论文标题
对移动设备的用户压力，心率和心率变异性的实时监测
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
论文作者
论文摘要
压力被认为是21世纪的流行。但是，移动应用程序无法直接评估其内容和服务对用户压力的影响。我们介绍了Beam AI SDK来解决此问题。使用我们的SDK，应用程序可以实时通过自拍相机监视用户压力。我们的技术通过分析用户脸部皮肤区域的微妙颜色变化来提取用户的脉搏波。然后使用用户的脉搏波来确定压力（根据Baevsky压力指数），心率和心率变异性。我们在UBFC数据集，MMSE-HR数据集和Beam AI的内部数据上评估了我们的技术。我们的技术在每个基准测试中的心率估计分别达到99.2％，97.8％和98.5％的精度，误差率几乎比竞争方法低两倍。我们进一步证明了在确定压力和心率变异性时的平均Pearson相关性为0.801，从而产生了商业上有用的读数以在应用程序中得出内容决策。我们的SDK可在www.beamhealth.ai上使用。
Stress is considered to be the epidemic of the 21st-century. Yet, mobile apps cannot directly evaluate the impact of their content and services on user stress. We introduce the Beam AI SDK to address this issue. Using our SDK, apps can monitor user stress through the selfie camera in real-time. Our technology extracts the user's pulse wave by analyzing subtle color variations across the skin regions of the user's face. The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability. We evaluate our technology on the UBFC dataset, the MMSE-HR dataset, and Beam AI's internal data. Our technology achieves 99.2%, 97.8% and 98.5% accuracy for heart rate estimation on each benchmark respectively, a nearly twice lower error rate than competing methods. We further demonstrate an average Pearson correlation of 0.801 in determining stress and heart rate variability, thus producing commercially useful readings to derive content decisions in apps. Our SDK is available for use at www.beamhealth.ai.
