Buyung Rinaldi Anwar, Bustamam Alhadi, Ramazhan Muhammad Remzy Syah
Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok 16424, Indonesia.
Data Science Center (DSC), Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok 16424, Indonesia.
Sensors (Basel). 2024 Nov 26;24(23):7537. doi: 10.3390/s24237537.
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening.
非接触式心脏监测对于推进远程医疗、健康追踪和大规模筛查至关重要。远程光电容积脉搏波描记法（rPPG）是一种非接触技术，通过分析血液循环周期中皮肤反射或吸收的光强度变化来获取有关心脏脉搏的信息。然而，该技术对环境光线和不同的皮肤色素沉着敏感，导致结果不可靠。本研究提出了一种通过结合面部视频和身体属性（包括年龄、性别、体重、身高和体重指数（BMI））来进行非接触式心率估计的多模态方法。为此，我们从60个人那里收集了本地数据集，其中包含1分钟的面部视频以及年龄、性别、体重和身高之类的身体属性，并且我们从体重和身高中导出了BMI变量。我们在多模态数据集上比较了两种机器学习模型——支持向量回归（SVR）和随机森林回归——的性能。实验结果表明，采用多模态方法可提高模型性能，随机森林模型取得了更好的结果，平均绝对误差（MAE）为3.057次/分钟，均方根误差（RMSE）为10.532次/分钟，平均绝对百分比误差（MAPE）为4.2%，优于最先进的rPPG方法。这些发现凸显了可解释的、非接触式实时心率测量系统对远程医疗和大规模筛查应用做出有效贡献的潜力。
