信号，同时将参数数量减少了 16 倍，心率和呼吸率的平均误差分别为 2.84 和 4.21。由于其重量轻，因此在移动电话或小型便携式设备等边缘设备中实施生命信号监测模型将具有优势。
